code/Paper.ipynb

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    "# Synthesizing Benchmarks for Predictive Modeling\n",
    "\n",
    "[Chris Cummins](http://chriscummins.cc/),\n",
    "[Pavlos Petoumenos](http://homepages.inf.ed.ac.uk/ppetoume/),\n",
    "[Zheng Wang](http://www.lancaster.ac.uk/staff/wangz3/),\n",
    "[Hugh Leather](http://homepages.inf.ed.ac.uk/hleather/).\n",
    "\n",
    "**Abstract**\n",
    "> Predictive modeling using machine learning is an effective method for building\n",
    "> compiler heuristics, but there is a shortage of benchmarks. Typical machine\n",
    "> learning experiments outside of the compilation field train over thousands or\n",
    "> millions of examples. In machine learning for compilers, however, there are\n",
    "> typically only a few dozen common benchmarks available. This limits the\n",
    "> quality of learned models, as they have very sparse training data for what are\n",
    "> often high-dimensional feature spaces. What is needed is a way to generate an\n",
    "> unbounded number of training programs that finely cover the feature space. At\n",
    "> the same time the generated programs must be similar to the types of programs\n",
    "> that human developers actually write, otherwise the learning will target the\n",
    "> wrong parts of the feature space.\n",
    ">\n",
    "> We mine open source repositories for program fragments and apply deep learning\n",
    "> techniques to automatically construct models for how humans write programs. We\n",
    "> then sample the models to generate an unbounded number of runnable training\n",
    "> programs, covering the feature space ever more finely. The quality of the\n",
    "> programs is such that even human developers struggle to distinguish our\n",
    "> generated programs from hand-written code.\n",
    ">\n",
    "> We use our generator for OpenCL programs, CLgen, to automatically synthesize\n",
    "> thousands of programs and show that learning over these improves the\n",
    "> performance of a state of the art predictive model by $1.27\\times$. In\n",
    "> addition, the fine covering of the feature space automatically exposes\n",
    "> weaknesses in the feature design which are invisible with the sparse training\n",
    "> examples from existing benchmark suites. Correcting these weaknesses further\n",
    "> increases performance by $4.30\\times$.\n",
    "\n",
    "**Keywords**  Synthetic program generation, OpenCL, Benchmarking, Deep Learning, GPUs"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "This is a live research artifact. See [here](https://github.com/ChrisCummins/paper-synthesizing-benchmarks) for instructions to install this artifact on your own hardware. Please note that performance numbers obtained on hardware different to what we used may differ from those in the paper."
   ]
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  {
   "cell_type": "code",
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   "source": [
    "# preamble\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "%run lib/preamble.py\n",
    "\n",
    "from IPython.core.display import HTML\n",
    "HTML(\"\"\"<style>\n",
    "/* hide index column in Pandas Data Frames */\n",
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   "source": [
    "# 1. Introduction\n",
    "\n",
    "Predictive modeling is a well researched method for building optimization\n",
    "heuristics that often exceed human experts and reduces development time. A set\n",
    "of training programs are identified which are expected to be representative of\n",
    "the application domain. The programs are compiled and executed with different\n",
    "parameter values for the target heuristic, to determine which are the best\n",
    "values for each training program. Each program is also summarized by a vector of\n",
    "features which describe the information that is expected to be important in\n",
    "predicting the best heuristic parameter values. These training examples of\n",
    "program features and desired heuristic values are used to create a machine\n",
    "learning model which, when given the features from a new, unseen program, can\n",
    "predict good heuristic values for it.\n",
    "\n",
    "It is common for the feature vectors to contain dozens of elements. This means\n",
    "that a large volume of training data is needed to have an adequate sampling over\n",
    "the feature space. Without it, the machine learned models can only capture the\n",
    "coarse characteristics of the heuristic, and new programs which do not lie near\n",
    "to training points may be wrongly predicted. The accuracy of the machine learned\n",
    "heuristic is thus limited by the sparsity of the training points.\n",
    "\n",
    "There have been efforts to solve this problem using templates. The essence of\n",
    "the approach is to construct a probabilistic grammar with embedded semantic\n",
    "actions that defines a language of possible programs. New programs may be\n",
    "created by sampling the grammar and, through setting probabilities on the\n",
    "grammar productions, the sampling is biased towards producing programs from one\n",
    "part of the space or another. This technique is potentially completely general,\n",
    "since a grammar can theoretically be constructed to match any desired program\n",
    "domain. However, despite being theoretically possible, it is not easy to\n",
    "construct grammars which are both suitably general and also produce programs\n",
    "that are in any way similar to human written programs. It has been shown to be\n",
    "successful over a highly restricted space of stencil benchmarks with little\n",
    "control flow or program variability. But, it is not clear how much effort it\n",
    "will take, or even if it is possible for human experts to define grammars\n",
    "capable of producing human like programs in more complex domains.\n",
    "\n",
    "By contrast, our approach does not require an expert to define what human\n",
    "programs look like. Instead, we automatically infer the structure and likelihood\n",
    "of programs over a huge corpus of open source projects. From this corpus, we\n",
    "learn a probability distribution over sets of characters seen in human written\n",
    "code. Later, we sample from this distribution to generate new random programs\n",
    "which, because the distribution models human written code, are indistinguishable\n",
    "from human code. We can then populate our training data with an unbounded number\n",
    "of human like programs, covering the space with any desired granularity, far\n",
    "more finely than either existing benchmark suites, or even the corpus of open\n",
    "source projects. Our approach is enabled by two recent developments:\n",
    "\n",
    "The first is the breakthrough effectiveness of deep learning for modeling\n",
    "complex structure in natural languages. As we show, deep learning is capable not\n",
    "just of learning the macro syntactical and semantic structure of programs, but\n",
    "also the nuances of how humans typically write code. It is truly remarkable when\n",
    "one considers that it is given no prior knowledge of the syntax or semantics of\n",
    "the language.\n",
    "\n",
    "The second is the increasing popularity of public and open platforms for hosting\n",
    "software projects and source code. This popularity furnishes us with the\n",
    "thousands of programming examples that are necessary to feed into the deep\n",
    "learning. These open source examples are not, sadly, as useful for directly\n",
    "learning the compiler heuristics since they are not presented in a uniform,\n",
    "runnable manner, nor do they typically have extractable test data. Preparing\n",
    "each of the thousands of open source projects to be directly applicable for\n",
    "learning compiler heuristics would be an insurmountable task. In addition to our\n",
    "program generator, CLgen, we also provide an accompanying host driver which\n",
    "generates datasets for, then executes and profiles synthesized programs.\n",
    "\n",
    "We make the following contributions:\n",
    "\n",
    "-   We are the first to apply deep learning over source codes to\n",
    "    synthesize compilable, executable benchmarks.\n",
    "\n",
    "-   A novel tool CLgen for general-purpose benchmark synthesis using\n",
    "    deep learning. CLgen automatically generates thousands of human like\n",
    "    programs for use in predictive modeling.\n",
    "\n",
    "-   We use CLgen to automatically improve the performance of a state of\n",
    "    the art predictive model by 1.27x, and expose limitations in\n",
    "    the feature design of the model which, after correcting, further\n",
    "    increases performance by 2.66x."
   ]
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   "source": [
    "# 2. Motivation\n",
    "\n",
    "In this section we make the argument for synthetic benchmarks. We identified\n",
    "frequently used benchmark suites in a survey of research papers in the field\n",
    "of GPGPU performance tuning from top tier conferences between 2013–2016: CGO,\n",
    "HiPC, PACT, and PPoPP:"
   ]
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Conference</th>\n",
       "      <th>Year</th>\n",
       "      <th>Paper</th>\n",
       "      <th>Benchmark Suite</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HiPC</td>\n",
       "      <td>2015</td>\n",
       "      <td>paper-01</td>\n",
       "      <td>NVIDIA SDK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HiPC</td>\n",
       "      <td>2015</td>\n",
       "      <td>paper-01</td>\n",
       "      <td>NVIDIA SDK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HiPC</td>\n",
       "      <td>2015</td>\n",
       "      <td>paper-01</td>\n",
       "      <td>NVIDIA SDK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HiPC</td>\n",
       "      <td>2015</td>\n",
       "      <td>paper-01</td>\n",
       "      <td>NVIDIA SDK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HiPC</td>\n",
       "      <td>2015</td>\n",
       "      <td>paper-01</td>\n",
       "      <td>NVIDIA SDK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>HiPC</td>\n",
       "      <td>2015</td>\n",
       "      <td>paper-01</td>\n",
       "      <td>NVIDIA SDK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>HiPC</td>\n",
       "      <td>2015</td>\n",
       "      <td>paper-01</td>\n",
       "      <td>NVIDIA SDK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>PPoPP</td>\n",
       "      <td>2013</td>\n",
       "      <td>paper-26</td>\n",
       "      <td>Rodinia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>PPoPP</td>\n",
       "      <td>2013</td>\n",
       "      <td>paper-26</td>\n",
       "      <td>Rodinia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>PPoPP</td>\n",
       "      <td>2013</td>\n",
       "      <td>paper-26</td>\n",
       "      <td>Rodinia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>PPoPP</td>\n",
       "      <td>2013</td>\n",
       "      <td>paper-26</td>\n",
       "      <td>Rodinia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>PPoPP</td>\n",
       "      <td>2013</td>\n",
       "      <td>paper-26</td>\n",
       "      <td>Rodinia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>PPoPP</td>\n",
       "      <td>2013</td>\n",
       "      <td>paper-26</td>\n",
       "      <td>GPGPUsim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>PPoPP</td>\n",
       "      <td>2013</td>\n",
       "      <td>paper-26</td>\n",
       "      <td>GPGPUsim</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>448 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Conference  Year     Paper Benchmark Suite\n",
       "0         HiPC  2015  paper-01      NVIDIA SDK\n",
       "1         HiPC  2015  paper-01      NVIDIA SDK\n",
       "2         HiPC  2015  paper-01      NVIDIA SDK\n",
       "3         HiPC  2015  paper-01      NVIDIA SDK\n",
       "4         HiPC  2015  paper-01      NVIDIA SDK\n",
       "5         HiPC  2015  paper-01      NVIDIA SDK\n",
       "6         HiPC  2015  paper-01      NVIDIA SDK\n",
       "..         ...   ...       ...             ...\n",
       "441      PPoPP  2013  paper-26         Rodinia\n",
       "442      PPoPP  2013  paper-26         Rodinia\n",
       "443      PPoPP  2013  paper-26         Rodinia\n",
       "444      PPoPP  2013  paper-26         Rodinia\n",
       "445      PPoPP  2013  paper-26         Rodinia\n",
       "446      PPoPP  2013  paper-26        GPGPUsim\n",
       "447      PPoPP  2013  paper-26        GPGPUsim\n",
       "\n",
       "[448 rows x 4 columns]"
      ]
     },
     "execution_count": 2,
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   ],
   "source": [
    "data = pd.read_csv(\"../data/lit-review.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis of results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average number of benchmarks per paper: 17.23\n",
      "Median number of benchmarks per paper:  14.5\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Benchmark Suite</th>\n",
       "      <th>#. benchmarks per paper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rodinia</td>\n",
       "      <td>6.674107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NVIDIA SDK</td>\n",
       "      <td>4.933036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AMD SDK</td>\n",
       "      <td>4.352679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Parboil</td>\n",
       "      <td>3.772321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NAS</td>\n",
       "      <td>2.205357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Polybench</td>\n",
       "      <td>1.334821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>SHOC</td>\n",
       "      <td>0.696429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ad-hoc</td>\n",
       "      <td>0.348214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ISPASS</td>\n",
       "      <td>0.348214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Ploybench</td>\n",
       "      <td>0.348214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lonestar</td>\n",
       "      <td>0.290179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>SPEC-Viewperf</td>\n",
       "      <td>0.290179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MARS</td>\n",
       "      <td>0.232143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GPGPUsim</td>\n",
       "      <td>0.174107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Benchmark Suite  #. benchmarks per paper\n",
       "11         Rodinia                 6.674107\n",
       "7       NVIDIA SDK                 4.933036\n",
       "0          AMD SDK                 4.352679\n",
       "8          Parboil                 3.772321\n",
       "6              NAS                 2.205357\n",
       "10       Polybench                 1.334821\n",
       "12            SHOC                 0.696429\n",
       "1           Ad-hoc                 0.348214\n",
       "3           ISPASS                 0.348214\n",
       "9        Ploybench                 0.348214\n",
       "4         Lonestar                 0.290179\n",
       "13   SPEC-Viewperf                 0.290179\n",
       "5             MARS                 0.232143\n",
       "2         GPGPUsim                 0.174107"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "benchmark_counts = [x[1] for x in data.groupby(\"Paper\").size().iteritems()]\n",
    "suites = pd.DataFrame(\n",
    "    [(x[0], x[1] / labmath.mean(benchmark_counts))\n",
    "     for x in data.groupby([\"Benchmark Suite\"]).size().iteritems()],\n",
    "    columns=[\"Benchmark Suite\", \"#. benchmarks per paper\"])\n",
    "suites.sort_values(\"#. benchmarks per paper\", inplace=True, ascending=False)\n",
    "\n",
    "print(\"Average number of benchmarks per paper:\", round(labmath.mean(benchmark_counts), 2))\n",
    "print(\"Median number of benchmarks per paper: \", round(labmath.median(benchmark_counts), 2))\n",
    "print()\n",
    "suites"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 2:**  The average number of benchmarks used in GPGPU research papers, organized by origin. In this work we use the seven most popular benchmark suites."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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Hh4fa71K/fv0ExRXz/V6a2LOYBK0Ezs/PZ/aILKabN28iOzsbbm5ugjLpvn37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\n8oK3t7fa6kWWxCgaVhvs2bMHR48eLbf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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd868ca20f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(x=\"Benchmark Suite\", y=\"#. benchmarks per paper\", data=suites)\n",
    "# threshold for benchmark suites used in paper:\n",
    "plt.axhline(y=.5, color=\"k\", lw=1)\n",
    "plt.setp(ax.get_xticklabels(), rotation=90)\n",
    "plt.xlabel(\"\"); plt.ylabel(\"#. benchmarks used\")\n",
    "viz.finalise(figsize=(4, 2.25), tight=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We selected the 7 most frequently used benchmark suites (used in 92% of\n",
    "results), and evaluated the performance of the state of the art *Grewe et al.*\n",
    "predictive model across each. Benchmark data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>benchmark</th>\n",
       "      <th>dataset</th>\n",
       "      <th>comp</th>\n",
       "      <th>rational</th>\n",
       "      <th>mem</th>\n",
       "      <th>localmem</th>\n",
       "      <th>coalesced</th>\n",
       "      <th>atomic</th>\n",
       "      <th>transfer</th>\n",
       "      <th>wgsize</th>\n",
       "      <th>...</th>\n",
       "      <th>runtime</th>\n",
       "      <th>speedup</th>\n",
       "      <th>penalty</th>\n",
       "      <th>runtime_cpu</th>\n",
       "      <th>runtime_gpu</th>\n",
       "      <th>Group</th>\n",
       "      <th>F1_norm</th>\n",
       "      <th>F2_norm</th>\n",
       "      <th>F3_norm</th>\n",
       "      <th>F4_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>amd-app-sdk-3.0-BinomialOption-binomial_options</td>\n",
       "      <td>default</td>\n",
       "      <td>98</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2048</td>\n",
       "      <td>255</td>\n",
       "      <td>...</td>\n",
       "      <td>1.443983</td>\n",
       "      <td>2.28</td>\n",
       "      <td>0.44</td>\n",
       "      <td>3.291073</td>\n",
       "      <td>1.443983</td>\n",
       "      <td>amd-app-sdk-3.0</td>\n",
       "      <td>1.925432e-08</td>\n",
       "      <td>0.002275</td>\n",
       "      <td>0.505709</td>\n",
       "      <td>0.016180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>amd-app-sdk-3.0-BitonicSort-bitonicSort</td>\n",
       "      <td>default</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>131072</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>0.121940</td>\n",
       "      <td>2.29</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.121940</td>\n",
       "      <td>0.279521</td>\n",
       "      <td>amd-app-sdk-3.0</td>\n",
       "      <td>7.599039e-06</td>\n",
       "      <td>0.014771</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>amd-app-sdk-3.0-BlackScholes-blackScholes</td>\n",
       "      <td>default</td>\n",
       "      <td>220</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3145728</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>2.740855</td>\n",
       "      <td>2.22</td>\n",
       "      <td>0.45</td>\n",
       "      <td>6.076052</td>\n",
       "      <td>2.740855</td>\n",
       "      <td>amd-app-sdk-3.0</td>\n",
       "      <td>1.472101e-05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.157296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>amd-app-sdk-3.0-FastWalshTransform-fastWalshTr...</td>\n",
       "      <td>default</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4096</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>0.130003</td>\n",
       "      <td>5.15</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.130003</td>\n",
       "      <td>0.668892</td>\n",
       "      <td>amd-app-sdk-3.0</td>\n",
       "      <td>3.562049e-07</td>\n",
       "      <td>0.014771</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>amd-app-sdk-3.0-FloydWarshall-floydWarshallPass</td>\n",
       "      <td>default</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>524288</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>0.283447</td>\n",
       "      <td>2.72</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.283447</td>\n",
       "      <td>0.769755</td>\n",
       "      <td>amd-app-sdk-3.0</td>\n",
       "      <td>3.419567e-05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>amd-app-sdk-3.0-MatrixMultiplication-mmmKernel</td>\n",
       "      <td>default</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49152</td>\n",
       "      <td>64</td>\n",
       "      <td>...</td>\n",
       "      <td>0.277644</td>\n",
       "      <td>4.03</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.277644</td>\n",
       "      <td>1.120132</td>\n",
       "      <td>amd-app-sdk-3.0</td>\n",
       "      <td>2.931058e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>amd-app-sdk-3.0-MatrixTranspose-matrixTranspose</td>\n",
       "      <td>default</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>32768</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>0.156639</td>\n",
       "      <td>6.44</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.156639</td>\n",
       "      <td>1.009519</td>\n",
       "      <td>amd-app-sdk-3.0</td>\n",
       "      <td>5.895806e-07</td>\n",
       "      <td>0.001846</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.005622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673</th>\n",
       "      <td>shoc-1.1.5-Sort-top_scan</td>\n",
       "      <td>default</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4198400</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>1.863657</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.885541</td>\n",
       "      <td>1.863657</td>\n",
       "      <td>shoc-1.1.5</td>\n",
       "      <td>2.190660e-04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.001755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>shoc-1.1.5-Spmv-spmv_csr_scalar_kernel</td>\n",
       "      <td>default</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2000628</td>\n",
       "      <td>128</td>\n",
       "      <td>...</td>\n",
       "      <td>2.038006</td>\n",
       "      <td>3.43</td>\n",
       "      <td>0.29</td>\n",
       "      <td>2.038006</td>\n",
       "      <td>6.981001</td>\n",
       "      <td>shoc-1.1.5</td>\n",
       "      <td>2.609742e-04</td>\n",
       "      <td>0.014771</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>shoc-1.1.5-Spmv-spmv_csr_vector_kernel</td>\n",
       "      <td>default</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2000628</td>\n",
       "      <td>128</td>\n",
       "      <td>...</td>\n",
       "      <td>2.036940</td>\n",
       "      <td>3.41</td>\n",
       "      <td>0.29</td>\n",
       "      <td>2.036940</td>\n",
       "      <td>6.951909</td>\n",
       "      <td>shoc-1.1.5</td>\n",
       "      <td>9.077364e-05</td>\n",
       "      <td>0.008050</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.002339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>shoc-1.1.5-Spmv-spmv_ellpackr_kernel</td>\n",
       "      <td>default</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2000628</td>\n",
       "      <td>128</td>\n",
       "      <td>...</td>\n",
       "      <td>2.035003</td>\n",
       "      <td>3.44</td>\n",
       "      <td>0.29</td>\n",
       "      <td>2.035003</td>\n",
       "      <td>6.998864</td>\n",
       "      <td>shoc-1.1.5</td>\n",
       "      <td>2.609742e-04</td>\n",
       "      <td>0.014771</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>shoc-1.1.5-Stencil2D-CopyRect</td>\n",
       "      <td>default</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>71647488</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>16.666277</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.96</td>\n",
       "      <td>17.328426</td>\n",
       "      <td>16.666277</td>\n",
       "      <td>shoc-1.1.5</td>\n",
       "      <td>7.476911e-03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>shoc-1.1.5-Stencil2D-StencilKernel</td>\n",
       "      <td>default</td>\n",
       "      <td>144</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>71647488</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>16.666380</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.93</td>\n",
       "      <td>17.866730</td>\n",
       "      <td>16.666380</td>\n",
       "      <td>shoc-1.1.5</td>\n",
       "      <td>4.673069e-04</td>\n",
       "      <td>0.002777</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.019313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>shoc-1.1.5-Triad-Triad</td>\n",
       "      <td>default</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>117440512</td>\n",
       "      <td>128</td>\n",
       "      <td>...</td>\n",
       "      <td>876.457180</td>\n",
       "      <td>1.11</td>\n",
       "      <td>0.90</td>\n",
       "      <td>876.457180</td>\n",
       "      <td>968.494606</td>\n",
       "      <td>shoc-1.1.5</td>\n",
       "      <td>2.451146e-02</td>\n",
       "      <td>0.014771</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001431</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>680 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             benchmark  dataset  comp  \\\n",
       "0      amd-app-sdk-3.0-BinomialOption-binomial_options  default    98   \n",
       "1              amd-app-sdk-3.0-BitonicSort-bitonicSort  default    12   \n",
       "2            amd-app-sdk-3.0-BlackScholes-blackScholes  default   220   \n",
       "3    amd-app-sdk-3.0-FastWalshTransform-fastWalshTr...  default     8   \n",
       "4      amd-app-sdk-3.0-FloydWarshall-floydWarshallPass  default    11   \n",
       "5       amd-app-sdk-3.0-MatrixMultiplication-mmmKernel  default   163   \n",
       "6      amd-app-sdk-3.0-MatrixTranspose-matrixTranspose  default    42   \n",
       "..                                                 ...      ...   ...   \n",
       "673                           shoc-1.1.5-Sort-top_scan  default     9   \n",
       "674             shoc-1.1.5-Spmv-spmv_csr_scalar_kernel  default     2   \n",
       "675             shoc-1.1.5-Spmv-spmv_csr_vector_kernel  default    12   \n",
       "676               shoc-1.1.5-Spmv-spmv_ellpackr_kernel  default     3   \n",
       "677                      shoc-1.1.5-Stencil2D-CopyRect  default     8   \n",
       "678                 shoc-1.1.5-Stencil2D-StencilKernel  default   144   \n",
       "679                             shoc-1.1.5-Triad-Triad  default     2   \n",
       "\n",
       "     rational  mem  localmem  coalesced  atomic   transfer  wgsize    ...     \\\n",
       "0           8   13        11          2       0       2048     255    ...      \n",
       "1           2    6         0          6       0     131072     256    ...      \n",
       "2           4    3         0          0       0    3145728     256    ...      \n",
       "3           0    4         0          4       0       4096     256    ...      \n",
       "4           1    5         0          0       0     524288     256    ...      \n",
       "5           1   12         0          0       0      49152      64    ...      \n",
       "6           0   16         8          2       0      32768     256    ...      \n",
       "..        ...  ...       ...        ...     ...        ...     ...    ...      \n",
       "673         6   11         5          0       0    4198400     256    ...      \n",
       "674         2    6         0          6       0    2000628     128    ...      \n",
       "675         5   11         5          6       0    2000628     128    ...      \n",
       "676         2    5         0          5       0    2000628     128    ...      \n",
       "677         2    2         0          0       0   71647488       0    ...      \n",
       "678         7   16        12          3       0   71647488     256    ...      \n",
       "679         0    3         0          3       0  117440512     128    ...      \n",
       "\n",
       "        runtime  speedup  penalty  runtime_cpu runtime_gpu            Group  \\\n",
       "0      1.443983     2.28     0.44     3.291073    1.443983  amd-app-sdk-3.0   \n",
       "1      0.121940     2.29     0.44     0.121940    0.279521  amd-app-sdk-3.0   \n",
       "2      2.740855     2.22     0.45     6.076052    2.740855  amd-app-sdk-3.0   \n",
       "3      0.130003     5.15     0.19     0.130003    0.668892  amd-app-sdk-3.0   \n",
       "4      0.283447     2.72     0.37     0.283447    0.769755  amd-app-sdk-3.0   \n",
       "5      0.277644     4.03     0.25     0.277644    1.120132  amd-app-sdk-3.0   \n",
       "6      0.156639     6.44     0.16     0.156639    1.009519  amd-app-sdk-3.0   \n",
       "..          ...      ...      ...          ...         ...              ...   \n",
       "673    1.863657     1.01     0.99     1.885541    1.863657       shoc-1.1.5   \n",
       "674    2.038006     3.43     0.29     2.038006    6.981001       shoc-1.1.5   \n",
       "675    2.036940     3.41     0.29     2.036940    6.951909       shoc-1.1.5   \n",
       "676    2.035003     3.44     0.29     2.035003    6.998864       shoc-1.1.5   \n",
       "677   16.666277     1.04     0.96    17.328426   16.666277       shoc-1.1.5   \n",
       "678   16.666380     1.07     0.93    17.866730   16.666380       shoc-1.1.5   \n",
       "679  876.457180     1.11     0.90   876.457180  968.494606       shoc-1.1.5   \n",
       "\n",
       "          F1_norm   F2_norm   F3_norm   F4_norm  \n",
       "0    1.925432e-08  0.002275  0.505709  0.016180  \n",
       "1    7.599039e-06  0.014771  0.000000  0.004292  \n",
       "2    1.472101e-05  0.000000  0.000000  0.157296  \n",
       "3    3.562049e-07  0.014771  0.000000  0.004292  \n",
       "4    3.419567e-05  0.000000  0.000000  0.004721  \n",
       "5    2.931058e-07  0.000000  0.000000  0.029185  \n",
       "6    5.895806e-07  0.001846  0.300000  0.005622  \n",
       "..            ...       ...       ...       ...  \n",
       "673  2.190660e-04  0.000000  0.272727  0.001755  \n",
       "674  2.609742e-04  0.014771  0.000000  0.000715  \n",
       "675  9.077364e-05  0.008050  0.136364  0.002339  \n",
       "676  2.609742e-04  0.014771  0.000000  0.001288  \n",
       "677  7.476911e-03  0.000000  0.000000  0.008584  \n",
       "678  4.673069e-04  0.002777  0.450000  0.019313  \n",
       "679  2.451146e-02  0.014771  0.000000  0.001431  \n",
       "\n",
       "[680 rows x 25 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amd_benchmarks = cgo13.LabelledData.from_csv('../data/amd-benchmarks.csv', group_by=\"suite\")\n",
    "amd_benchmarks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>benchmark</th>\n",
       "      <th>dataset</th>\n",
       "      <th>comp</th>\n",
       "      <th>rational</th>\n",
       "      <th>mem</th>\n",
       "      <th>localmem</th>\n",
       "      <th>coalesced</th>\n",
       "      <th>atomic</th>\n",
       "      <th>transfer</th>\n",
       "      <th>wgsize</th>\n",
       "      <th>...</th>\n",
       "      <th>runtime</th>\n",
       "      <th>speedup</th>\n",
       "      <th>penalty</th>\n",
       "      <th>runtime_cpu</th>\n",
       "      <th>runtime_gpu</th>\n",
       "      <th>Group</th>\n",
       "      <th>F1_norm</th>\n",
       "      <th>F2_norm</th>\n",
       "      <th>F3_norm</th>\n",
       "      <th>F4_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>amd-app-sdk-3.0-BinomialOption-binomial_options</td>\n",
       "      <td>default</td>\n",
       "      <td>98</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2048</td>\n",
       "      <td>255</td>\n",
       "      <td>...</td>\n",
       "      <td>1.443983</td>\n",
       "      <td>2.28</td>\n",
       "      <td>0.44</td>\n",
       "      <td>3.291073</td>\n",
       "      <td>1.443983</td>\n",
       "      <td>amd-app-sdk-3.0</td>\n",
       "      <td>1.925432e-08</td>\n",
       "      <td>0.002275</td>\n",
       "      <td>0.505709</td>\n",
       "      <td>0.016180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>amd-app-sdk-3.0-BitonicSort-bitonicSort</td>\n",
       "      <td>default</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>131072</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>0.121940</td>\n",
       "      <td>2.29</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.121940</td>\n",
       "      <td>0.279521</td>\n",
       "      <td>amd-app-sdk-3.0</td>\n",
       "      <td>7.599039e-06</td>\n",
       "      <td>0.014771</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>amd-app-sdk-3.0-BlackScholes-blackScholes</td>\n",
       "      <td>default</td>\n",
       "      <td>220</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3145728</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>2.740855</td>\n",
       "      <td>2.22</td>\n",
       "      <td>0.45</td>\n",
       "      <td>6.076052</td>\n",
       "      <td>2.740855</td>\n",
       "      <td>amd-app-sdk-3.0</td>\n",
       "      <td>1.472101e-05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.157296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>amd-app-sdk-3.0-FastWalshTransform-fastWalshTr...</td>\n",
       "      <td>default</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4096</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>0.130003</td>\n",
       "      <td>5.15</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.130003</td>\n",
       "      <td>0.668892</td>\n",
       "      <td>amd-app-sdk-3.0</td>\n",
       "      <td>3.562049e-07</td>\n",
       "      <td>0.014771</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>amd-app-sdk-3.0-FloydWarshall-floydWarshallPass</td>\n",
       "      <td>default</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>524288</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>0.283447</td>\n",
       "      <td>2.72</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.283447</td>\n",
       "      <td>0.769755</td>\n",
       "      <td>amd-app-sdk-3.0</td>\n",
       "      <td>3.419567e-05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>amd-app-sdk-3.0-MatrixMultiplication-mmmKernel</td>\n",
       "      <td>default</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49152</td>\n",
       "      <td>64</td>\n",
       "      <td>...</td>\n",
       "      <td>0.277644</td>\n",
       "      <td>4.03</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.277644</td>\n",
       "      <td>1.120132</td>\n",
       "      <td>amd-app-sdk-3.0</td>\n",
       "      <td>2.931058e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>amd-app-sdk-3.0-MatrixTranspose-matrixTranspose</td>\n",
       "      <td>default</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>32768</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>0.156639</td>\n",
       "      <td>6.44</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.156639</td>\n",
       "      <td>1.009519</td>\n",
       "      <td>amd-app-sdk-3.0</td>\n",
       "      <td>5.895806e-07</td>\n",
       "      <td>0.001846</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.005622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673</th>\n",
       "      <td>shoc-1.1.5-Sort-top_scan</td>\n",
       "      <td>default</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4198400</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>1.863657</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.885541</td>\n",
       "      <td>1.863657</td>\n",
       "      <td>shoc-1.1.5</td>\n",
       "      <td>2.190660e-04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.001755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>shoc-1.1.5-Spmv-spmv_csr_scalar_kernel</td>\n",
       "      <td>default</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2000628</td>\n",
       "      <td>128</td>\n",
       "      <td>...</td>\n",
       "      <td>2.038006</td>\n",
       "      <td>3.43</td>\n",
       "      <td>0.29</td>\n",
       "      <td>2.038006</td>\n",
       "      <td>6.981001</td>\n",
       "      <td>shoc-1.1.5</td>\n",
       "      <td>2.609742e-04</td>\n",
       "      <td>0.014771</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>shoc-1.1.5-Spmv-spmv_csr_vector_kernel</td>\n",
       "      <td>default</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2000628</td>\n",
       "      <td>128</td>\n",
       "      <td>...</td>\n",
       "      <td>2.036940</td>\n",
       "      <td>3.41</td>\n",
       "      <td>0.29</td>\n",
       "      <td>2.036940</td>\n",
       "      <td>6.951909</td>\n",
       "      <td>shoc-1.1.5</td>\n",
       "      <td>9.077364e-05</td>\n",
       "      <td>0.008050</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.002339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>shoc-1.1.5-Spmv-spmv_ellpackr_kernel</td>\n",
       "      <td>default</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2000628</td>\n",
       "      <td>128</td>\n",
       "      <td>...</td>\n",
       "      <td>2.035003</td>\n",
       "      <td>3.44</td>\n",
       "      <td>0.29</td>\n",
       "      <td>2.035003</td>\n",
       "      <td>6.998864</td>\n",
       "      <td>shoc-1.1.5</td>\n",
       "      <td>2.609742e-04</td>\n",
       "      <td>0.014771</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>shoc-1.1.5-Stencil2D-CopyRect</td>\n",
       "      <td>default</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>71647488</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>16.666277</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.96</td>\n",
       "      <td>17.328426</td>\n",
       "      <td>16.666277</td>\n",
       "      <td>shoc-1.1.5</td>\n",
       "      <td>7.476911e-03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>shoc-1.1.5-Stencil2D-StencilKernel</td>\n",
       "      <td>default</td>\n",
       "      <td>144</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>71647488</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>16.666380</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.93</td>\n",
       "      <td>17.866730</td>\n",
       "      <td>16.666380</td>\n",
       "      <td>shoc-1.1.5</td>\n",
       "      <td>4.673069e-04</td>\n",
       "      <td>0.002777</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.019313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>shoc-1.1.5-Triad-Triad</td>\n",
       "      <td>default</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>117440512</td>\n",
       "      <td>128</td>\n",
       "      <td>...</td>\n",
       "      <td>876.457180</td>\n",
       "      <td>1.11</td>\n",
       "      <td>0.90</td>\n",
       "      <td>876.457180</td>\n",
       "      <td>968.494606</td>\n",
       "      <td>shoc-1.1.5</td>\n",
       "      <td>2.451146e-02</td>\n",
       "      <td>0.014771</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001431</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>680 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             benchmark  dataset  comp  \\\n",
       "0      amd-app-sdk-3.0-BinomialOption-binomial_options  default    98   \n",
       "1              amd-app-sdk-3.0-BitonicSort-bitonicSort  default    12   \n",
       "2            amd-app-sdk-3.0-BlackScholes-blackScholes  default   220   \n",
       "3    amd-app-sdk-3.0-FastWalshTransform-fastWalshTr...  default     8   \n",
       "4      amd-app-sdk-3.0-FloydWarshall-floydWarshallPass  default    11   \n",
       "5       amd-app-sdk-3.0-MatrixMultiplication-mmmKernel  default   163   \n",
       "6      amd-app-sdk-3.0-MatrixTranspose-matrixTranspose  default    42   \n",
       "..                                                 ...      ...   ...   \n",
       "673                           shoc-1.1.5-Sort-top_scan  default     9   \n",
       "674             shoc-1.1.5-Spmv-spmv_csr_scalar_kernel  default     2   \n",
       "675             shoc-1.1.5-Spmv-spmv_csr_vector_kernel  default    12   \n",
       "676               shoc-1.1.5-Spmv-spmv_ellpackr_kernel  default     3   \n",
       "677                      shoc-1.1.5-Stencil2D-CopyRect  default     8   \n",
       "678                 shoc-1.1.5-Stencil2D-StencilKernel  default   144   \n",
       "679                             shoc-1.1.5-Triad-Triad  default     2   \n",
       "\n",
       "     rational  mem  localmem  coalesced  atomic   transfer  wgsize    ...     \\\n",
       "0           8   13        11          2       0       2048     255    ...      \n",
       "1           2    6         0          6       0     131072     256    ...      \n",
       "2           4    3         0          0       0    3145728     256    ...      \n",
       "3           0    4         0          4       0       4096     256    ...      \n",
       "4           1    5         0          0       0     524288     256    ...      \n",
       "5           1   12         0          0       0      49152      64    ...      \n",
       "6           0   16         8          2       0      32768     256    ...      \n",
       "..        ...  ...       ...        ...     ...        ...     ...    ...      \n",
       "673         6   11         5          0       0    4198400     256    ...      \n",
       "674         2    6         0          6       0    2000628     128    ...      \n",
       "675         5   11         5          6       0    2000628     128    ...      \n",
       "676         2    5         0          5       0    2000628     128    ...      \n",
       "677         2    2         0          0       0   71647488       0    ...      \n",
       "678         7   16        12          3       0   71647488     256    ...      \n",
       "679         0    3         0          3       0  117440512     128    ...      \n",
       "\n",
       "        runtime  speedup  penalty  runtime_cpu runtime_gpu            Group  \\\n",
       "0      1.443983     2.28     0.44     3.291073    1.443983  amd-app-sdk-3.0   \n",
       "1      0.121940     2.29     0.44     0.121940    0.279521  amd-app-sdk-3.0   \n",
       "2      2.740855     2.22     0.45     6.076052    2.740855  amd-app-sdk-3.0   \n",
       "3      0.130003     5.15     0.19     0.130003    0.668892  amd-app-sdk-3.0   \n",
       "4      0.283447     2.72     0.37     0.283447    0.769755  amd-app-sdk-3.0   \n",
       "5      0.277644     4.03     0.25     0.277644    1.120132  amd-app-sdk-3.0   \n",
       "6      0.156639     6.44     0.16     0.156639    1.009519  amd-app-sdk-3.0   \n",
       "..          ...      ...      ...          ...         ...              ...   \n",
       "673    1.863657     1.01     0.99     1.885541    1.863657       shoc-1.1.5   \n",
       "674    2.038006     3.43     0.29     2.038006    6.981001       shoc-1.1.5   \n",
       "675    2.036940     3.41     0.29     2.036940    6.951909       shoc-1.1.5   \n",
       "676    2.035003     3.44     0.29     2.035003    6.998864       shoc-1.1.5   \n",
       "677   16.666277     1.04     0.96    17.328426   16.666277       shoc-1.1.5   \n",
       "678   16.666380     1.07     0.93    17.866730   16.666380       shoc-1.1.5   \n",
       "679  876.457180     1.11     0.90   876.457180  968.494606       shoc-1.1.5   \n",
       "\n",
       "          F1_norm   F2_norm   F3_norm   F4_norm  \n",
       "0    1.925432e-08  0.002275  0.505709  0.016180  \n",
       "1    7.599039e-06  0.014771  0.000000  0.004292  \n",
       "2    1.472101e-05  0.000000  0.000000  0.157296  \n",
       "3    3.562049e-07  0.014771  0.000000  0.004292  \n",
       "4    3.419567e-05  0.000000  0.000000  0.004721  \n",
       "5    2.931058e-07  0.000000  0.000000  0.029185  \n",
       "6    5.895806e-07  0.001846  0.300000  0.005622  \n",
       "..            ...       ...       ...       ...  \n",
       "673  2.190660e-04  0.000000  0.272727  0.001755  \n",
       "674  2.609742e-04  0.014771  0.000000  0.000715  \n",
       "675  9.077364e-05  0.008050  0.136364  0.002339  \n",
       "676  2.609742e-04  0.014771  0.000000  0.001288  \n",
       "677  7.476911e-03  0.000000  0.000000  0.008584  \n",
       "678  4.673069e-04  0.002777  0.450000  0.019313  \n",
       "679  2.451146e-02  0.014771  0.000000  0.001431  \n",
       "\n",
       "[680 rows x 25 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nvidia_benchmarks = cgo13.LabelledData.from_csv('../data/amd-benchmarks.csv', group_by=\"suite\")\n",
    "nvidia_benchmarks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *Grewe et al.* model predicts whether running a given OpenCL kernel on the GPU gives better\n",
    "performance than on the CPU. The performance of a model trained on one benchmark\n",
    "suite and used to predict the optimal mapping for another suites is generally\n",
    "very poor.\n",
    "\n",
    "**Table 1:** Performance relative to the optimal of the *Grewe et al.* predictive model across different benchmark suites on an AMD GPU. The columns show the suite used for training; the rows show the suite used for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7o/7akOzj1DjIHSuFwuLpNYAgNwdGNgvmdFwWqflqbK2taBjoSoinI4fvZBCf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oAAAg\nAElEQVSjmbc7g19oxM4Ltyqza2UPSJ/8H3v3HRXF9TZw/LtIL4J0UKOiCPbesPeOGnuMJYklGkss\niSW/iEnssfdesLdo7N0YS1RUFAuiIiii0qRJW8q+f6wuLmAUQ0ve53OO57gzd2bvXebuPnPbTJ5M\n3bp18fb2RldX3QDl5ubGxYuZF13LaxkDmZiYGKytrdHT0yM8PJyjR4++9xwHDhwgJSWFxMREDh48\niJubG6amplSoUIE9e/YA6vFI3t7e1KpV6z1ny31WZoYU0sncxaf3etaSbiHtff4vYvF+9JLYhGTq\nudigo1Bw4V7oO88/or0rQeFxHLyqHrtka25IaHR6MBYanYidhVFOFCVHKVNSGbfvIk+j4pjXpT4l\nLLXvZiLi1GVIzSL4TUlTkfo3A2f33PAnPjmFPjXVU4DDXyViY5oebFibGhL2KuFdh+cL+Tz+3vOw\nKMIiY6lRoWSmfbUqlOKmX3qAd9PvCcaG+riUtNdKV6eSEyoVWmnfZqCvy/Rvu+OxdC+v4hOxtjBF\nT7eQJjhLUqYQGRtPUZsiWR6fm0LDX6JCxfQVG2g1YJT63xej8PF7SMDT57T+YhQrtu79R++x7eAJ\n4hOTGNC1g/o9X0ZiY5leVlurIoSEv/xH75Fbgq7/ia6BIQ4VPvw7Py1FSXLiu5dveHThKClJiTg3\ncQcgIfolhoXTx/oZmVuSEF0wP4+3Jb7uln6VmMSLyFhi4hPpMn0t7aaspN2UlXy5cCsKFKw5/hft\np6wi4EXWN1s7znkTn6TULCYZFv0KG/P0rl4bc1NCo94/GzUnZbuL7caNGyxbtoxChQppmufNzc2J\nick8aDEvZTUOqG/fvowaNYqOHTtia2uLm9vf918qFAqcnJzo3bs3MTExNG/eXNMdN2fOHCZPnszm\nzZvR0dFh2rRp2NvbZ3rvvB6PFBj6ivqutpSwMeFxWHpl7FirOGkqFfeCsx4gZ6Cnw+iO5QmNTtQE\nPxmVtDXl80al6Tk3/S4mPDaJ0nbpP66l7c0Ij8k8xTM/palUTDp4mdvPXzKncz2tAcZvFLcwRUeh\n4KTfU7q8dcceEhvPjeBwqhWzzvLcMYlKVl/0ZWKr6ui9nupsaWzAnRfpX2QBEbFarTP5TT6PD7P3\n1DU+7+CGo42FphWoae1yOJewY/6mY5p0B/7wZu53vRnSoxljZqc/YmlQ9yYEh0by182HWZ5//Jft\niYyOY91edatCRPQrUlLTcCnpgH9QKFYWpthYmPIiIus6m5ucSxZn0Y9jMm1fuHEn8QmJTBran2L2\nH794YXTsK5Zu3s1PIweh/3r5BysLc275+WvS+D95irWlxUe/R25JehVD6INbFK/eiEJ62k+MSFEm\noQDNYpBvBN/8C2VCHEWKO5MVZfwrfI9vp1r3oejoqrtmDc0seP7kgSZNzIsgDAsXnM/jZWw8lmba\nvS4pqWkcuHIHAz1dnOyt6dOkBs2rOGc67uftx+hUpxJNKztTNIvW15j4RFYcucCPvVqj93pJESsz\nE24/fq5J8+hFBNZ5vPBstgMkCwsLQkNDcXBIb4oPCgrC1jZ/V/7Mav0iR0dHdu3alWX6Ll260KVL\nF61tnp6e7zx/8eLFWb9+fZb7fH19/zYfuWn1ifs0LG/H9jGN2XTWn6g4Jc0qOdCwvB07LgQQHqPu\nUlz4VW1CoxJ4+CIWU0NdutUrSTFrYwYuvUiCMusmzx+6VebA1SDuBKV3GRzzDmb5kLqMcS+PAgVN\nK9kzaFn+tx6+bcEfPpz3f07D0g5EJSg5mqF7p025T7AwNqBDxRIcuB3IN7v+pEmZosQpk/nt5iOU\nKan0f8cihyvO36GMjTlNndNnpzRxLsraS77MPumNnZkx+3wCGN20cpbH5wf5PODrHs2wMDPG0Vb9\ng9OhcVWK2akDxSXbTvIqPpFZaw/xaYuanFgzniVbT2BmYsjovm3wuR+E54ELmnM9C4ti0ebjjO7X\nBn29Qly9E0inptVxq+JMv0krs3z/4vaWjO7Xho5vraKdlqbi4NkbzP2uN584WNG5WXWCw6K45OOf\n5Tlyk0VhM5rVq5lpu+feIyiApnXTx9HcD3jC6UvXAHjy7AWxcfGs2KZuXXJ1KkGTOtUznWfRxp24\nlPqElg1qa7a1rF+b5Vt/4+cla3GwsWbXkdPvXHspPz31Po8qLY1PamTuXnsV9ozzK6ZQrGp9zGyL\ngY6CyCcPCbr+JyaWdpRu2D7Lc949sgVzhxJaA74dK9XF9/hOvHevxLiINYGXjlO581e5Vq7s+mX7\nMeISlVQvUwxbCzMiYuI4fPUugSEvGfdpU4z09XAtZodrMTut497MYivtYE2TSlk/kmTJwXM4O9rQ\nomr6wpzNq5Zl5dGLTNtxHPsihdlz4SbfdW2WewXMQrYDpF69ejF8+HC+/fZb0tLS8PLyYt68efTp\n0yc38ife46p/BD3m/MHI9uX4rJETRUz0CYqIY+7vd1h9Mn0dKJ/HkXSrW4KeDUqRmJyK18NwRq27\nwv1nWbf8NSpvR83SVjT3OK61/cztF8zdf4d+TUqjQMGc3+9w3vfdXXT54UFYNAoFnH/0nPOPnmfa\n/2aNngktq1PW1oL9twJZfl493bqCvSU/tatFlaKZW0z8w6M5eCeQdZ9pV9IyNub82KYmay6qZ211\nr1qazpVKZTo+v8jnAaP7teYTe/VK3yoVdGpanU5N1T/kWw5d5FV8IsGhkbQYOIvZY3sxdUQ3lMkp\nHD53k/Hzd2rGH70xadFuXsbEMbBrE/p2qM/DJyH0/2EVu457Zfn+M7/twZFzPpy/rr022/Dpm1jx\nY39+Hv4pDx6/oPvoxVqPNCkIMraK330YwJJN2jeeb153atEoU4D0IDCI347/wY6FU7W2ly31CdPH\nDGXplt3ExSfyWcdWdG+btz+AHyLI+08Mzcyxcc4c5BtZWFG0Sj3CHt7mydU/SEtNxdjShjIN2+PS\nvCv6xplnA0Y/f0zg5dM0/Xa21nZzxxLU7D0C32PbSUlKwKlBO0rWbZlr5cquNjXKsfcvH3afv0FU\nfCImBvqUK27H6E5NaFSx9N8em9WjRt548CyMfZdusWVcX63tzo42/Px5O1YcuUBcopJejarTNYvH\nmeQmheojRiBv2rSJ7du3ExwcjKOjI7169aJv3775Pt3936jMsN/yOwsFxsNl6im+Uasm5XNOCgaL\nwdMB+TzeZjF4OvrVvnx/wv8nlN7qx5ikBlzP55wUHIVKVWfiobwdzFuQzWivXsk68fjafM5JwWHY\n6sNa5rLdgpSYmEjfvn3p27dvpu2GhgV/nIEQQgghxPtkexbbuwY6N2pU8NauEEIIIYT4GNkOkLLq\nkVMqldK9JoQQQoj/jA/uYuvZsycKhQKlUkmvXtrPSAkJCaF69cwzF4QQQggh/o0+OEDq1asXKpWK\nu3fv0rNnT812hUKBlZUVdevWzZUMCiGEEELktQ8OkN6sGVSxYkXKli37ntRCCCGEEP9eHxQg7dmz\nR/OgVm9vb7y9vbNM93bLkhBCCCHEv9UHBUhHjx7VBEgHDx7MMo1CoZAASQghhBD/CR8UIK1evVrz\n/02bNuVaZoQQQgghCoJsT/MXQgghhPivy/ZK2q6uru9c8+jth7YKIYQQQvxbZTtAOnz4sNbrsLAw\n1qxZQ6tWrXIsU0IIIYQQ+SnbAZKTk1Om1y4uLvTp04fu3bvnWMaEEEIIIfJLjoxBSktLIyQkJCdO\nJYQQQgiR77LdgjRv3jyt1wkJCfz55580bdo0xzIlhBBCCJGfsh0ghYWFab02NjZm0KBBuLu751im\nhBBCCCHyU7YDpBkzZuRGPoQQQgghCoxsj0Hy8vLi6dOngLo1afz48UycOJGIiIgcz5wQQgghRH7I\ndoDk4eGhWQdp5syZJCQkkJaWxuTJk3M8c0IIIYQQ+SHbXWwhISEULVqUlJQUzp07x6lTpzAwMKBR\no0a5kT8hhBBCiDyX7QDJ0NCQiIgI7t+/T4kSJTAzMyM5OZnk5OTcyJ8QQgghRJ7LdoDk7u5O165d\nUSqVjBgxAoA7d+5QrFixHM+cEEIIIUR+yHaANH78eOrXr4+enh516tQBoFChQkyYMCHHMyeEEEII\nkR+yHSABNGjQQOt1pUqVciQzQgghhBAFQY48akQIIYQQ4r9EAiQhhBBCiAwkQBJCCCGEyCDbAdKJ\nEyey3L5kyZJ/nBkhhBBCiIIg2wHSjBkzuHTpkta2xYsXc+TIkRzLlBBCCCFEfsp2gLRs2TLGjx/P\nrVu3AFi0aBFHjx5l48aNOZ45IYQQQoj8kO1p/q6ursyZM4fhw4fTtGlTvLy88PT0xMrKKjfyJ4QQ\nQgiR5xQqlUr1vkQBAQGZtp08eZKNGzeyYMECTXBUqlSpnM+hEEIIIUQe+6AAydXVFYVCQVZJ32xX\nKBT4+vrmSib/y+6FxOR3FgoMV7vCAMTvmJHPOSkYjHtOBCA57Ek+56Tg0LP5hKOlq+V3NgqMNv7e\nAOhX+zKfc1JwKL3XkfL8QX5no8DQdXAG4FjZ6vmck4Kj9f3rH5Tug7rY7t27948yI4QQQgjxb5Kt\nQdqpqal06NABpVKZW/kRQgghhMh32QqQChUqREJCAsnJybmVHyGEEEKIfJftaf7Dhw9n8uTJBAYG\nolQqtf4JIYQQQvwXZHua/8SJ6oGjhw4dQqFQAMggbSGEEEL8p2Q7QDp16lRu5EMIIYQQosDIdoBU\ntGjR3MiHEEIIIUSBke0ACeD48eNcuXKFyMhIre1z587NkUwJIYQQQuSnbA/SXrhwIT///DOFChXi\n5MmTWFtbc+nSJYoUKZIb+RNCCCGEyHPZDpD27dvH+vXrmThxInp6ekycOJEVK1bw9OnT3MifEEII\nIUSey3aAFBMTg7OzeulyPT09lEollSpVwsvLK8czJ4QQQgiRH7I9BqlEiRLcv3+fsmXL4uzszNat\nWzEzM8Pc3Dw38ieEEEIIkeeyHSB9++23xMSoH7A6duxYxo0bR3x8PB4eHjmeOSGEEEKI/JDtAKlR\no0aa/1epUoUTJ07kaIaEEEIIIfLbR03zVyqVPHnyhPj4eK3tlStXzpFMCSGEEELkp2wHSIcPH8bD\nw4PExEQMDQ012xUKBVeuXMnRzAkhhBBC5IdsB0gzZszgl19+oU2bNrmRHyGEEEKIfJftaf4qlYqW\nLVvmRl6EEEIIIQqEbAdIAwYMYMWKFahUqtzIjxBCCCFEvst2F1vr1q354osvWLt2babHi5w6dSrH\nMiaEEEIIkV+yHSCNGjWKSpUq0a5dO61B2kIIIYQQ/xXZDpACAgLYtWsXhQoVyo38CCGEEELku2yP\nQXJzc+POnTu5kRchhBBCiAIh2y1INjY2DBo0iGbNmmFjY6O1b8yYMTmWMSGEEEKI/JLtACkpKYlm\nzZoBEBYWluMZEkIIIYTIbx+1UKQQQgghxH/ZRz2LLS4ujsDAwEzPYqtVq1aOZEoIIYQQIj9lO0A6\ncOAAkydPRkdHByMjI61958+fz7GMCSGEEELkl2wHSHPmzGH27NnyuJEC4vaNa/xv1NBM2xUKBbOW\nraNs+QoApKaksGvTes4cO0REeBhW1jY0b+dO1z79tZZsiAgPY+nsafjeuoGVjS39vx5BLbeGWue+\n+MdpVsyfxYptv2FsbJK7BfwI/qFRrDxzA99nEYS/SsBQrxBONhb0b1CRRi7FNenuBIfz+/UH3A4O\n50FIJGlpKq791D/T+ZJTUpl7zIvjtwPRL6RDt1ouDGxcRStNSHQcny7ey/L+rahc3DbXy/hPrdyw\nhSVrN1LGqSR7N67SbB8wfCzXbt7KlL5BnZosnzNd8zo0PByPWfPx9rmNnY0No4cOpEn9ulrHnPjj\nHL/MXcSRHRsxMTbOvcJ8hEJGhpQaPADzKhUxr1wBPfPC3Preg2d7D2qlK1ypPMW6dcK8SkXMXJxR\nFNLhWNmamc6n0NPFddJY7Nu3Ik2pJGjrbh4tW6uVxsDelobHfsOr/1Cib2T+jPOSsaE+4wa0pWZF\nJ2pVKEWRwsYM9FjH5oMXM6V1KWnPnHG9cavqjDI5hSPnffhu7nYiol5lSju2fxsGdWuKg7U5D56E\nMHvdIXYe036IuXuTakwd0RV7GwvOXfPjm2mevAiP1kqzd8FInoZGMmL6ppwt+D+wwnM7S9ZvwblU\nCfauW5JlmthXcbT7fDCR0THM/2kiLRu5afaFhkfgMWcx12/dxc7GmjGDB9DErbbW8cfPXmDqguUc\n2bKqwNUZUNebkoMGYF65AuaV1PXm9gQPnu07pJXOsUtH7Fo2xay8C3rm5iQ8DebFoeMErPVElZys\nSafQ08Vlwhjs26nrzdNte3i0IkO9sbOlwZE9XP1iGNFZfDflpmwHSImJiTRv3jw38iL+gY7de1PG\npZzWNodixTT/n/vLj/z15xlatnOntIsrfndvs3XtCsJDQxg2bqIm3YJpHkRGhNP/65H43r7JbI+J\nLN20C1t7BwCSlUo2rlhE30HDCmRwBPA86hXxymQ6ViuDjZkRicmpnLobyLdbT/E/dzc+rVEWgPP3\nn/K79wOc7SwpXsSMxxExWZ5vw/nbHL75iIGNKxOXlMzqszcpblmY1pVKadLMP36VpuU++VcERyFh\n4azdsgPjDC3AoA6s7W1tGP31V7z9NCEbayutdJN+mU1YxEvGDB2E9607jJs8lf1b1uJobweAUqlk\n7rLVjBryZYH8otcrUoTSwweREPycWF8/LOtkDnoAbJo0oGj3TsTee0D8kyBMSpXIMl2pwQNw7NQO\n/6Vr0DU1ofTwQcQ/DuLFoeOaNK4TRhNy4ky+B0cA1kXMmDSoI4+fR3DT7wmNa7pmmc7RxoLT6yYS\nFRPHD4t2Y2ZiyJh+bahQuihufX8hNTVNk3bayG6M7d+G1XvOcu1uIO5NquI5fTBpaSp2n/ACoKSj\nNZtnfs2Oo5e5fMufkZ+1ZNWUL3EfPl9znpb1KlCvahnKuU/MlJ/8EhIWztptezA2+vvFkRev20yS\nUolCoci0b+L0eYS9jGTskC/wvn2XsT/N4sDG5Tjaq78zlMpk5q1cz6iB/QpknYHX9WbYQHW9uXcf\ny9o1MqUpZGRIxRkeRHn7ELRtN8qIl1hUq0zpkUOwrFeLq/2/1qQtNbA/ju7t8F++Bl1TU5y+GUj8\nkyBeHE6vNy7jvyX01B95HhzBRwRIvXr1Ytu2bfTp0yc38iM+UvlKVajXuFmW+x7cu8vFP07Ra8Ag\nen0xCIDW7p9iVtic/bu20f7T7pRwKoMyKYnb3teYvngV5SpVoU2nT7l36yY3vC7RqmMXAPZu24SJ\nqRktO3TKs7JlV4OyxWhQtpjWtl51XOm9/ACbL97RBEjda7vyRcNK6OsWYuahS+8MkM7df0q/+hXo\nV78iAC+i4zjrF6QJkLwfh3D+/lP2juySi6XKOb8uWUnlCuVITU0lKiZzmU1NTWjXMutrCSApSckV\n75tsWDKX6pUr0qNzB7xv3eHilWt0c28HwPqtuyhsakrXDm1zrRz/RFJoGGfqtkAZEUnhiuWot3dz\nlumebNnFo5XrUSmTKecx/p0Bkm2ThgSs8SRwrbrFw9DRHtvmjTUBkkWNqtg0acC5lgXjGnkWFkXx\nFqMJi4ylWrkS/LX5xyzTTRjYASMDPWoNms2zsCgArt4J4MjysfRzr8/6vecAcLCxYGSflizdfoqx\nv24DYMO+c5xcM54Zo7trAqQW9SoQFPKSQVPWAeAX+JxjK75DX08XZXIKOjoKfh3bi6kr9xMZE5fb\nH8MH+3XZWqqUdyElNZXomNgs0zwMeMyO/UcY1r83S9Zv0dqXlKTkyo1bbFw4k+qVytPDvS3et325\ncPU63Tu0AWDd9j2YmZrQtX2rXC/Px0oKDeMPt5YoX0ZSuEI56u7J3MKXlpzC5Z5faAU0wbt/JyH4\nOWVGDMGybi1eXlJfDzZNGhK4dhOP16nrn5GDPTbNGmkCJE29af1pHpQusw9aKLJnz5706tWLXr16\ncenSJWbMmEHz5s012978y65mzZrRtm1bOnfuTIcOHdiyZcvfpl+yZMkHzaLr27dvnj0XbuLEiXh6\neubJe71PQnw8qampmbbf9bmBQqGgQTPtbtGGzVuhSkvj/OkTgPquX6VSYWJqqkljYmpGUmIiABFh\noezZ6snAkWNzsRS5Q6FQYGduQmyiUrPN0sQQfd33rwifmJyCmaG+5nVhIwMSk1MAUKlU/HrkCgMa\nVMTGrGDe9b3t6g0fTp49z/iRX/9tutTUVOITErLcl/T6Oin81nVS2NSUhNfXyZsWqgmjhuVcxnOY\nKiUFZUTke9Mlv4xEpUx+bzodI0NS3vrhTI6KodBbrQ3lfvyOR6s2kBQW/nEZzmEpKamERWb9Q/+2\nzs1qcPjcTU1wBHDmii8PHofQrWX6pBz3JtXQLVSIVbvOaB2/atcZitlaUrdKaUDdtRcdmz65Jyom\nHoVCgaGBHgDf9GqBjkLBsh0F57meV2/e5sS5vxj/zcC/TTd98UpaNnKjeqXymR7mnl5n0lvdzUxN\nSExMAtJbqCYMH5zzBchBqpQUlC//vt6oUlKybO0JPXEGFApMSqe3vBcyMiQ59q16Ex2tVW9cJ40j\nYNVGlPlUbz6oBeljgp8PtWDBAlxcXHj27Bnu7u7UqlWLsmXL5tr7/VctmvkzCfHx6OjoUL5yNQYM\nG6npcktWqoMCfQMDrWMMDNQXor/fPQBMzcywdyzGrk3r+XzQMHxv3STA/wFDyqtbTjYsX0TNevUp\nX7lqXhXrH0lQppCUksKrxGT+uPeEiw+e0rqSU7bPU7GYNXuu3qdGSXvikpI5eusRveuoP9u91x4Q\nHZ+kaV0qyNLS0pixYCndOrbF2anUO9M9CQqmVsuOJCenYGVZhG4d2/L1gL7ovg4mC5uZUryoA6s8\ntzJqyJd437qDn78/P5QfDsC8Zatp5FaH6lUK/meSU6J9blO8d1deXrmGrokJDh3b8HijuiWlWM8u\n6FmYE7CmYNxIfSgHGwtsLc24dicw0z6vOwG0rl9J87qKyyfEJSThF/hCO93tABQKqOpSgks3/fG6\nHcCs0T3p0bo2l338mfBVB/yDQol5lYB1ETN+GNyRfpNWkZamyviW+SItLY3pi1bSrUNrnJ1KvjPd\nsT/O43PXjwOeK3j67EWm/YXNTCnuaM/KzTv5dmA/vG/fxe9hAJVHuQAwd+V6GtetSY3KFXKrKPnO\nwMYagOTI9GA72uc2xXt2JfLKNQqZmGDfvg1PNr2uNz1e15u1+VdvPihA6tIl95qF30Tajo6OlCpV\nisDAQM6dO8fevXvR0dHBxcUFDw8PTN+6WwXo2LEjU6dOpUoV9WDZHTt2cPnyZebNmwfAX3/9xfLl\ny4mNjaVZs2aMHz8egPDwcKZOncqzZ88046lGjRoFqFu0OnfuzMWLFwkPD6dr164MHaoeAB0SEsK0\nadMICAhAR0eH5s2bM3LkSAAePnzIgAEDeP78OS4uLsybNw9d3Y9aQSHbdHX1cGvSnBp13ShsbkFQ\nYAD7tm9m4vBBzF6+jlJlylL0kxKoVCp8b93UjCUCuHPTG1APzH5j2HeTmO0xgXOnjqNQKHDv3huX\nCpXwvXWTKxf+ZInnzjwpV06Yd8yLPVf9ANBRKGhevgTj29fJ9nm+blqV4ZtO0mPZ7yhQUL2EHb3r\nlic2UcnSU9eZ0KHuB7VE5bcdew/wPCSMdYsGvDPNJ8UcqVOjKs5OpUhITOT4mXOs3LiVx0HB/PrT\nD5p0U74fzZgff+HIqT9QKBT07fEpVSqWx/vWHU6fv8j+zWvf+R7/RQ8XrqDmuiXUP7QTVCpeel3n\n8cZt6Jqa4jzmG+56zPyglqiCxN7aHCDTAGqAF2FRWBY2QVe3ECkpqThYmxPyMnN37fNw9Y+ho40F\nAH/dfMiSbSfZOG0wCgVERMfRc9xSAH7+5lMu+fhz/OLt3CpStm3//TDPQ8NY9+Xn70yTlKRkzvJ1\n9OveGQdbmywDJIAp40YwxmMmR07/iUKhoF+3TlSp4Ir3bV9On7/MAc/luVWMAqHkoP6kxL4i7OwF\nzbaHi1dSY/US3A7sAJWKSK/rPPbcjq6pKWW+HYbvTzO1BnXntWz/iq9atYq6detSuXJlzTYfHx+u\nXLnCwIF/3wT5d/z8/AgICCA0NJTffvuNHTt2YGpqyuTJk5k7dy4eHh5a6fv164enpydz584FYNu2\nbUyePFmz39/fn507d6JUKunTpw+HDh2iffv2jB8/nqFDh1KzZk1SU1MZMmQIx44do3Xr1gDExsay\nfft2IiMjadmyJV27dsXW1pbvvvuOhg0bsmjRIgAiI9ObGf38/Ni0aRN6enp89tlnHD9+nHbt2n30\nZ5EdrhUr41ox/W9Ry60h9Ro3Y9QXvdm0cimTf11Izbr1sbFzYP2yhegbGFDGpRx+d26xZc1yCunq\nokxK0hxfuXpN1u46yJMAfyytbbCysUWlUrF28Tw69/wcGzt7juzbzcHdOwD14PA2nfKnf/h9Pq9X\nnpYVShIWG8/x24GkpqlITsncBfk+toVN2Da0I/6hUegV0qHU6y/7OUeuUNLanJYVSuL9OIT5x7wI\ni02gablPGNO6FrqFsv2ow1wTHRPD0nWeDP3ic8wLF35nup/Gaz8uqEOr5vw0ewF7Dh6hX8+uVCqv\nHsxbu3pVTuzZwsOAx9haW2FnY41KpWLWouUM6NUdBztbtu89wJbdewHo2/1TenTukHsFzGdJIWFc\n6NgbU+fSqJKTiXsUCEDZSWOJ8w8k5OhJLGpUxXXiaAzsbAg5fga/6fNQZdElXlAYGai7lZOy+IFK\nfB3sGRnoEZuSiqGhPsosAsDEJPU2Q0M9zbbv5m5nwaZj2Fmb4/voGQmJSqqULc5n7epSs5cHZiaG\nLJ7Yl0Y1XXjwOISRMzZlapnKC1ExsSxdv5Wh/XpjUdjsnelWb91Famoqg/p0/yncjzoAACAASURB\nVNvz1alWmZM71/Ew8Am2VpaaOjNzySq+6NkFB1sbtv9+mC179gPQt1snergXzDF82VXq6y+xqluL\nu1NmkBqXPrYsKSSMi50z1xvniWOIexRIyLFTWNSoisv40RjYWhN64gx+M+fnWb3J9je4p6cnpUuX\n1trm5OTExo0bPyoDo0ePpnPnzkyZMoXp06cTHBxM27ZtNS1GvXv35uLFzFNP3d3d8fLyIiIigqtX\nrwJQvXp1zf5OnTqho6ODoaEh7u7uXLx4kYSEBC5fvsy0adPo3LkzXbt2JSgoiICAAM1xHTt2BKBI\nkSIUL16cp0+fEh8fz/Xr1/nyyy816YoUKaL5f4sWLdDX10ehUFC5cmWePHnyUZ9FTnEoWow6DRpz\ny/saKpUKPX19Js9eQOHC5syePIFBPdxZOOMneg0YhKmZGYYZZjMZGBriXK4CVjbq2RUnD+0n8mUE\nn/bpxw2vy2xYvpj+Q0fQf+gI1i9bwO0b1/KjmO9Vwtqc2k4OtK9SmoV9mpOgTGbklo8b21BIR4ey\n9paa4CgwPJrdXn58374OMQlJjNx8kmblSvBrzyZc8n/G2j99crIo/9jCVeuxKFyY3p9mf3B9/17d\nUKlU/HX1utZ2I0NDKpVzwe510/lvh44SHvGSrz7vyUWva8xbtpqxQwcxduggfl26Ei/vmzlSlgIr\nLY1Xfg80X/ImpUpQvE83fH+ejW5hM2qsXUTI8dPc+OY7rOvXxWnoV/mb3/dISFJ3zRvo6WXaZ6iv\n9zqNOgBKTFSir59FutdjixITtYOn4NBIrt8NJOH1mMC533/Gyt1/8PBJKEsm9cXR1oJPv13EHf9g\nfls4KstZYblt0RpPLMzN+KzLuwP74OchbNjxG6MG9cPI8O9nuMHrOuNaNr3OHD5B+MsovvqsGxev\nejN3xXrGfP0FY77+gtnL1uJVAGY8/lP27VrhPGooT3ft4+mO3zInyFBvjEuVoHjvbtybqq431Vct\nJOT4aW6O/B6r+nVx+vrLzOfIJdluQVIqlRhkGMtiaGhI4usBmtn1ZgzSG9evX/+gymBgYECXLl3Y\nvn07jx49eu+sOoVCoenO27lzJ3pZVHoAff30wbg6Ojpag54zDrx7Oy9vH5OSkvLe/Oc2axs7UlKS\nSUxIwMjYmOIlS7Fo43aCAgN4FRtD8ZJO6Ovrs2bJPCpWzTxV8434+Di2rFnOl8NHY2BgyLnTJ6jf\ntDm16zcCwK1xM86eOPq35ygompcvybQDf/E4IoYSVu9uRfkQvx6+QoeqpXGxt+TgDX8sjA0Z0FA9\nJmNAg4qs/fMWQ5oWjLFaT54Gs+fAYSaMHEZouHqwo0qlQqlUkpKSwrMXIZgYG2P+jrtkezv1Q6nf\nNXsHIC4+nsWrN/D98CEYGhhw9NQftGraiCYN6gHQqklDDh0/Ta1qVd55jv8a1x+/49neg8Teu49j\n5/Ykv4wiYJX6RjJg9Qachn6F/5JV7zlL/nnTtfamq+1t9jYWvIyJI+V1i+zz8GgaZbFUgIO1+obi\n7UHeGXVvVQuXkvZ0HrkQhULBpy1q0ubrOdy49wTfRzv5qksj6lR24tJN/5wo1gd5/PQZuw8eY8KI\nwYSGRwAZ60woJibGLFm/GTsba2pUrsCzF6EAhEW8BCAyKppnL0JxsLPJ8jctLj6eRWs38f03A9V1\n5vQ5WjepT1M39TCAVo3rc/DkH9SqWinTsf8WVm51qDjrJ0LP/Mldj+nvPwBw/WEcz/YdIvbeAxw6\ntSc5MorANW/qzUacvv4S/6WrczPbGtluQSpbtiwHD2ovpnb48GHKlCnzURnIGHS4ublx5MgR4l43\nw+3YsYMGDRpkeexnn33Gzp07uXz5Mu7u7lr7Dhw4QEpKComJiRw8eBA3NzeMjY2pU6cOK1eu1KQL\nDQ0lJCTkb/NobGxMrVq1WLdunWbby5cvs1XOvPb82VP09PUxyrCeRvGSpShXqQqmZmb4XL+KKi2N\nqjXfPS5nx/rV2DkWpVELdRdkZHgYllY2mv2W1jZE/EseWpz4OnB99dZMto/xp18Qt56G8U1zdYtl\neGw81mbprXA2ZsaExsS/6/A8FxIWjkoFMxYuo3X3vrTu3pc2Pfrhc/cegU+e0qZHP1ZufPcM0qDg\nZwBYWmT+oXxj2bpNFHO01ywPEBYege1bayfZWFsREl4wZnDlBZumDbGoWon7c9XjawxsrbVmsCWG\nhGFgV7DXzHoeFkVYZCw1KpTMtK9WhVLc9EtvKb/p9wRjQ31cStprpatTyQmVCq20bzPQ12X6t93x\nWLqXV/GJWFuYoqdbSBOcJSlTiIyNp6hNkSyPzy2h4RGogBmLV9Gq91e06v0VrT8biI/vfQKCgmn9\n2UBWeG7jeWg4T4Kf0+azQZp030+dg0Kh4Of5y2j92UDi4rOeDbpswzaKOdrTvnlj9XtGRGBjlV5n\nbK0tNcHZv5F55QpUXTKHaJ873Bw1Ad7RwPA2myYNsahSiQfzX9cbG+16kxSat/Um2y1I33//PV98\n8QVHjx6lRIkSPH78GC8vL9auzf6gzKyi6kaNGvHw4UN69uypNUg7K3Z2dpQrVw4nJyetVhyFQoGT\nkxO9e/cmJiaG5s2ba8YE/frrr8yYMUPTlWZiYsJPP/2EnZ1dpvy8/XrWrFlMnTqVDh06oKenR/Pm\nzRk+fHi2y5zTYqKiKGxhobUt4OF9vC6eo2bd+u88Likpka1rV2BpbUPD5lmvuxEc9JjDe3cz461o\n3dzSkqdPAjWvgx4HUsTSKouj88/LuEQsTbSbu1NS0zjo7Y/B61W1P1ZyahrzjnoxqHEVirx+D0tT\nI4JexpCWpkJHR8Gj0CisTTMvwphfnJ1KsXB65jq0aNV64hMSmThqGMUcHYiLj0dfTy9T6+rKjVtR\nKBTUf8diioFPnrL9t/14Lktf7M/KsggBT4I0rx8FBmFtaZlDJSrYFLq6uE4ag/+S1ZoZO0nhLzEu\nURwUClCpMC3jlG9Tl7Nj76lrfN7BDUcbC00rUNPa5XAuYcf8Tcc06Q784c3c73ozpEczxszeqtk+\nqHsTgkMj+evmwyzPP/7L9kRGx7Fu758ARES/IiU1DZeSDvgHhWJlYYqNhSkvIjIPFM9NzqVKsPCX\nHzJtX7TGU11nRg6muIM9sXHxREZrD05/GPCYxes281XvrlSpUA4jQ4NM5wkMCmbbvkNsWjxbs82q\nSIY68zgIa8u8DQxziknpUlRftYiEp8F4D/n2gwZaK3R1cZkwGv9l6fVGGRGB8ScZ6k0e3mhlO0Cq\nXLkyhw4dYv/+/Tx//pzq1aszZcoU7O3t339wBu9aq+jLL7/UGu/zRsaAJD4+Hl9fX378UXuRs79b\nl8jS0pJff/31g/Kze/duzf9tbW01A7TflnFdpjez5fLKr1Mmom9giGvFypgXKcKTgEecOLgPIyNj\n+g75RpNutsdELK1t+KRkKeLj4jh5eD8hz58xefbCTGOQ3li7eD4NW7TSWqHbrXEzpv/wHZtWLwOV\niqsXz/HjrAW5Xs7smLb/Iq+Skqlewg7bwsZEvErgsM8jHofHMLZNLYz01Zf986hXHHrdbH83WH2n\ntuasepyMg4Up7auUznTurX/dBaB33fTPpIFzUWYeusTE3WepXMyGNX/6aBajLAgszAvTtIFbpu2e\nO34DhULTDeblfZPvp0ynXYumFC9WlKSkJE6ePc/NO750d2+Pq3PWrcSzFi+nbYumVHBNL3OLxg0Z\nNcmDhSvXoULFnxcvsfTXqblTwI/0yec90C1shuHrO1Lb5o0xdFCvBP544zZS4+IxdLDHsUt7AMwr\nlgfAaZh67FBC8HOe/34403lLfPGZ5hxvhJ89T/kpE6iycAZR130o/c1AgrZnMR4jD33doxkWZsY4\n2qpvGDo0rkoxO3UQu2TbSV7FJzJr7SE+bVGTE2vGs2TrCcxMDBndtw0+94PwPJA+G+lZWBSLNh9n\ndL826OsV4uqdQDo1rY5bFWf6TVqZ5fsXt7dkdL82dHxrFe20NBUHz95g7ne9+cTBis7NqhMcFsUl\nn7zrXgN1nWlWP3PLuueufSgUCk03WFbMTIxRqVRUdC2b5TkAZi1dTbvmjajgkl6nWjZyY+T/prJw\njScqlYqzf3mxbGbWjQP5qXifHuiZmWHwuuvdplljDF///j/etA1UKmqsXYKumSkBqzdi01T7UVXx\nT55muU5Sif7qevPEc7tmW/jZC5SbPIHK82cQ7e2D07CveLpjb24VLZOPmotub2/P4MH5u6DV9u3b\nWbFiBX369KFo0aL5mpf8VLdhU86ePMr+nVuJj4/D3KIIbo2b0XPAQOwd01eTdnYtz6kjBzh+YC/6\nBgZUqFyNcR7TKVk66x+965cv4nvrBsu3an+J13JrSN9Bwzi4Rz2Lre/gb6hWu25Wp8g3rSuVYt+1\nB+y+6kd0fBImBnqUc7BidKuaNHzrWWzBka9YdtobBekthctP3wCgRkm7TAHSy1cJrPnzJtO7Ndaa\noWZpasTcXk2Zc8SLy/7PaeJanMFN/h1jbd4uu6O9HTWqVubUuYtEvIxUt8SW/ITJ40ZpVsjO6Pwl\nL7x9bnNo+wat7U3q12XU4C/Zsud3UKkYNeQr6tfOugUqv5Qc2A8jx9c3dioVdq2aYteqKQDP9h4i\nNS4eo+JFcR49TKt7wPlb9dIfL69cyxQg6VupH8Vwc/QkrZk2yohIvIeNxfWHcVi51SH05B/4L87f\n8Uej+7XmE3t1669KBZ2aVqdTU3W38ZZDF3kVn0hwaCQtBs5i9theTB3RDWVyCofP3WT8/J2a8Udv\nTFq0m5cxcQzs2oS+Herz8EkI/X9Yxa7jXlm+/8xve3DknA/nr9/X2j58+iZW/Nifn4d/yoPHL+g+\nerHWI03y3QeMF/+7cbTnLl/j+q27HN6s/fdv4labUYP6sfW3g6hUKr4d1J/6taq/4yz5p+SXfbXr\nTcum2LV8XW9+PwQKheamo+y4EZmOD957MFOApG9ZBKehX+EzNkO9eRnJjeHjcJ00Fqt6tQk9dTZP\nx+0pVO8aefwOsbGxbNiwgTt37hAfrz3OoqCsKP1vci8k68db/H/kaqceOB2/4/2rpf9/YNxT/Syq\n5LD8nRVZkOjZfMLR0tXyOxsFRht/9Vpm+tXybmZPQaf0XkfK8wf5nY0CQ9fBGYBjZQtesJVfWt+/\n/v5EfEQL0rhx44iOjqZNmzYYfsC0RiGEEEKIf5tsB0hXr17l/PnzGL1j3IoQQgghxL9dtqf5lyhR\ngpgsngAuhBBCCPFfke0WpI4dOzJ06FD69euHtbW11r53rVckhBBCCPFvku0AadOmTQCZprwrFIp3\nTtsXQgghhPg3yXaAdPr06dzIhxBCCCFEgfFRjxtPSUnh6tWrHD6sXgMkPj4+05R/IYQQQoh/q2y3\nID18+JCvv/4ahUJBeHg47dq14/Lly+zfv5/58+e//wRCCCGEEAVctluQpkyZwuDBgzlx4gS6uur4\nqnbt2ly9ejXHMyeEEEIIkR+yHSD5+fnRvXt3IH05dRMTExITE3M2Z0IIIYQQ+STbAZK9vT0PH2o/\nmfnevXv/r5+HJoQQQoj/lmwHSAMHDuTrr79m+/btpKSksG/fPsaMGZPvD68VQgghhMgp2R6k3alT\nJ8zMzNi+fTsODg4cPHiQsWPH0rx589zInxBCCCFEnst2gATQrFkzmjVrltN5EUIIIYQoELLVxZaW\nlsaLFy9IS0sD4MKFC0ydOpVdu3blSuaEEEIIIfLDBwdIXl5e1KtXjyZNmtCiRQsOHTrEhAkTCAsL\nY+HChSxevDg38ymEEEIIkWc+uItt1qxZjBs3jo4dO/Lbb78xadIkdu7ciYuLCw8fPmTIkCGMGDEi\nN/MqhBBCCJEnPrgFKSAggO7du2NoaEivXr1QqVS4uLgAUKZMGSIjI3Mtk0IIIYQQeemDAySVSpV+\nkI4OhoaGuZIhIYQQQoj89sFdbCkpKezYsUPzOjk5Wet1ampqzuZMCCGEECKffHCAVKVKFQ4ePKh5\nXbFiRa3XlStXztmcCSGEEELkkw8OkDZt2pSb+RBCCCGEKDCy/agRIYQQQoj/OgmQhBBCCCEykABJ\nCCGEECIDCZCEEEIIITKQAEkIIYQQIgMJkIQQQgghMpAASQghhBAiAwmQhBBCCCEykABJCCGEECID\nhertp9AKIYQQQogPf9SIyB1p9y/kdxYKDJ2y9QFIvrwvn3NSMOjV6QzAqy0/53NOCg7TPpPl+njL\nm2tEGf40n3NScOhbF0O/2pf5nY0CQ+m9DgCfZ9H5nJOCo7Kj+Qelky42IYQQQogMJEASQgghhMhA\nAiQhhBBCiAwkQBJCCCGEyEACJCGEEEKIDCRAEkIIIYTIQAIkIYQQQogMJEASQgghhMhAAiQhhBBC\niAwkQBJCCCGEyEACJCGEEEKIDCRAEkIIIYTIQAIkIYQQQogMJEASQgghhMhAAiQhhBBCiAwkQBJC\nCCGEyEACJCGEEEKIDCRAEkIIIYTIQAIkIYQQQogMJEASQgghhMhAAiQhhBBCiAwkQBJCCCGEyEAC\nJCGEEEKIDCRAEkIIIYTIQAIkIYQQQogMJEASQgghhMhAAiQhhBBCiAwkQBJCCCGEyEA3vzMgct7y\nHQdYvGUfziWK8vvin7X2efs+ZM76nfg+eoKJsRFtG9Ti235dMTY00KQJjYjkxyUbuX73AXZWRRg7\noBtNa1fVOs/xC1f5ecVmjq2cgYmxUZ6U60P5B4ewbO8J7gQGExEVi6GBPqUdbRnQrjFNqpXTSqtS\nqdhx+hK7z1wh8EUYRgb6uBR3YPznHXEuZg9AbHwCUzfu45yPH4VNjBjcsRmfNq6ldZ7bj57yxfQV\n/D5zLI7WRfKsrB/iUVg0K8/64Pv8JRGvEjDU08XJ2py+buVoVLaYVtqA8GjmHrvGzaAw9Arp0MC5\nKKNbVaeIsaEmTWyikpmHvbj48BlmRvp81aAinaqV1jrP3WcRDNp4gt1DO+BgYZon5cyOD71GKvWf\n8M5z1KtQhlXfDwT+/ddIVlZu2MzSNRso41SS3zzXaO1LTklhw5YdHDh2kmfPX2BqakIF17J4fD8G\nWxtrAELDwpkyax7ePrexs7Fm9LBBNK5fT+s8J878ydS5Czm8YxMmJsZ5VraMjA31GTegLTUrOlGr\nQimKFDZmoMc6Nh+8mCmtS0l75ozrjVtVZ5TJKRw578N3c7cTEfUqU9qx/dswqFtTHKzNefAkhNnr\nDrHz2BWtNO5NqjF1RFfsbSw4d82Pb6Z58iI8WivN3gUjeRoayYjpm3K24B/h0f177Ny4Gr/bN0lW\nJmPr4EjLjl1o26WHJo3fbR82rVxM4EM/jIxNqNekBZ8NHIahUfpvxcvwMFbMmc692zewsrbl8yHD\nqVGvodZ7XTp7mjULZ7N48x6MjE3yrIxvSID0HxMSEcmaPYe1Ap43fB894cv/zaH0Jw5MGNiLF+GR\nrNt7lMfPQ1np8a0m3fj5awh7Gc24Ad257vuAMbNWcHD5VIraqr/4lMnJzNmwi2/7flrggiOAZ+GR\nxCcq6dygBjZFCpOYlMyJq7cZsWAjHl98SrcmtTVp/7d6F4cv3cC9fnU+a+lGQpIS38fPiIh+hfPr\n2OHXbYe46hfAiK6tePwinJ/W/0bporZUKVNCc55ZWw7Qr03DAvnD9zzqFQnKFDpWccLGzIjE5FRO\n+z5hzPaz/NChDl2qlwEgNCaegRtOUNhQnxHNqxKXlMKmv+7yMDQKz4Ft0NVRNzjPP3Ed7yehDG1a\nmScvY5l28DJONuZUKmatec85x67Rp265AhkcwYdfIzOH9Mx07O2Ap2w5foH6lcpqtv3br5GMQsLC\nWLd5O8ZGmet3Skoqw8ZOxOeOL13d21G2tBMxsa+4ddeXV3FxmgBp0tRZhIdHMHrYIG7cusO4H3/h\n963rcbS3A0CpVDJv2SpGDfkqX4MjAOsiZkwa1JHHzyO46feExjVds0znaGPB6XUTiYqJ44dFuzEz\nMWRMvzZUKF0Ut76/kJqapkk7bWQ3xvZvw+o9Z7l2NxD3JlXxnD6YtDQVu094AVDS0ZrNM79mx9HL\nXL7lz8jPWrJqype4D5+vOU/LehWoV7UM5dwn5u6H8AFuel1i1g/jKFXWhW59B2JoZETIs2AiwkI1\naQIe3ufncd9QrEQp+g8bTURYKPt3bOZF8FMmzUwv15IZU4iMCKfv4BHcu32TeT9NYsGGndjYOwCQ\nrFSyedVien81NF+CI5AA6T9n1todVHEpTWpqGlGx2nc08z33YG5mgueMCZoAytHWCo+lG7l44w5u\nVSuQpEzmyq17bJoxgerlnenZtgnevg+54H2HHq0bA7D2t6OYmRjTrVWjPC/fh2hYxZWGVbS/4D5r\n6Ub3HxfhefSc5sfv6OWb7L9wnUWj+tG0evl3nu/PG/cY17s9HdyqAeAX9Jw/vH01P34HLlznWfhL\nBnb8KpdK9M/Udy5KfeeiWtt61ipLn9VH2HLJVxMgrT1/m6TkVFYOboFtYfUPVgVHK4ZtPsWBG480\n6c4/CObbltVpV6kUAPdfRPHn/aeaAOmwTwDPo17xRYNmeVXEbPvQa6T967/52y77+qNQKGhbN71V\n9d9+jWQ0Z/EKKlcoR2pqKlExMVr7PHfs4rrPLTyXL6KCa9ksj09KUuJ1/QYbls6nWuWK9OjcEW+f\n21y8cpVu7u0BWL91J2ZmpnzasV2ul+d9noVFUbzFaMIiY6lWrgR/bf4xy3QTBnbAyECPWoNm8yws\nCoCrdwI4snws/dzrs37vOQAcbCwY2aclS7efYuyv2wDYsO8cJ9eMZ8bo7poAqUW9CgSFvGTQlHUA\n+AU+59iK79DX00WZnIKOjoJfx/Zi6sr9RMbE5fbH8LcS4uNYMvMnarg1YOyUme9Mt23NMkzNzPlp\nwUpNi5GNnT0r583A59oVKteojVKZxO0b1/h5wUpcK1Whpfun+N3x4ebVy7To0BmA/Ts2YWxiRvP2\nnfKkfFn5V49BatasGW3btqVz5860bduWVatWZfscW7ZsYeJEdWR++vRpZs589x/+jdDQUD7//PNs\nv1du87rtx8m/rjFhYK9M++LiE7h08y7uTetptS51auaGkYEBR8+rK2ySMhmVCszeuqMzMzEmMUkJ\npLdQTRrUO5dLk7MUCgX2VubExidotm06ep7KTsVpWr08KpWKhNdlzChRqcTsrS4mcxNjEpXJACQk\nKVmw6yije7bDyEA/dwuRgxQKBXaFjYlNTNZsO+MbRMOyRTXBEUBtJ3s+sSrMibuPNdsSk1Mxe6us\n5kb6JCanApCQnMKS0zcY0aIaRnr/rvuvrK6RjJJTUjh59Ta1XJ2wLVJYs/2/dI1cveHDybPn+X7U\nsEz7VCoVW3btpXnjhlRwLUtqaiqJSUmZ0iUplahUKsxM01sQzcxMSUxMBNJbqCaM+ib3CpINKSmp\nhEXGvjdd52Y1OHzupiY4AjhzxZcHj0Po1jK9S9W9STV0CxVi1a4zWsev2nWGYraW1K2i7pI2NtQn\nOjZesz8qJh6FQoGhgR4A3/RqgY5CwbIdp/5R+XLCuZNHiY6KpPdXQwFISkxEpVJppUmIj8Pn2hUa\ntWqr1Z3WuHV7DA2NuHjmJKBuHUKlwuSt68PYxIyk19dHRFgo+7Zt4osRY3K7WH/rXx0gASxYsIB9\n+/axYcMGVq1axa1btz76XM2aNWPChHePOXjD1taWzZs3f/T75Ia0tDSmr9pKt1aNKFuiWKb99x8H\nk5KaRoUyJbW26+nq4ur0Cb7+TwAobGpMcXsbVu48SHBIOAf++Au/gCAql3UCYM76XTSuWYUaFbK+\ncyxIEpKURMXGERQagefRc5z38aNuBWcA4hKSuPUoiApOxVi46yh1h3hQe/Bk2o6bzbErPlrnqehU\nnI1HzvEkJJwLPn5c8PGjUuniAKzafxr7IuaaloOCLCE5haj4JJ5GxrLlki8XHj6jjpN6nFVYbDwv\n4xIp52CZ6biKRa3wexGZ/trRis2XfAl6GcvFh8+46P+MikWtAFh37ja2Zsaa1qWC7u+ukaycvXGP\n2PhE2rtpj8n7r1wjaWlpzJy/hK7u7XB2yvw39A94TFh4BGWdSjFl1jxqt2hP7ebt6dp/EF7Xb2jS\nFTYzpXhRR1Z5biH4+QsOHjvJ/Yf+VCqvHt81b+lqGrnVoXqVSnlWtn/KwcYCW0szrt0JzLTP604A\nVV3Tu1OruHxCXEISfoEvtNPdDkChgKouJTSvq7mWoEfr2pRwsGLCVx3wDwol5lUC1kXM+GFwR8bN\n3U5amnYgkh9uXffC2NiEiNAQRvXrTt92jenXvimr589SBzzAk0f+pKWm4lRWu3VWV1eXkmWcCXzo\nB4CJqRl2jsXYs3k9oS+ece7EUR77P8C5fAUANq9cTPW69SlXSbue5bV/1y1eFt5EsHZ2djg5OfHs\n2TMqVarEmjVr2LdvHzo6Ori4uODh4YGpqSlxcXH873//4969e1haWlK6dPrg0r1793Ly5EmWLl3K\nlStX+OWXX6hZsybXr19Xf3HMnEmFChUIDg6mc+fOeHmpW12+++47AgICSE5OxsHBgWnTpmFlZZWn\nn8O2I2d4HvaS9dO6ZLk/7GUUCgXYFDHPtM+miDnX7z7QvP55eH++nbmcw+euoFBAf/dWVHUtjbfv\nQ05f9ubgsmm5Vo6c9Ou2Q+w6cxkAHYWCFjUrMqmvOwBBoRGogCOXbqJbSIdxvdtjYmTAlmMX+G7p\nVkyNDDVjTMb36ciwuetp//0cFEDLWpVoV7cqT8NesvnYedZNGpJPJcye+cev89s19d9ZR6GgWbni\nfNemJgDhsepWE2uzzGNOrE2NiElQkpyahl4hHca0rsGobWfosmQ/CoWC5uWK07piSYIjX7H18j1W\n9WuZd4X6h/7uGsnKob+80dctRMta2j/s/5VrZMfe/TwPCWXtoDlZ7n/8w3a40AAAIABJREFU9CkA\nnjt2Y164MFPGj0WlUrHacytDx05k29plmsDK4/vRjP3xZ46ePINCoaBvz65UqVieG7fucOb8BfZv\nWZ9n5coJ9tbq786MA6gBXoRFYVnYBF3dQqSkpOJgbU7Iy5hM6Z6Hq1ueHG0sAPjr5kOWbDvJxmmD\nUSggIjqOnuOWAvDzN59yycef4xdv51aRsuX50yBSUlKY9b/vaNGhE30Gf8OdG9c58tsO4uNeMep/\nvxAZEQ4KBUUsrTMdb2Fpzb3bNzWvh4ydyLwpE7l45gQoFHTo1puy5Svhd9sHrwt/smDjzrwsXpb+\n9QHSG/7+/kRHR1O7dm3Onj3L3r172bFjB6ampkyePJm5c+fi4eHBkiVLMDAw4MiRI7x69YoePXpQ\npUoVzXkUCoXm/wEBAcyYMQMPDw+2b9/OvHnzWLt2baZ0P/zwAxYW6gt+1apVLFq0iJ9++imPSg5R\nsa9YsuV3hvbqyP+xd99RVRztA8e/V7r0jh2ICnbA3hWxoIICllgwphiNMYnGGKP+3rQ3iZrExG7s\n3dgIscceY2LD3juKonSkd+7vjytXbkGNgpK8z+ccz/HOzs7uLHv3PjszO2tjqX9QbFFzv7Gebg8T\nYyNych91tTRvWId9S77jetQ9nOxtcLa3RalUMnnhz7we1JVKjnb8vH0/q7aomkuHBHamv3+H0q/Y\ncxrStQ1dmzUgLjmVncfOUqgsJDdf1RWUma3qFkhJz2TNZ6Oo765qdevgXZduH05hweZ96gCpVlUX\ntn83jmt3Y7GsaEo1J1Xw+92arXRp1pAG7tXYHXGenzbtISM7h95tGjOit99LqPHjDWrhiV/d6iSk\nZbH74m0KlUryHg4qzX54XIwNDHTWMzZUpeXkF2BkUIGaTjb8OiqQ63EpWJoaUdXWElAN3varW4N6\nVezZdymKhX+cJyM3j4BG7gxrVz5bCh53jmjLyMrh4JkrtPeqg4WZqcayf8M5kpKaytzFyxnxeijW\nVlZ682RmqQLpzMwsNi5boB6Q3ayxNz36h7J09Tq++c8n6rRdv/zMjchbODrY4+zoiFKpZOqMOQwd\n0A8XZyfWhW9m9YZwAAb3C6Zf74AXUNNnU9Q9mpOXp7Os6PpqZmJEWn4BpqbG5ObqyZejSjM1NVKn\njZu2lukrd+LsYM2lm/fIys6lUe1qDOzegiavfoaluSmzJoTSrokH127H8v7klTotUy9CdlYWubk5\ndAkMYei7qq6vZm06kJeXy56tv9L/9eHk5qquq0bGRjrrGxsbk1usO7a+dxPmrdvCnVs3sbV3wN7R\nCaVSydI5PxDYfxAOTs7s3LSRHb+oAqUeIa/SOTD4BdT0kX98F9uYMWPo3r07AQEBDB48GFtbW44c\nOYK/vz8WD/s3BwwYwKFDqsc1jx49Sp8+fQCwsLCgZ8+eJZZdvXp16tevD4CXlxd3H949adu0aRMh\nISEEBAQQFhbGlStXSrOKTzR95S/YWJkzqGenEvOYPjxhc/PydZbl5OZhonVCm5ma0KC2G872qidu\nwnYfJCE5hbdCuvPXqQt8v2w9H73el49e78u3S9Zx7NyLrfPTcK3kSPO6NQlo7cPsMUPJzM5l1A/L\nANT1reJopw6OACqaGNPeuw7nbt7R6F83MjSkrmsV9Q/f0YvXOXLhOmP6+RN5P56P561hSLe2/Pet\nvqzde4RNf554cRV9SjXsrWjm5kL3hm78+GoHMnPzGf3z7wCYPgyCcgt0g4OigMHE8FHwZGRgQJ1K\ndurg6FhkDMdu3ue9Tl7cSkhl4i9/MaiFJ58FtGDD8atsPXOzjGv3bPSdI+/+oL9lY1fEWXLz8vUO\n3IZ//jkyc/4SbKysGBDSu8Q8psaq8YteDeurgyMAFydHvBvU5/S5Cxr5zUxNqV/HE2dHRwDCt+4g\nISmZNwa/yqFjx/lhzgI+HDmMD0cO4/vZPxFx6gzlVdEYRRMj3R//outr1sMAKDs7F2M9QULR2KLs\nbM3gKToumZMXb5GVrdrGtI8HMn/j71yPimP2xFAqO9kQPHomF25E88uMDzRu0F8UYxPV3761r2YL\ncdtOXUGp5OrFcxg/PD/y9ASHubm56jKKmJiaUtOzLvaOTgDs276ZB0mJ9BowhDPHj7Jq/mwGD3+P\nwcPfY/m8GVw4fbIsqlaif3yANH36dLZv387ixYv5/vvvuXr1KkqlslROIJNif0wDAwPy83WDi+PH\nj7Nq1SoWLVrEli1bGD9+PDl6Bi2Wldv3Ytm48w8GB/gRm5hMdFwC0bEJ5OTmkZ9fQHRcAinpGTja\n2aBUQnyybvNwfHIKTnY2JW4jIzOLGavCGTu0D6Ymxuw4eIxurZvSsZkXHZt50aVVE7YeOFyW1SwV\nnZvU50LkXW7HJKgH2Npb67a42VlZkJ9fQGa2/kHbhYWFTFm9hbcCOuJgY8muY2fxruVKrzaNaerp\nTt+Ozdl66FSZ1qU0+NapzqX7SUQlpqq71oq62opLSM/CyswYIwP9l4tCpZJpO08wtE09HCzM2HPx\nNo2qOdKzkTuNXZ0J8anF9nORZVqX0tK5SX0uRkZzOyZBZ9m2Q6exqGhKu0YeTyznn3aORN2NJmzL\nNgb2DSIuIYF7MbFE348hJzeX/PwC7sXEkpKahqOjKvizt9WdqsDO1obUtJIHOmdkZDJr4VLGvDMM\nUxMTdu79nS6+7enQphUd2rSic4d2bNv18gcjl6Soa62oq604F0cbklIzyH94M3E/IQUXe918lRxU\n19nig7y19e3SFA9XF/770yYUCgXBfk34Yt6vnL4cxYTp66nqZEvzhu6lUaW/xc5eFRDb2GqOU7Sy\nUX3OSEvF1t4BlEqSk3S/Pw+SEtRl6JOVmcHaJT8x+O1RmJiYcmj/blq270STVm1p0qotLdr7cnDP\nb6VYoyf7xwdIRXf5LVu2ZODAgUyfPp1WrVqxY8cOMjJUj0WuW7eONm3aqPOFhYUBkJ6ezvbt259r\nu2lpaVhYWGBtbU1ubi7r1q173ir9LbGJD1Ci5JsFa+j81njVv2HjOXv1JpHRMXQZNp55a7dQq0YV\nDA0qcP6a5g9VXn4+l29G4elevcRtzPl5M9VcHOnRvgUAcUkPcCwWUDnZ2xCbWPIXvrzIfth6lpaZ\njaONFQ7WFsQl644TiEtOxcTIEHMz3bmkANbuPUJWdi5D/duq8zsWe6LJydaKOD2BaHmT8/B4pOfk\n4WhZEVtzUy7dT9LJdz46EQ/nkufu2RBxlazcfEJbqgbgxqdn4VhsLJOjpRnxegKv8qj4OVJcwoM0\nIi7fpEvTBhgZPnlkwj/tHImNT0CphCnT59CtzyC69RmEf9/BnLt4mVtRd/DvO5j5y1ZS290dQ0ND\n4hJ0fwDjEhKxtSn5Rmve0hVUrVyJ7p191fkdHR6N1XRysCcuXrfc8uJ+/APik9NoXM9VZ1nTem6c\nuRKl/nzmShQVTY3xcHXRyNe8gTtKJRp5izMxNuSb0X35bE446ZnZONhYYGRooA7OcnLzSU7LpIrj\ni59Ly7226vudlBCvkZ6cqPpsZWNHNTd3KhgYcOPKJY08+fn53Lp+DdeaJT/cs2H5IpwrVaFNp64P\ny03A1uFRQGVn76iz7bL2jw6QtFuJ3nnnHU6ePImDgwPBwcH079+fwMBAMjIyGDNmDAAjR44kOzsb\nf39/hg8fTuPGjZ9r2+3atcPV1ZVu3boRGhpKnTp1nrBm6apVowqzJo5i1sRRzJ706F/N6pWp7GjP\n7EnvEdK5LRYVzWjRqC5bfj+iHn8DsGnfIbJycujWpqne8iOjY1izfR+T3h6oTnOwsSLy7n315xt3\n7uFgq3/MwsuQlKo7o21+QQGb/zyBibERr1RRNed2a96ImMQHHLlwXZ0vOS2D309dpHm9mnrLTsnI\nZG74bsa+2kP9Q2lvbUHkvUcTpd2IjsXB2rI0q/RckjOyddLyCwvZejYSE0MD3BxVd7q+dapx8Go0\ncamPHjs+djOGqMRU/OrV0CkDIDUrh/kHzjG6szdGD8cv2ZubcivhUeB5MyEFe3NTveu/LE97jhTZ\nfuQ0SqWyxO614v6J50gtd1emf/MF07/5ghmTv1T/e8XNlcouzsyY/AXBPbtTsaIZbVs24/S5C9yK\nuqNe/+btKM6cu0CrZk30ln8r6g5rwzbxyehR6jR7O1tu3X4UKNy8HYWDffmeRDN87wm6t22kHmQN\n0LFZHWrVcGbjrgh12pbfT1FQWMjwfppzgQ3r24HouGQOn7mOPuPf6EFySgZLwv8AIDElnfyCQjxc\nVZMn2ttY4GhjQUziiw+uW3b0A6WSvds3a6Tv3bYJA0ND6np5U9HcgoaNm3Fw929kZz26KTqwcxvZ\n2Vm07KB/3N29O7f57deNvP7eWHWata0d0VGPphe5GxWJjZ3uU7Zl6R89SHvvXs3mWCsrK44cOQJA\n3bp1eeONN3TWMTc358cff9RJBwgKCiIoSPUUWLNmzQgPD1cvq1Wrlnp7VapU4dgx1XTxBgYGOuWN\nHj2aF8XWygLf5roX7eWbdqNQKDReETI6NJhBH08m9JMp9OvanpjEJJaF76K1d31ae9fTW/6URWvp\n0a65xvQAnVs15r2vZzN9RRhK4EDEWX769IPSrtoz+2LpL2Rk5dDY0w0nWysSUtLZdugUt+7HM25g\nT/Vgy7cCOrLz2FnGzFpJaNc2WJiZsmH/UfILCvmgbze9Zc/auIva1SrRuWl9dZpfk/r89Ote/rss\nnEr2Nmz8/RjjB5WfwaZfbztGRk4e3tWdcLIyIzE9mx3nIrmdmMaYLj7q+YreaFOfvRejeHvFHgY0\n8yAjN49Vhy9R28WWgEb6m/Tn7j9DLWcbfOs8aoHs6FmNhX+cZ/L2Y7hYmfPLiet81PXZbkTKytOe\nI0W2HjqFo60VTT2f3LXxTzxHbKyt6di2lU76ynUbUSigQ5tHyz4Y/iZHj5/izffGMqhfMIWFSn7e\nGI6NjTVvheqfH+3bmfPw7+yrMbFk5w5t+WDCp8ycvxilUskffx1hznfflH7lntKIfr7YWFakspMq\n+OnZ3ouqzqof5Nk/7yE9M5upi7cR7NeE3YvGM3vNbizNTRkT2o2zV++wYstf6rLuxT9g5qpdjBnS\nDWMjA45fuEWvjj60alSLIRPn691+NRc7xgzpRkCxWbQLC5VsPXCaaeMGUL2SPb19fYiOf8CRszfK\n8Ejo51azNh39A9j/21YK8vOp28ibC6dOcOTgfoIHDVU/uTbgzXf4v/fe4tMPhtO5Z28SE+LYsn41\nXk1b0KhJc71lL5sznTa+XXjF41EDQ4t2Hfn2/z5mzaK5oISTh/9kwmT9v91lRaHUnulJvFCFV/96\ncqZn8NrEb0lJS+dXPe9im7ZsAxdv3MbczBT/ts103sVW5OCJc3z47U/8Nn8y9jaaLUSLwnaweuse\nlErVU2xvBOsPKP6OCrVbA5B39NfnKue3o2f45UAE1+7G8CA9E3NTE+q6VmFQl9a099Js4YuOT+L7\ntds4euEGeQUFeNWqwZh+/tR1raJT7rW7Mbz62Sx+/nwUtatV0li2+a+TzA3fTWZWDr3aNuHD/v7P\nPQ7OqLlqsGz66i+fkPPxdl24zaZTN7ge94CUrBwqGhtRp5IdrzbzoG1tzXpGxqfww64TnC7+LrbO\nPtjqaQG6HveAIYt+Y/mbXaml1QW37exN5h84R2aO6im29/28S2VcoMWgT5/7/IC/d47cjkkgYPz3\nvObfjrGvPn7W55d1juQm6H+A5Hm9MepDUtLSCFu+UCP98tXr/DhvIWfOX6RCBQXNG/vw4ci3qVa1\nsk4Zfx45xrhPv2LruuU6Y5eWrF7Lmo2/olQqCe0XwtCB/XTW/7uMHapi7K17c/wkV7ZOpbqL/ulZ\navf8mDsxqu5nT7dKfDv2VVo/fBfb9oNnGP/jehL0TDT50VB/3grpQCUHa65HxTJVz7vYiqyeMgKF\nQsHA8fM00h1sLfnpP6/Rrokn127HMPK/yzlz9Y7eMvTJPaWapfvsvedvdSooKCB89TL2/7aF5IQE\nHFxc8O/dF/9gzVfyXDl/llULZhF57QqmZhVp1bGzzrvYipw6dpgfv5zIrJVhWGuNb9r08wp2hK9H\nqYQefV4lsH/pTNDcsLLu+DB9JEB6ycoqQPonKq0A6d+itAKkf5PSCpD+Lco6QPonetYA6d+qNAOk\nf4unDZD+0WOQhBBCCCHKggRIQgghhBBaJEASQgghhNAiAZIQQgghhBYJkIQQQgghtEiAJIQQQgih\nRQIkIYQQQggtEiAJIYQQQmiRAEkIIYQQQosESEIIIYQQWiRAEkIIIYTQIgGSEEIIIYQWCZCEEEII\nIbRIgCSEEEIIoUUCJCGEEEIILRIgCSGEEEJokQBJCCGEEEKLBEhCCCGEEFokQBJCCCGE0CIBkhBC\nCCGEFgmQhBBCCCG0SIAkhBBCCKFFAiQhhBBCCC0SIAkhhBBCaJEASQghhBBCiwRIQgghhBBaJEAS\nQgghhNAiAZIQQgghhBYJkIQQQgghtEiAJIQQQgihRQIkIYQQQggtCqVSqXzZOyGEEEIIUZ4Yvuwd\n+F+Xf/fCy96FcsOwaj0Apuy/9pL3pHz4pGMtAD7cdP4l70n58UOv+hTcOv2yd6PcMHD1AiD3r/Uv\neU/KD+PW/ejw44GXvRvlxu9j2gNg7P3GS96T8iP31JKnyiddbEIIIYQQWiRAEkIIIYTQIgGSEEII\nIYQWCZCEEEIIIbRIgCSEEEIIoUUCJCGEEEIILRIgCSGEEEJokQBJCCGEEEKLBEhCCCGEEFokQBJC\nCCGE0CIBkhBCCCGEFgmQhBBCCCG0SIAkhBBCCKFFAiQhhBBCCC0SIAkhhBBCaJEASQghhBBCiwRI\nQgghhBBaJEASQgghhNAiAZIQQgghhBYJkIQQQgghtEiAJIQQQgihRQIkIYQQQggtEiAJIYQQQmiR\nAEkIIYQQQosESEIIIYQQWiRAEkIIIYTQIgGSEEIIIYQWCZCEEEIIIbQYvuwdEKXvp1UbmL1sLbVc\nqxO+6Ed1+sI1Yew/FMGd+zFkZGbj4mhPuxaNGT6oD7bWVup8aekZ/HfmAg4eO4W1hTlvD+pDsH8n\njW2cv3KdoR/+h81LZlDZ2emF1e1pHFw+netH9pa4vP+U5VS0tiP64ikij/9B/K2rpMTcwdzOkb5f\nLdbJn5uVweE1c7l74QQmFS1o6N+P2q27aORJuH2NHdM+IeizeVjYl6/j8ap3FZpUsylx+Rc7r5CW\nk6+RZmpYgQl+tTE3NmB5xB3O3U9VL7MyNaRfoyq42VckJSuPrRdjuRibprF+w0pWhDSszDd7r5KT\nX1i6FSoDP60JY9aKDdRyrcavP32nTj904izbDxzi3JXr3IyKppKTA7uWz9JZPy0jky9nLeJgxGms\nLS14e0AQIV07auQ5f/UGr330BZsXTqOKs2OZ1+nvuBEdx9xN+7h4+x6JKemYGhvhXtmJ17u1ob2X\nh0ZepVLJ+t8j2HggglsxiZgZG1G7mgvjB3SnVlVnANIys/lq1Rb+PHcNq4qmvN2zPUFtG2uUcyEy\nmtenLubXr96nskPJ5+fLVMXalDdbu1G/shWWpkbEpWaz50oc647fJbdAdV43rm6Lr4cjdVwsqWFn\nTmxaNgOXHNMpy9zYgNG+tWjuZkdadj6rjkWx40KMRh4PZwum9/Vi6PIIYtNyXkgdS1LR1JiPhvrT\npL47Teu5YWtVkbc+W8KqrYd08nq4uvD9RwNo5VWL3Lx8dvx5lnHT1pL4IF0n79jXujGsT0cqOVhz\nLSqWb5dsY/1OzeMV2MGbr94LwcXRhoMnrvDu1yuISUjRyBM+/X3uxiXz3jcrS7fixUiA9C8TG5/I\n4rXhVDQz1Vl24eoNPGu50d23LeYVTblxO5qN23Zx8OhJwhZMw9TEBIDvflrGibMXeW/oAG5H3+Pz\nH+bxSo2qNKr76EI5Ze4ShvQJKHfBEYBnO38q1/HSTFQqObRmDpYOLlS0tgPgZsQBbp04iH31V6ho\nY19iecc2Libm+gV8AgeTGnePQ6tnY1OpOk7unuo8R9cvpJ5f73IXHAEcupXE1TitC5UC+jaqTGJG\nnk5wBOBfxxmjCgq95Q30qYqViSFbL8Tgal+RIU2qMXXfNZKz8gAwqKCgZz0Xtl+K/UcER7EJSSxa\nv1nvd2br/j/Z+ccR6tR0w8nBrsQyvl2wghPnLvP+a/25HX2fz6cvoGb1KjSqU1udZ8pPy3ktpEe5\nC44A7iU+IDMnl16tvXGysSIrN489Jy7w3qzVfPZaICHtmqjz/t+ScHYcPUtgKy8GdmpBZk4el6Pu\nk5iaTi1UAdL3637jxJVbjOrdiajYRL5Yvhn3yk40eqWaupypP28ntEurchscOVoY89NAH9Jy8vnl\n9D3SsvOoW8mK11u6UtvJkv9suQCAn6cTHWo7ci0unfj0koOake1eoVFVa5YcukVVGzM+8qvN7cQM\nLsY8urkY1b4mG07cfenBEYCDrSUThwVw+34iZ65E0b6Jp958lR1t2LdkAg9SM5g0cyOW5qZ8OKQb\n9V6pQqvQ/1JQ8Oga8PX7fRj7WjcWhh3gxMVbBHbwYsU3b1NYqGTj7ggAXCs7sGrKCNb9dpSj527w\n/sDOLPj8DQJHPbrZ79yyHi29alIncEKZHgMJkP5lvvtpGY3q1Ca/oJCUVM27+umff6yTv1Hd2nz4\n5ff8fvg43Tq0BuDA0ROMG/4aPf3aA3Dlxm1+P3xcHSBt2X2AezFxDBsQUsa1eTaObh44umne9cZe\nv0h+bg7uzTqo0xr3fo3Woe9RoYIBe+Z8QfL9KL3l3T0XQdOQN3iluapFIOluJHfOHlMHSDeO7ic9\nMZaG3b4smwo9p6jkLKKSszTSXO0qYmRQgZN3H+jkd7Y0oaWrLbuuxNPNUzPgM6ygoKaDObP/jORW\nUiaHbyfj5meOh5MFR24nA9CxpgNZeQUcjUouu0qVom8XrKCRZy0KCgp4kKYZSI55YyD/HTMCA4MK\njPx0Ktdv39VbxoFjpxg3bDABvm0BuHLzNvuPnFQHSJv3HiQ6Np5h/XuXbWWeUduGtWnbsLZG2sBO\nzen3xVxW7DykDpB+O3aOLYdOM2PUADp61ymxvD/OXmFsv270bNkIgCt3Yzhw+rI6QNpy+DTRiQ+Y\n36NdGdXo+XWp40JFY0PeXXtK/f3Zdj4GA4WCznWcMTc2ICO3gIV/3uS73VcoVMI3verjal9Rb3kt\n3O2Y98dN9lyOA+AVRwtauturA6TOnk64WJmyOkL/dehFuxf/gGp+Y4hPTsO7Tg0Or/qP3nyfvNUT\nMxMjmg77lnvxquvJ8QuR7Jg3liGBrVkafhCASo42vD+oM3PW7mXsdz8DsOzXg+xZNJ7JY/qqAyS/\nlvW4E5vEsM+XAHDl1n12/jQOYyNDcvPyqVBBwXdjX+Wr+ZtJTs0o02PwPzEGadeuXQQHBxMUFET3\n7t15/fXXUSqVhIaGsnevZlfMhAkTWLFihfrzyZMnCQ0NpWvXrnTu3JmxY8cSHx+vsc727dsJCQnB\n39+fPn368M4773Dt2rUXUrfijp+9wO4/jzJ+5OtPvU5lZ0eUSiWp6Y9OtOycXCwtzNWfra0syMpR\n3dFkZecwffEqPhwWipmpSentfBm7eex3FAoF7k0fXZArWttSoYLBE9fNz83BuKKF+rOJuSX5uTkP\nl2Vz/NflNAkaiqGxbgtEedW4qjVKJZyMTtFZFtSgEmfvpRKZqHvxMXzYqpSVV6BOy8orwMhAdSmx\nNjXEt5YDv567X0Z7XrqOn7vI7r+O8cmIIXqXO9rZYGDw5MtkdnYOVubFvjOWFmQX/84s+ZkP3xz4\nj/rOKBQKXOysScvMVqet3H2YBu5V6ehdB6VSSVZOrt51s3PzsKr46PtgbW5GVq6qhTErJ5cZG3cz\npk8XzEyMy7YSz6GiserakJyZp5GelJmLUqkkr0D58HMehconl2diaEB6sdba1Ow8TI0MHi6rwLA2\nbsz/82a5aXXNzy8gPjntifl6+zZm+8Ez6uAIYP+xS1y7HUufzk3VaYEdvDE0MGDBhv0a6y/YsJ+q\nTna0aPQKoOraS0nLVC9/kJqJQqHA1MQIgHdf9aOCQsHcdSUPoygt//oAKT4+nk8//ZS5c+cSHh7O\n9u3b+fjjj1Eo9HcfFHf58mVGjhzJqFGj2LlzJ7t378bT05PQ0FByHl78wsLCmDlzJt999x07duxg\n48aNvPfee8TFxZV11TQUFhbyzezF9OnuRy23Go/N+yAljYSkB5w4e5FvZi/CwMCAZo3qq5c38KjJ\nsg2buR19nz8jTvFXxCkaeqruLhes3oizg726demfoLCggFun/sLplbpY2P39LjBH19qc3xNOatw9\noi+cIPrCCRzdVMfjzPb1mNvYq1uX/gkqKKBhZWtuJWXyIEvz4t+oshU1bCuy9UKs3nWz8wtJzMil\nc21HbM2M8KlqTWUrU6KSVRe0nnVduBSTRmRSpt71y5PCwkK+nruMvv6dqOVa/bnKauBRk2VhW7kd\nHcOfx0/z5/EzNPSsCcD8n8NxdrRTty6VZ1k5uTxIz+ROXBIrdh3iz3PXaFHXHYCMrBzO37xLfdcq\nzAzbTct3v6b5yK/o/smP7Iw4r1FOfbeqLN/5F1Gxifx1/hp/nbtGQ/eqACzcegBnO2t161J5dfru\nAxQKGN/Fg1cczHG0MKZjbUcCG1Qm7FS0egzS07ock0Y/n6pUsTalaQ1bmrnacenh2L7BzaoTn5ar\nbl36p6jkaIOTnSUnLtzSWRZxIRIvz0e/RY08qpORlcOVW5rjriLOR6JQgJdHDfVnb88a9OvajBqV\n7PnkzZ7cuBNHanoWDraWTHo7gI+mraXwaaLS5/Sv72JLTEzE0NAQS0tLdVqdOiU3DRe3ePFi+vTp\nQ/PmzdVpw4YNY9euXWzbto3g4GBmz57Nl19+ibu7uzpP3bp1S69TdAcgAAAgAElEQVQCT2nt5p3c\nj0tgyfcDHpsvIekBHfq9qf7s4mjPd5PG4Fqtsjpt/MjXeWfi1/R4bRQKhYIu7VrQ3bcNd+/HsvKX\nrSydVj67kkoSfeEE2emp+BTrXvs7mvV9i91zviDss+EoUFDDpzXuTduTlhDDxX2b6Pbh5NLd4TLm\n6WSJubEBJ7S61wwrKAio58KBGwk8yM7D3txI7/obztxjSJNqeHW2BuDAjURuJ2fhamtG/UqWTN17\nvczrUBrWbt3N/bgE3pv66XOX9cmIIYz4z1S6vzkahQK6tGlB9w6tuRsTx8rw7Sz77vm38SJ8v+43\nNhw4DkAFhQK/xnWZMKgnAHfik1ACO46exdDAgLH9umJhZsKqPUf4+Kf1WJqZ0qq+Kij8+FV/3p2x\nkp4TZ6AAOjeph3/zhtyNT2bV7sMsGf/GS6rh04u4ncySQ7cY1Kw6rV5RjVFUKmHVsSiWHr71t8ub\nc+A6k3s3YOXrzVAq4cC1ePZdjcfFypQ+PlUZveFM6VbgBXBxUF0DtAdQA8TEP8DOyhxDQwPy8wuo\n5GBNbFKqTr77CarrUGVH1Vi0w2euM/vnPSz/+m0UCkhMyaD/R3MA+PLdYI6cvcGuQ+d1yikL//oA\nycPDAx8fH3x9fWnatCne3t707NkTZ2fVYMLJkycze/ZsQPV0xv3799UB1MWLF+natatOmV5eXly4\ncIEOHTpw//59vLy8dPK8SA9S05izfC3vhPbFxsrysXmtrSxY9N3n5Obmcul6JHsOHiEjU3N8Si23\nGmxfMYfrkVFYWphTrbILoBrf1KVdKxp41mL3wSPMW7mezMwsenXtyDuh/cqsfs/rZsQBDAwMcfVp\n/Uzr21ZxJeTLBSTfu42JmQWWjqrjcWzjYlx92uDoWptbpw5xZtvP5GVnUbNlJ7x6PD5QfZl8qlpT\nUKjk7D3Ni1WnWo5UUCjYey2+hDVVridk8N/dV3CxNCU1O4+UbFW3Qe8Glfj9eiIPsvNo5WpHW3fV\noOY/biRy+Hb5Go/0IDWd2Ss3MHJQCDZWFk9e4QlquVZnx5IZXL/18DtTSXV9+XbBCrq2a0EDj5rs\n/vMoc1eHkZmVTe/O7XlnUPkbwxfapRVdmtYn7kEquyLOU1hYSG6+6u+bma3qTkvJyGLN/w2nnlsV\nANp7eeL/8Q/M3/q7OkCqVdWZbZNHcy06DkszU6o5qc6F79ftoHPT+tR3q8qeExf4afPvZGTn0Ku1\nNyMCy18rbExqNmfuPuDAtQTSsvNo4WbP4GbVSc7M5dcz9/5WWZGJmQxeegw3B3PSc/K5n6LquhzZ\nzp0DV+O5EptG25oOvNa8BmbGBvx2MYaVR8vHeKSSFHWR5uTl6SzLftilamZiRFp+AaamxuTm6smX\no0ozNX10QzZu2lqmr9yJs4M1l27eIys7l0a1qzGwewuavPoZluamzJoQSrsmHly7Hcv7k1fqtEyV\nhn99gKRQKJg5cyaRkZFERERw4MAB5s+fz8aNGwHVmKNOnR49wj5hwgSd9bUplWXftPd3zFyyBhsr\nSwb27v7EvEaGhrTwbgBAu+aNae7VgMEfTMTe1pp2zR89hmtsZETd2q+oPx85dY7DJ8+ybflsIu9E\nM+7rH/jiw3eo7OzER19No7KzI726lL8LXH5uNlFnj1KlXmNMzB8fPD6OgaERDtVrqj/fu3yG+5dP\nE/zFAlJi7nJg8be0HvweFnZO/L7oWyzsnajZotNjSnw5jAwU1HOx5HJcOpnFxhHZmhnRoaYDYWfv\nqcdWPE5egZI7Dx4F1s2r22JpYsi+a/HUdjSnZz1nVh1XDWgObVKVuPQcbiSWn263GcvWYmNlwcBe\n3UqtTGMjQ+rWetSSfOT0eQ6fOs/2xdOJvHOPj6bM5MsP3qaysyNjJ8+gspMDvTqXr65qVxcHXF0c\nAAho6cWIH5bz3ozVrPnPcEyMVT8XVRxt1cERQEUTY9o38mDbkbMolUr1NdPI0JC6NR61TB+9dJMj\nF2+ydfIHRN6P5+P5G/j8tV5UcrDh45/WU9nehsDW3i+wto/nW9uRsX61GbT0GEkZquDwzxuJVFDA\n8Dbu7LkcpzGm6GnkFyq5VuyJUu9qNjSubkvosmNUszXjP/51+H7PVWJTs/m0Rx1iU3PYdUl/d3d5\nUDQGzcRIt7XZ1NjoYR5VAJSdnYuxsZ58D8cWZWdrBk/RcclExz26sZr28UDmb/yd61FxLP96GJUc\nbQgePZMhgW34ZcYH1O89sdR/m//1Y5CKuLm50a9fP+bMmUPDhg3Zt2/fE8ch1a1bl5MnT+qknzlz\nhnr16mFnZ0elSpU4depUWe32E92Ovs/GbbsZFNSDuIRE7sXGER0TR25uLvkF+dyLjSMlTXcuiiJe\n9TxwtLNl694/SsxTWFjI1DlLGDYgGEc7W3YeOIR3PU96delI00b16NezC1v3lLz+y3T71GEKcnM1\nnl57XsrCQo5tWEjDbn2paG1L5Mk/cXqlLjVbdMKldgM82nbjxtHfS217palhJSu9T691q+NESnYe\nNxMzsDUzwtbMCMuHd3QWxgbYmunvbgPVAFP/Ok5svRhLXqESryrWnIlO5WJsGhdj0zhzLxWfquXn\nUe7b0TFs3LGXwb38iU1IIjo2nuiYOHLy8sjPzyc6Nv6x35mnUVhYyJSfljOsfy8c7WzYefAwPnU9\n6NW5PU0b1qVfdz+27PuzlGpUdvwa1+PCrWhuxybiZKOaK81eT4ubnZU5+QUF6lYmbYWFhUz9eTtv\n9WiLg7Ulu45fwLtmdQJbe9PUw42+7Zuy9Uj56mIKbFSZa3Hp6uCoyKGbiRgbVqCW0/O1PCqAUe1f\nYXVEFEmZeXSo5cj5eynsuhTLmegUtpy9T+c65W/akOKKutaKutqKc3G0ISk1g/x81Y3Y/YQUXOx1\n81V6OM1D8UHe2vp2aYqHqwv//WkTCoWCYL8mfDHvV05fjmLC9PVUdbKleUP3Etd/Vv/6FqTY2Fii\no6Px8fEBICUlhejoaKpXr/7EaPPNN99k6NChtG3blhYtWgCwcOFCUlNT6dGjBwDvvvsuU6ZMoWrV\nqupxSJcuXSI5OZlWrVqVYc1U4hJU4wImz1nMN7MX6SzvOngkg4N6PPbJtpzcXNLTS767/3nzb2Rm\nZ/Na30DVNhOTcLJ/NCeMo70dsQlJz16JMnTj2O8YmphSrWGzUivz0oFt5OVkUc8vCICsB0nquZUA\nKtrYk/kgsdS2V5p8qtqQk1/IhRjNp1NsTY1wMDdmol9tnXVCGqlaASZtv6T3CZsuHk4kZuRy6uET\ncdamRtwt1rqUmp1PZavy84RfXGISSpR8M28pX89dqrO869D3CO3dnfHD9T/Z9jR+3rKLzKxshoYE\nPNxmMo72turlTva25fY7U1zOwy6R9Mxsajjb42BlQVyy7jiSuORUTIwMMTfT/5Te2v3HyMzO5bWu\nqm7u+AdpONo8atF1tLHUW+7LZFfRmNRs3S4hg4dPcho8xYM+j9O7UWXMjA1Yf0LV0mpvYUxisWAs\nISMXB4vy/dTj/fgHxCen0bieq86ypvXcOHPlURfhmStRvN67LR6uLhrdYc0buKNUopG3OBNjQ74Z\n3ZfP5oSTnpmNo60lRoYG6uAsJzef5LRMqjja6l3/efzrA6SCggLmzJlDdHQ0ZmZm5OfnExQUhK+v\nL8uWLXvsup6ensydO5dp06bx2WefUVBQQMOGDVm5ciUmDydV7NOnD2ZmZnz00UdkZWVhYGBA9erV\nGTt27AuonWrsw4wvdOc3mrlkDZlZ2Ux4902qVXYmKzsHhQL1ZJBFdv1xmNT0DOp71tQpAyAlLZ25\ny9fx+YfvYPywGdXB1obzlx8NxL1x+w4OduWnhaBIdnoK9y+fwb1ZBwyNSudx4pyMdE5v/ZlWg0dh\nYKg6HqZWNsTfuqrO8+B+FGZW5e94mBsbUMvRnJN3U8jXegJk+6VYzE00Lwculqb413Fi/7UEbiVn\nkqsnOHI0N6aNmx2zDt5Up6Xl5ONk+eg8c7Y00TsZ5ctSy7UaMz/9SCd9xrK1ZGZlM3Hk61R1efY7\n95S0dOas2sgXHwzD2Eh1TO1trTl35YY6z43bd8vVdyYpNQM7K3ONtPyCAjYdOo2JsRHulVWTW3Zt\nVp81e45w5OINWtRVdcEnp2Xw++krNK+r/w4+JSOLeZv289lrgRgZPjweVuacu/loTqmb9+JxsH72\nLvCycCc5kyY1bKlibUp0yqOpDvw8nVEqldxMePZWRgsTQ4a2dOX7PVfV38XkzDzquDx6o4GrXUWd\n1qvyKHzvCQb3bEVlRxt1K1DHZnWoVcOZH1fuVOfb8vsppo0bwPB+vnz47Rp1+rC+HYiOS+bwGf0P\nd4x/owfJKRksCVf1UiSmpJNfUIiHayVu3InD3sYCRxsLYhJ1B4o/r399gFS5cmUWL9Z9fQSgMd9R\nkcmTNZ9I8vHxYfXq1Y/dRo8ePdQtSi+ajbUlvq10W0dWbNyKAgUdW6nmobh8I5K3xn1Btw6tcate\nhQoKBeevXGfr3oNUreTMoCD9+z9zyRpqu9egc9sW6jS/Ni2Yt3IDX86YTyUnRzZu2834keXvqZSb\nEX+gLCzklRK615Kib3HnzFEAUuPvk5eVyZnt6wCwq+qmt9Xp5OaV2FZ1xdX7UetgDe9WnNm2lsNr\n5mJu58iVgztp3m9Y6VfoOXlXsaaCQqHz9BrALa2JJAGyH45RinqQpdPiVKRX/UqcvJvC3WI/IGfv\npfJ6s+r413FCgYK6zpYsPHK7lGrx/GysLPFt2UQnfcUv21AoFHRs8Wgs3tXIKPYdVj3VFXUvhrSM\nTH5a8wsAnu416NCisU45M5evw8OtOp3bPHr6tXPr5sxbHcaXsxZRycmBDTv28smI10q7as/syxWb\nSM/KobGHK842ViSkpLHtyFluxSQw7lV/9WDct3q0Y1fEeT6cs5bQLi0xNzNlw+8R5BcW8n5wZ71l\nz/plD7WrOuPXuJ46rVPjuvy0+Xe+WrkFFztrNv5xnI9f9X8hdX1aa0/cpZmrHbP6exN+OpqU7Dxa\nudvTtIYd287fJ+nh/Ehu9ua0fviUWxVrU8yNDRncTDVtxI34dA5H6rYUvtnKlRvx6Ry8nqBO++Na\nAkOa12C0b03i0nLo2aASsw/c0Fn3RRrRzxcby4pUdlIF8z3be1HVWdVaPvvnPaRnZjN18TaC/Zqw\ne9F4Zq/ZjaW5KWNCu3H26h1WbPlLXda9+AfMXLWLMUO6YWxkwPELt+jV0YdWjWoxZOJ8vduv5mLH\nmCHdCCg2i3ZhoZKtB04zbdwAqleyp7evD9HxDzhytvSP1b8+QPqfVqwF2MXBgS7tWnLs9Hk27/6d\n/PwCKjs7MjioO8MGhmBtqduffi3yNuG/7WPtnKka6bXda/D1x6OYu3wdGVlZDOjVnb499F8cX6ab\nEQcwtbKhkqf++VYSo25waotm8Fv0uWZLX50AKTn6FtcO7abnJz9opNtVcaXNa6M5vXUNeTlZ1OnQ\ng9ptdJ9+fNm8q9qQlpPPtfjSmX3W08kCN/uKTN5zVSP9Ymwa2y/F0sbdHgWw7WIMV+Ofb0zPi6Ld\naXLxeiSzV67XSCv63MuvvU6AdO1WFL/s3M+6md9opNd2q843Y0cyZ9VGMjKzGBjYlb7+5WcQf7dm\nDQg/eJIN+yN4kJGJuakJdWtUZmy/rrRr9GhWensrC1ZMHMa0db+xcvdh8gsK8apZjanD+6rfw1bc\ntbux/PrnSdb8Z7hGeu2qLnz1ZjBzN+0jMzuXAb7N6dNeN2h9mc5FpzBq3SmGtnAlsFFlrE2NuJ+S\nxaK/Ill7/I46X21nC15v6aqxbtHnnRdjdAIkN/uKdK/vwog1mmNXIxMzmLLrMkNbuFLR2IDwM/fY\n+pInXB0zpCvVXR5NcdCrow+9OqqGq6zedoj0zGyi45Lxe2sq3459la/e60NuXj7bD55h/I/r1eOP\nikycuZGk1AzeCulAaM/WXI+K5bVJC9iwK0Lv9qeM7seOg2f586TmNWbUNyv56T+v8eWoYK7djqHv\nmFkarzQpLQpleXsk639M/t0LL3sXyg3Dqqo7zCn7X/ws5OXRJx1rAfDhphcz58c/wQ+96lNw6/TL\n3o1yw8BVNcVI7l/rn5Dzf4dx6350+PHAy96NcuP3MaonJY29y18r/8uSe2rJU+X7n3mKTQghhBDi\naUmAJIQQQgihRQIkIYQQQggtEiAJIYQQQmiRAEkIIYQQQosESEIIIYQQWiRAEkIIIYTQIgGSEEII\nIYQWCZCEEEIIIbRIgCSEEEIIoUUCJCGEEEIILRIgCSGEEEJokQBJCCGEEEKLBEhCCCGEEFokQBJC\nCCGE0CIBkhBCCCGEFgmQhBBCCCG0SIAkhBBCCKFFAiQhhBBCCC0SIAkhhBBCaJEASQghhBBCiwRI\nQgghhBBaJEASQgghhNAiAZIQQgghhBYJkIQQQgghtEiAJIQQQgihRQIkIYQQQggtEiAJIYQQQmiR\nAEkIIYQQQosESEIIIYQQWhRKpVL5sndCCCGEEKI8kRYkIYQQQggtEiAJIYQQQmiRAEkIIYQQQosE\nSEIIIYQQWiRAEkIIIYTQIgGSEEIIIYQWCZCEEEIIIbRIgCSEEEIIoUUCpH+xjIwMvL29+c9//qNO\nO3bsGJ6enkyePFkj78cff4ynpyeXL18GYMKECbRr147g4GC6du3KoEGD2LRp0wvd/7Lg6+tL9+7d\nKSwsVKeFhIQQERHB7NmzadmyJUFBQfTo0YMxY8aQlpYGQGhoKJ06dSIoKIiePXsyfPhwkpKSXlY1\nnlpJ9d2+fTshISE6+ZcuXcq7776rXvdpz4fo6GiaNm2qUZa+86888PX1xd/fn969e9OzZ09Wr179\nt8sIDQ1l7969f3u9oKAgMjMz1ftRdHzLyt+t6+zZs3WuDfo8a/2fxYQJE1ixYsUL2Vbx4+Xv78+C\nBQv+dhmrV69mwoQJAOzbt48pU6Y8cZ24uDgGDx78t7f1suzatYvg4GCCgoLo3r07r7/+OkqlUu95\nof33O3nyJKGhoXTt2pXOnTszduxY4uPjNdYpuj75+/vTp08f3nnnHa5du/ZC6lac4Qvfonhhtm/f\nTv369dm1axcTJ07EzMwMgBo1arBv3z7GjRuHoaEh6enpnD59GhcXF43133rrLYYMGQLA5cuXGT16\nNMnJyQwdOvRFV6VU5ebmsmHDBvr37w+AQqFQLwsMDGTChAkolUref/995s6dy/jx4wGYNGkSvr6+\nAHz++ecsXryYcePGvfgK/E366mtlZcW9e/e4du0atWrVUuf95ZdfGDt2rN5ynnQ+FD+OUPL5Vx5M\nnz4dDw8P7t27R2BgIE2bNqV27dpPtW7xYPPvCg8Pf+Z1n9Xz1PV/UdHxio2NpUePHrRs2ZIGDRo8\nU1m+vr7qa8bjODk5sWrVqmfaxosWHx/Pp59+yq+//qr+zbh06ZLO91+fy5cvM3LkSGbMmEHz5s0B\nWLhwIaGhoWzatAkTExPCwsJYuHAhc+fOxd3dHYCLFy8SFxenca16EaQF6V9s48aNDBs2jCZNmrBj\nxw51upmZGS1btlRH+tu3b6dr164YGBiUWJanpyeTJk1i4cKFZb7fZW3UqFHMnTuXnJwcAPS9bUeh\nUNCyZUsiIyPVaUX58vPzyczMxNra+sXs8HPSV18TExMCAgLYuHGjOt/Zs2d58OAB7dq1e2KZT3M+\nlHT+lQdFf8vKlSvj5ubGlStXGDJkCCEhIQQEBPDVV1+p84aHhzNkyBDef/99AgMDOXfuHACHDx+m\nT58+dO3alalTp6rzR0VF8frrrxMYGEhQUBB79uxRL/P09CQ9Pf0F1VJFu663bt1i8eLF9OzZk8DA\nQMaNG6d3nwICAjhz5oz687p16/jwww/Vn0uqf0JCAqNHj6Zfv34EBgYyY8YM9TJfX19mzpzJq6++\nip+fH/PmzVMvi42N5f333ycgIIBevXoxc+ZM9bLr168zdOhQunbtyvvvv09+fn7pHBw9io6Xs7Mz\n7u7u3Lt3D4BFixbpPWYZGRmMGTMGf39/Bg0axJUrV9RlhYeHq1tkjx07RkBAAF988QW9evUiICCA\nCxcuALotsOPGjaNPnz706tWLESNGkJiYWGb1/bsSExMxNDTE0tJSnVanTp2nWnfx4sX06dNHHRwB\nDBs2DEtLS7Zt2waoWjEnTZqkDo4A6tatS+vWrUupBk9PAqR/qWvXrhEbG0vbtm0JCQnR+CFUKBQE\nBwer08LCwggJCdEbKBTXqFEjkpKS/hFdS4/j6elJixYtWLZsWYl5srOz2bt3L/Xr11enTZ48maCg\nINq2bcuVK1cYOHDgC9jb51dSfUNCQtiyZQsFBQWA6jwICgqiQoWnuyw87nzQPv82bNjw3PUoC1eu\nXCEyMhJPT0/mz59PWFgYmzZt4u7du2zfvl2d79y5c4wdO5bNmzfTqFEjAG7cuMH69evZtGkTx44d\nU1/gP/roI7p3787mzZuZPn06kyZN4v79+4BuK9uLVFTXuLg4fvnlF9auXcvmzZsxMzNj2rRpOvmH\nDBmi0TXy888/a3QDlVT/8ePHM3jwYNavX094eDjnzp1j586d6vXS0tJYu3YtGzZsYPHixcTFxQGq\noKBBgwZs2bKFTZs2ERoaqrHvCxYs4LfffiM+Pp5du3aV+vHRduPGDVJSUmjWrBkHDhwgPDxc7zGb\nPXs2JiYm7Nixg/nz53P8+HGNcor/zSMjIwkJCWHTpk0MGjSIH374QW++SZMmsXHjRjZt2oSPj49G\nsPiyeXh44OPjg6+vL6NGjWLx4sXExsaqlxddJ4OCgujduzf79u1TL7t48SJeXl46ZXp5eXHhwgWS\nkpK4f/++3jwvg3Sx/UuFhYXRq1cvFAoF7dq149NPP+XmzZvq5V5eXsTExPDnn39iaGiIq6vrE8v8\nN73X+P3336dfv37qbqcimzdvJiIiAoCmTZvy9ttvq5dNmDCBTp06ATBr1iwmTpxYri5cj6Ovvh4e\nHlSpUoX9+/fTtm1bduzY8bcCmcedDyWdf8XvCl+mMWPGYGpqiqmpKZMnT6ZatWpMnTqVkydPolQq\nSUpKonbt2nTv3h0Ab29vatSooVFGr169qFChAqampgQGBnLo0CE6dOjAxYsX1eO7atSoQePGjTl+\n/DgBAQEv5Ts0ZswYTExMMDMz45tvvuHUqVP4+/tjYWEBwIABAxg9erTOeoGBgcyaNYvExER1S6qP\nj496ub76+/r6cvToUZKSktR1zcrK0miJDQgIAMDW1pZq1apx9+5dLCwsOHnyJEuXLlXns7W1Vf/f\nz88PY2NjABo2bEhUVFRpHR4dY8aMQaFQcOvWLSZMmICtrS1Hjhwp8ZgdPXqUiRMnAmBhYUHPnj25\nc+eO3rKrV6+uvuny8vLSqG9xmzZtYvPmzeTm5pKbm6txLF42hULBzJkziYyMJCIiggMHDjB//nz1\nDXfx62TRZ+31tZXX3xYJkP6F8vPz2bx5M0ZGRmzduhWlUklOTg5hYWG0b99ena+oqfhpx9GcPXsW\ne3t77OzsymrXX5gqVarQs2dP5s2bp3cM0pN0796dPn36lOUulqqS6lvUupiZmYmHh4dOEPA4xc+H\n6Ohodfrjzr/yMmaraJxJkXnz5pGcnMzGjRsxMjJiypQp6i5JgIoVKz6xTIVCUS4v9Np1PXny5FO1\nZJmYmBAUFMTatWu5efMmgwYNemz+4vVfv349RkZGevMVBToAFSpUULdgQsk/lCYmJhrrlGUXW9Hx\nOnz4MCNGjKB58+YolcpSaf0rXg8DAwO99Th+/DirVq1i/fr12Nrasm/fPmbNmvXc2y5tbm5uuLm5\n0a9fP9566y327dv3xGNUt25dTp48qRFAAZw5c4YBAwZgZ2dHpUqVOHXq1FN19Zc16WL7F9qzZw/V\nq1fnwIED7N27l3379rF27Vp+/fVX8vPz1RehkJAQ3njjDfz9/Z9Y5uXLl5k8eTLDhg0r691/YUaM\nGMHmzZvVTfx/x6FDh3BzcyuDvSo7+urbs2dPIiIiWLp0qd6n2kryuPPhcedf8R/Dl0n7hzg1NRUH\nBweMjIxISEjgt99+e2IZW7ZsIT8/n+zsbLZu3UqrVq2wsLCgXr16hIWFAarxSKdOndJ5wu9F0q5r\nq1at2LFjBxkZGYBqbFGbNm30rjtw4EDWr1/P0aNHCQwM1Fimr/4VK1akefPmzJ8/X50vLi5OowtG\nn4oVK9K0aVOWLFmiTntZXflFx6tly5YMHDiQ6dOnP/aYtWzZUv33Tk9P1+iafZbtpqWlYWFhgbW1\nNbm5uaxbt+55q1SqYmNjOXnypPpzSkoK0dHRVK9e/Yk3CG+++SZhYWEcOXJEnbZw4UJSU1Pp0aMH\nAO+++y5TpkzR6PG4dOkShw4dKuWaPJm0IP0LhYWFqZuxi7zyyis4OzuTmZmpjvLt7Ow0fuC0o//F\nixcTHh5OZmYmDg4OjBgxQuci+U9TvI62traEhoY+9d3Z5MmTmT17NgUFBVhbW2sMTC2vnlRfCwsL\n/Pz82Ldvn06g/Kznw+POv/379+Pn51caVXtm+u5yQ0ND+eCDDwgICMDJyYlWrVo9sQx3d3cGDBhA\namoqnTp1UnfHff/993z66aesWrWKChUq8PXXX6uf9im+7RcxHknfNtq1a8f169fp378/FSpUwMPD\ng88++0zv+s7OztSpUwd3d3eN1o/H1f+7775j8uTJ6nPA3NycL774AmdnZ539Kf556tSpfPXVV/Ts\n2RMjIyM6derEqFGjnvsY/B3a+/fOO+/QpUsXRo0aRXBwsN5jNnLkSP7v//4Pf39/7OzsaNy4Mbm5\nuc+87Xbt2rF582a6deuGra0tLVu2fKabuLJSUFDAnDlziI6OxszMjPz8fIKCgvD19X3suE5QjYec\nO3cu06ZN47PPPqOgoICGDRuycuVK9fnVp08fzMzM+OijjyV2ArUAAA0NSURBVMjKysLAwIDq1auX\n+HRtWVIoy2ObsBBCiJcuMzMTf39/1qxZQ5UqVV727gjxQkkXmxBCCB1r166le/fuDBo0SIIj8T9J\nWpCEEEIIIbRIC5IQQgghhBYJkIQQQgghtEiAJIQQQgihRQIkIYQQQggtEiAJIYQQQmiRAEkIIYQQ\nQosESEIIDeHh4Tov8X3RPD09NV5wWhaGDRumfkXEP0Vubi6enp7cu3evxOUjR46kadOmvPnmmy94\n70q2ZcsWhg4d+lR5jx8/TpcuXcp2h4R4CvKqESHKqdDQUM6cOYOhoSEKhQI3NzfGjx//Qt7r9SJe\ng/Gyt79w4UL1/8PDw1m7dm25e++VPo87Njt37iQmJoYjR45gYGBQKtvz9fXlyy+/LPF9bU8jICBA\n5/UzJWnSpAm7du165m0JUVqkBUmIcmzSpEmcPHmSEydO0L9/f0aNGkVhYeHL3q0yU/Qy2xc9f21p\nva39RXjcsbl79y5ubm7PFBw96zEvLy8gFqK0SYAkxD9EQEAAKSkpGi+u3LRpEwEBATRt2pTBgwdz\n/fp19TJfX1+WLl1KcHAwjRs3Zvjw4aSlpamXnz17lsGDB9OsWTPatGnDggUL1MuUSiU//vgjzZs3\np3379mzZskW9bMKECXz++ee8++67eHt7ExwczJ07d1i0aBFt2rShbdu2Gm80/+OPP9T70LFjR2bM\nmKFeFh0djaenJ+Hh4XTq1IlevXrp1Pvy5cv4+vqye/duvcdl6tSptG7dmsaNG9O9e3ciIiLU+/nD\nDz+o8928eRNPT0/159DQUNatW8fVq1f5/PPPOXfuHN7e3vj4+JCdnQ3A0qVL6dq1K82bN2f48OEl\nvpX+2LFjOi0s/fv359dffwXgzp07vPbaazRp0oQWLVoQGhqqzpeQkMCYMWNo3bo1HTt2ZPbs2Rp/\nh2nTptGyZUt8fX3ZvHlziYHcjz/+yJw5c9i5cyc+Pj7qF4cuXLgQPz8/WrRowahRozTOH19fXxYt\nWkRwcDBeXl4kJCRolDl27Fju37/PqFGj8PHxYfr06YCqC3TNmjX4+/urWzQXL15Mly5d8PHxoUeP\nHhqtQNrdtp6enqxfv169/rhx48jPz9d7LJ90Hm/duhU/Pz+aN2/O1KlTNY67EM9FKYQolwYPHqxc\nu3atUqlUKgsKCpSrV69Wdvz/du49pMm+jQP4d65Z04rUysOKpwibUVotXdZI85CYHWiRmogLOkkH\nO5jVoGAdoBMVslIiDFKKDtABKcwoohNRUmRWpB2tLafVOi1Pu+f1/CHdb5ubTxa9T73v9fnr3n3/\nTvdvt9wXv+s34+LI4XAQEdHFixcpPj6eampqqK2tjY4dO0YJCQlkt9uJiCguLo60Wi1ZLBZqbGyk\n9PR0MhqNRERUV1dHKpWKTp06RXa7nWw2G1VWVhIR0cmTJ2n48OF06NAhcjgcdP78eRo1ahTZbDYi\nItLr9aRWq6myspIEQaAVK1ZQQkIC7d27lwRBoLKyMoqMjKSWlhYiIqqoqKDq6moiIqqpqSGNRkPl\n5eVERGQymUipVFJubi7ZbDaxjlKppGfPntGtW7coJiaGbty44XaOrl69SrGxsfT27VuxvVevXonj\n3LVrl1j26dOnFBYW5nZ+T548Senp6U5tl5SU0MyZM+n169ckCALl5+fT7Nmz3Y7j5s2bpNFonM6l\npaXRqVOniIgoNzeXDAYDORwOEgSBKioqiIiora2NZs2aRfn5+dTa2koNDQ2k1Wrp+PHjRER07Ngx\nSkpKIpPJRF++fKHFixdTWFgYmc1mt+PYs2cP5ebmip9PnDhB8fHx9Pz5c2ppaSGDwUAZGRni9bi4\nOJo6dSqZTCYSBEF8tr4VFxdH165dczqnVCopKyuLrFar+J2Vl5dTQ0ODeDxy5EiyWCxu51epVNLc\nuXPp48eP9O7dO0pMTKQTJ064ncvOnuOnT5/SyJEj6ebNmyQIAu3fv5+GDx8uzjtjP4NXkBj7jW3b\ntg1qtRqjRo3C9u3bsWrVKnh5tf/ZHj16FAsWLEBoaCgkEgnS0tIgkUhw9+5dsb5Op0NgYCDkcjmS\nk5Px4MEDAEBpaSmioqIwY8YMdOvWDb6+voiIiBDrBQYGIjMzE15eXpg0aRK8vLzw4sUL8Xp8fDwi\nIiIglUqRkpICq9WKxYsXQyqVIjk5Gc3NzXj58iWA9j0lQ4cOBQCEhoZiypQpuHXrltN95uTkwNfX\nF97e3uK5CxcuIC8vD3v37kV0dLTb+ZHJZGhtbUVNTQ0EQYBCocCAAQN+Ysb/4+jRo1ixYgWCg4Mh\nlUqxZMkS3L9/HxaLpcttyWQyvHnzBiaTCVKpFJGRkQCAqqoqWCwWLF++HDKZDP369cOcOXNw9uxZ\nAMDZs2eh0+mgUCjg4+ODnJycLvX7dXP0oEGD4O3tjdWrV+PevXt49eqVWCYrKwsKhQJSqVR8tlyR\nm/RbdnY2/Pz8xO8sKSkJ/fr1E48HDx6Me/fueRzbokWL0Lt3b/j7+yM2NhYPHz70WNbTc1xWVobY\n2Fio1WpIpVLMnz8fvXv3/ueJYew78CZtxn5jer1eTE1UV1dj/vz56NOnDzQaDcxmM3bs2CGmkYgI\ngiA4pVD69u0rHvfo0QONjY0AgLq6Ovz1118e+/36ovtKLpeLdV2v9+jRA35+fk6pn2/7qqysxK5d\nu/D48WPY7XbY7XYkJiaKZSUSCUJCQjqMobi4GImJiQgPD/c4zrFjxyInJwe7d+9GbW0tJkyYAL1e\n32H8P8JsNmPlypVi0EBE6NatGywWC4KCgrrU1po1a2A0GqHT6SCTyZCamoqFCxfCbDbDarVCrVaL\nfRARgoODAQANDQ3iMQAoFIou7RWqr6+HQqEQP/v6+qJPnz6or6/HwIEDAcCp/a5wrXf69GkcPHgQ\nZrMZANDU1IT37997rB8QECAey+XyDum9b3l6jl3nRyKRIDAwsGs3wpgHHCAx9odQKpVQqVS4dOkS\nNBoNgoODsWDBAsyYMaPLbQUHB+POnTu/YJQd5eXlITMzE0VFRfD29sbWrVs7fRkC7S+6/Px8GAwG\nGI1GLFu2zGPZjIwMZGRk4PPnz1i3bh127tyJ7du3w8fHB01NTWK5N2/edNqfq5CQEGzcuPG7fjXo\n4+Mj7lty15+/vz82bNgAoH1P1Zw5cxAREYGQkBAEBQXh4sWLbtvt378/6urqxM9ms7lLm8kDAwPF\ngAUAvnz5gg8fPjgFeP/Unqfr355//fo11q9fj+LiYowZMwYAoNVqf/lm+/79+6Ompkb8TEQe94kx\n1lWcYmPsD/HkyRPcvn0bSqUSQHtgsH//fjx69AhA+8vv0qVLTis9nkyfPh0VFRUoLS2FIAiw2Wyd\npkN+RmNjI3r16gVvb29UVVXhzJkzTtfdvUSJCAEBASgpKUF5eTn27Nnjtu2qqircvXsXdrsd3bt3\nh1wuF1d8hg0bhitXrsBqteLDhw8oKiryOMaAgADU19ejtbVVPDd79mzs3r1bTEd9/PgRZWVlbusP\nGjQIDocDFy5cgMPhwOHDh51W8srKysTUXM+ePSGTySCVShEeHg5/f38UFhaiqakJRITa2lpxo3lK\nSgpKSkpgMplgs9lQUFDg8R7cmTZtGoqLi/HixQu0tLRg586dCA8P71Iasm/fvmK61JOmpiZIJBL4\n+fmhra0Np0+fxuPHj7s01h+RnJyMK1euoKKiAg6HAwcOHMCnT59+eb/s/wMHSIz9xrZs2QKVSgWV\nSoXs7Gykp6cjNTUVAJCQkIClS5dCr9cjKioKycnJKC0tFet2tjIQFBSEAwcO4MiRIxg3bhwmT57c\nYV/Qz/i2b4PBgIKCAowZMwYFBQWYPHmyx7Ku5wICAlBcXIxz5845/brrK5vNBoPBgOjoaMTExMBm\nsyEvLw9AexAYERGBpKQkZGZmdvjng9/2Gx0djbCwMEyYMAFqtRrNzc3IysrC1KlTkZ2djcjISGi1\nWly/ft3t/fbs2RMbN27Epk2boNFo8O7dO4wYMUK8/uDBA6Snp0OlUkGn00Gn0yEqKgpeXl7Yt28f\namtrkZSUBLVajZUrV4qrT6mpqZg0aRJSU1Mxbdo0TJw4sbNp70Cr1SItLQ3z5s1DTEwM6uvrxV+i\nuc6BJwsXLkRRURHUajWMRqPbekOGDMG8efOQkZEBjUaD6upqjB492mObXVkF66zskCFDsHnzZuj1\neowfPx5WqxVKpdJpLxtjP0pCv3oNlDHGGPsvcDgc0Gg0KCwshEql+reHw/5wvILEGGPsj/U1rdzc\n3Ayj0Qi5XN7pxn7Gvhdv0maMMfbHunz5MtauXYu2tjYMHToUBQUFkMlk//aw2P8ATrExxhhjjLng\nFBtjjDHGmAsOkBhjjDHGXHCAxBhjjDHmggMkxhhjjDEXHCAxxhhjjLngAIkxxhhjzAUHSIwxxhhj\nLjhAYowxxhhzwQESY4wxxpiLvwHFEASF7PZ/RwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd868320828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = amd_benchmarks\n",
    "metrics = cgo13.classification(data, group_by=\"suite\")\n",
    "results = np.zeros((len(metrics), len(metrics[0])))\n",
    "\n",
    "for j in range(len(metrics)):\n",
    "    for i in range(len(metrics[j])):\n",
    "        results[j, i] = metrics[j][i].oracle * 100  # % optimal\n",
    "results = results.T\n",
    "\n",
    "groups = sorted(set(data[\"Group\"]))\n",
    "\n",
    "ax = sns.heatmap(results, annot=True, fmt=\".0f\", linewidths=.5,\n",
    "                 xticklabels=shortlabels(groups),\n",
    "                 yticklabels=shortlabels(groups), cbar=False,\n",
    "                 cmap=\"RdBu\", center=50, vmin=0, vmax=100)\n",
    "for t in ax.texts:\n",
    "    t.set_text(t.get_text() + \"%\")\n",
    "plt.xlabel(\"Benchmark suite used for training\")\n",
    "plt.ylabel(\"Benchmark suite used for testing\")\n",
    "viz.finalise(figsize=(6, 6), tight=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The benchmark suite which provides the best results, NVIDIA SDK,\n",
    "achieves on average only 49% of the optimal performance. The worst case is when\n",
    "training with Parboil to predict the optimal mappings for Polybench, where the\n",
    "model achieves only 11.5% of the optimal performance. It is clear that\n",
    "heuristics learned on one benchmark suite fail to generalize across other\n",
    "suites. This problem is caused both by the limited number of benchmarks contained in\n",
    "each suite, and the distribution of benchmarks within the feature space.\n",
    "\n",
    "**Figure 3a**: A two dimensional projection of the Grewe\n",
    "et al. predictive model over Parboil benchmarks on\n",
    "an NVIDIA GPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FhnGghEid+akGz/P4c1C/Dl/j6aHAq9NGSqbBjr7mSa8l9FTD010umZn8KUFJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd868c6d278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = cgo13.model()\n",
    "parboil = nvidia_benchmarks[nvidia_benchmarks[\"benchmark\"].str.contains(r\"^parboil-\")]\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "pca.fit(cgo13.raw_features(parboil))\n",
    "\n",
    "B_out = cgo13.xval_benchmarks(clf, parboil)\n",
    "X = pca.transform(cgo13.raw_features(parboil))\n",
    "\n",
    "ax = plot_pca(X, B_out)\n",
    "\n",
    "xlim = ax.get_xlim()  # we'll use these for the next plot\n",
    "ylim = ax.get_ylim()\n",
    "\n",
    "viz.finalise(figsize=(2.5, 2.5), tight=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we see, there is a dense cluster of neighboring benchmarks, a smaller cluster\n",
    "of three benchmarks, and two outliers. The lack of neighboring observations\n",
    "means that the model is unable to learn a good heuristic for the two outliers,\n",
    "which leads to them being incorrectly optimized. Now we train the predictive\n",
    "model with additional benchmarks which are neighboring in the feature space.\n",
    "\n",
    "**Figure 3b:** Two outliers in (a) are incorrectly predicted due to the lack of nearby observations. The additional neighboring observations in (b) corrects this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hAOTk5PDkk0/a161cuRKT6Y8JtlesWMGePXvcLrMjiYmJjXK70OUMOnr0aGbOnMmUKVMI\nDQ2tss6ZmcBbg6YaYtaYfr56hryyAnRy3UOxJEkir8zAz1fP0D/C+WlbKtu3bx9RUVHs37+fhQsX\nOrwaq6u+blvXqVMnPv/8c/vylStXMm3aNPvoq5baecblAP3ss8+Aml37JElqNR0PGqKphpg1tl9y\nL9YbnDY6WeaX3AtuB+j27dt54YUX2Lp1K19//TUTJkzAYrHw9ttvc/jwYQIDA+3T69h8+OGHJCUl\n0b59+ypjMzMzMxk3bhypqaksWbIESZJ4/PHH0el0rF+/nnfffZfevXszZcqUeoerORr+tmfPHjZu\n3IjFYkFRFF588UUeeOABt96D+rgcoA3tLdTaNdUQs8akKAo3SvOces2N0nwURakyasQZv/76K9eu\nXWPIkCFYLBbWrVvHhAkT2Lp1KxkZGezbtw9VVXn66aftr0lOTmb//v3s2rULf39/Xn755Sr7tH3x\nvfnmm2zdupXNmzdXef6ozapVq+ocruZo+NuQIUMYNWoUUPGFMGnSJA4cOICXV+NdFbnVrLht2zam\nTZtm7xOZmprKvn37PFKwlqIphpg1NotqRcG5OZMUVcGiNLyjf3U7duxg7NixSJLEfffdx+XLl7l4\n8SJHjhxh7Nix6HQ69Ho9jz76qP01R44cISEhwT5aZdKkSS4d+/Dhw3UOV3M0/O33339n+vTpjB49\nmhdeeAGDwcCVK407w77LAbpy5Ur+8z//k/Hjx9ufJNWpUyfWrVvnscJpnTtDzLTES9bjJTuXBbx0\nepfr0xaLha+++opdu3YxbNgwHnroIYxGIzt27Giyy/+6juNo+Nu8efOYNGkSSUlJ9ize2HNwuRyg\nO3bsYO3atfZvQah4rNzvv//uscJpXVMMMWsKkiTRJdC56VG6BEa4HEzfffcdt912GwcPHuT777/n\nhx9+YMuWLezevZuBAwfy1VdfYbFYMJlMVZ56PWjQIL755htKSkoqHlJ1c8iZTeWW8rqGhzk7XM2m\nsLCQyMhIAHbv3o3BYHD63J3lch3UZDIRFBQE/PFtVF5e3qY6LTTFELOm0i88mgs3Mhrc0f+uzlEu\nH2vHjh32apFN9+7dCQsLIzQ0lG7dujFy5Eh7I1F6ejpQ8XTttLQ0JkyYQEBAAPfdd1+VfVT+wnjq\nqaeYNm0a/v7+rF+/vsp2zgxXq/z7woUL+dd//VcCAwO5++67q8y/1ViZ3+XHD86dO5du3brx4osv\nEhcXx9GjR1m9ejUZGRn8+7//e4P20RqGm2lxiFllzgw323/hIOnXztfZ8KMoCr073UnCnf/PU0Vs\n1Zrt8YOLFi0iJSWFIUOGUFJSwrBhwzhw4AB/+ctfXN1lixQV1rNBz9QE7Q8xe7D7fUR37o2KWuuc\nRKqqEhXWi4fvuL+ZStj2uHytFRoayhdffEFaWhqZmZlEREQQHR3tcrN7S9WahphJksTw7vcyMKIv\nqVk/k1V4DbNiwkv2IrxDGHERMQT6iS5+TcmtypAkSfTt25e+fft6qjwtjm2IWfWeRJVZFYVbA8Nb\nzBCzQL8ODO/etnqDaZUYbuYBrWWImaA9YriZh2h5iFlGRkZzF6HNysjIoGvXri6/Xgw3awRaGmJ2\n++23N3cR2rSuXbu69TdwOUBtw81EgGqbXq93aUY/QRvEcDNB0DAx3EwQNEwMNxMEDXPrPujhw4fZ\ntWsX165dIywsjHHjxjFo0CBPlU0Q2jyX7wFs376duXPn0qlTJxISEujUqRPz5s2rMcJAEATXuZxB\n161bx7p164iK+mNUQ0JCAvPmzWPixIkeKZwgtHUuZ9C8vDx69epVZVmPHj3sj2sTBMF9LgdoVFQU\na9asqfIMybVr19KnTx+PFU4Q2jqXL3EXL17Mc889x6ZNmwgPD+fq1au0b9+eNWvWeLJ8gtCmuRyg\n3bp1Y+/evZw8eZKcnBzCwsKIiYlBr9febAGC0FK5FU16vZ4BAwaQl5dHcHCwGK0hCB7mch20uLiY\nV155hejoaOLj4+nbty8LFiwQj4MQBA9yOUAXLlyIwWBg27ZtHD16lG3btmEwGHjttdc8WT5BaNNc\nvsQ9fPgwBw8etE8i3KtXL959991GnwpfENoSlzNo586dyc/Pr7KsoKCAzp07u10oQRAquDXcbPr0\n6Tz55JOEh4eTnZ3N5s2bGTt2LCkpKfbtxNAzQXCdywG6ZcsWgBqTAtuWgxh6JgjuEsPNBEHDmn9G\nK0EQHHI5g164cIHly5dz5swZ+7SbqqoiSRKnTp3yWAEFoS1zOUDnzZtHXFwcs2bNEhOHCUIjcTlA\nMzMzWbRokejeJwiNyOU66PDhw6s8lVgQBM9zOYMuWrSIxx57jC5dutSYdnP58uVuF0wQBDcC9LXX\nXkNVVbp27SrqoILQSFwO0B9//JHk5GQCAwM9WR5BECpxuQ56++23YzKZPFkWQRCqcTmDjhw5khkz\nZpCYmCge/SAIjcTlAN20aRMgHv0gCI1J9MUVBA0TfXEFQcPEs1kEQcPEs1kEQcPEs1kEQcPEs1kE\nQcPEs1kEQcPEs1kEQcPcfjbLqVOn7K244tksguBZTl/iHjhwwD57vF6vJzY2lkceeYTY2FiWLl3K\nwYMHPV5IQWirnA7QTz/9lFGjRtW6btSoUXzyySduF0oQhApOB+jFixe5++67a103YMAALly44Hah\nBEGo4HSAlpaWUlZWVuu68vJy+wx/giC4z+kA7dGjBz/++GOt63788Ud69OjhdqEEQajgdIBOmzaN\nN954g2+++Qar1QqA1Wrlm2++YenSpTz11FMeL6QgtFVO3xMZMWIEOTk5vPrqq8yfP5+goCAKCgrw\n9vZmzpw5JCQkNEY5BaFNcumm5dSpU3n00Uc5ceIEBQUFBAUF0a9fPwICAjxdPkFo01zuVRAQEMCQ\nIUM8WRZBEKoRA7YFQcNEgAqChokAFQQNEwEqCBrm1tCTbdu2sWfPHvLy8khKSiI1NZXc3FweeeQR\nT5VPE1RVRSkvd/p1sq+vePqb4BaXA3TlypX88MMPTJ06lbfeeguATp06sXz58lYXoKgq/7vuE6xO\nBKnO15c7XpgJIkAFN7h8ibtjxw7Wrl3L2LFj7Vnitttu4/fff/dY4bRCkmVCBg4AVUWS5Xp/UFVC\n4gZU/F8Q3ODyJ8hkMhEUFARgD9Dy8nJ8fX09UzKNCRk4AH2HDg3aVt+hAyEDBjRyiYS2wOUAjYuL\nY/Xq1VWWffrppwwePNjtQmmRpNMRfFcM6s3+x46oVivB/WKQdLomKpnQmrkcoIsWLSIlJYUhQ4ZQ\nUlLCsGHDOHDgAH/5y188WT5NaUgW9UT2VBTVrdcLrYfLjUShoaF88cUXpKWlkZmZSUREBNHR0cit\nuN5ly6K5//1jrRnSE9nTqqh8mnSap0ZHoZNFA1Nb51SApqSk1Lq8ffv2FBUVcejQIaB1P34wZOAA\n8k+ewlpSUmOdJ7Ln0fRscvPLOJqezaDoCLf2JbR8TgXo4sWL692mtT9+0FEW9VT2PHk+F71e5uT5\nXOL6hIss2sY5FaDikYMVasuinsqehhIjOlnGUGIUWVTwTFe/vLw8+wzzbUH1Fl1PZc8T53LQ3azD\n6+SKLGoVDUZtmssBWlxczCuvvEJUVBSDBw+mb9++LFiwgMLCQk+WT7Mqt+jqO3QgODYWa1mZ0z+2\nL7YjaVmc+jW3yhedLYsKbZfLrbgLFy7EZDKxfft2IiMjyczM5IMPPuC1115jxYoVniyjJtmyaE7y\nfxPaLwZJlrnoYndAqwr7f8rAaFa4biinY5BfxXpZ1EXbOpcz6OHDh/mP//gPevXqRfv27enVqxfv\nvvsuhw8f9mT5NC1k4AB8O4YSMmCAW90Bj6RlkXm9GFmWKCgsF1lUsHM5QDt37kx+fn6VZQUFBXTu\n3NntQrUUkk5Ht6em2uuernQHtCoq+3/KwGqtCEqztSKL2oi6aNvm8iXu6NGjmT59Ok8++STh4eFk\nZ2ezefNmxo4dW+V+aWu+JwpU6RBfX0cGm8qNSkdOZZJ5vdjen1mSKrJoaOAfQ9VEi27b5XKAbtmy\nBYD169fXuhxa/z3R2tTVkcGmtuxZedyoLYuKuqjgcoCKe6J/qD6gO/Cfe3P9H4ccdgcMiRuAYjJx\n5FxelexpI7KoYONUgB4+fJhBgwYBjrv9Qeu/rK2h+oBuRaXktwxUi6XGppJejyTryDvxM3v1MVgt\naq2zLpgtCtfzy7jlZhaVkEg9c427+4QjiyzaZjgVoMuWLWPPnj2A425/bfGy1taCa697yuATEkJ5\nTg6S9EcdVVUVfEJCQILggf0J/92XDu18HO63nZ+ep0dHVQlIMUFD2+JUgNqCE8QlbnXV657et4Rg\nys9HtfwxflT28sL7lhB0AQFc9O9CSXkWOp3jhvSScjM/X8gVl7VtWOsdG9bEagzoliS8g4NRVQWo\nyJ7ewcGoikJgTF9OXrhh79bniLjFIrgcoLNmzeLYsWNVlqWmpjJ79my3C9VSVb8P6n1LCLKXV8Uv\nN7OnvkMHLvjfiqHE2KB9io4KbZvLAZqamspdd91VZVm/fv346aef3C5US+Uoi1oVhe/b98VqVQi8\nqy+pZ3ORkFAUtd4fW+OQmGWhbXL5NouXlxcmkwm9/o9dGI3GVj2jQkPUVhc9VeRNoVc7zul86NE/\nlmf6Od/SIxqH2ia3Jg177733sNy8lWC1Wvnb3/7GwIEDPVa4pqSqqlujUWyqZ1EFictB3dBJcMkv\ngk/3/YK3lw4/H71TP2IC7LbJ5Qy6YMECZsyYQXx8PBEREWRlZREZGclHH33kyfI1HScmp1ZUkKXa\nJ6dWFLVKFk0v96dU1aHT68i2+JCVkS86HLRQiqI2+T1olzNop06d2L59O2vXruXZZ5/l448/Zvv2\n7YSFhXmyfE2moaNRVElmb3FoRSBWm5zaqqh8knQaRZIJvisGq8XKeaMfEipewcEYSs0Ulpg4cS5H\ntMy2MLa/bVP/3dyqMMqyzF133cWIESOIiYlp8fXPhoxGSS/3J9+q45wutMYg7SMnMsi5XsxPJzII\njI7iLMGUWGRknY5CyQez2YrZYuVSlkG0zLYwlSdza0ouX+IWFRWxYcMG0tPTKS0trbJu48aNbhes\nOdQ3GkVR4bzRDx3wm38EChKXbl4WKyocLAylTNWRfOk8HdpfJz03AKvRiOrjQ055LlZVQpIkDHod\nJ87liM7vLURzTubmcoDOnz8fg8FAQkJCq3rcQ12jUdLL/SlVKuqTxnaBpP5yjR43u/idMbWnVNUj\nS1Cq6vm25BbKvfVI5hKK9O0oNuvwkxVkH2/MVsWeRUVdVPuaczI3lwP02LFjpKSk4Ofn58nyNDtH\nWdSWPSVUvEOC0el0nDyfy8Axsdw4cYrzWb7YvlQl4Jdyf8K9zMgB7bhh8kJBwoyMl7cPSGAoNnH8\n7DWRRTXOlj2rT+bWVH83lyuNXbt2bbUThNVWF7VlT1mvxzskBKjo5ZN6NoffOvagxPrHW2mw6ihX\nZAxWHQVWPUal4g9plf/4PjRbFH5Kv8qRtKwmOCPBVbbsWVlT9u5ya0aFmTNnMmXKFEJDQ6usa+nD\nzapn0erZ03ZbRSfLnDiXi6oEove6imq1oqpQqOiQ5Yp/jYqMigQSqJKE0WzFx1uHyaJgNFv59shv\n3BMdIbKoBlXPnjZNmUVdDtDPPvsMoMYMfq1luFmNe5k365627GlzKcsAqAQFB1Oem4vBqr/ZGARl\nVhkjEjK2qVEkjGYLPl46TGYrqqLyv1kGjqRlER8T2RynKdShct2zuqaqi4oZFRywZdFrB3+sNXsC\nqCoYSkygqnS8NRhjfj4Gs96+zoSMQkWdVJJkFFXBalEpKjWi3FxRbhRZVIscZU+bpsqiHp9RQZIk\n4uPj3S+ZBoQMHMDBlLMOs+cNQxnmm+M9rxca6RAczK3mXJAkDFYdJYqMDgkfLxm/dt4YTRbKTVYs\nVoWgAN+KyAUyrxeLLNrMqvcSqit72jRFFhUzKtRBkWQy/MOR8q7hHRJaI3vmFxntfWQLCssJvTUY\nc34+isXKDasXUHGpa5F0KIqKyaKiqmBVwGi24OtdMRRNsap8ffg3BkVHiOlMmkjlgKz+yMf6sqdN\nU2RRl2ebEESpAAAK6UlEQVRU2Lp1KyEhIeha8ZOkj6ZnY2wXiM6nwGH2tAWo2apwvdBIYHAwZTm5\neEsKOllF5+OD5K3Hx0uHBJSUW5AlBVmWufO2IKSbabSdnx5FVZERAdrYqgdk9Uc+NiR72jR2FnXp\nNouqqgwfPtzTZdEURVFJPXMNSZLxu70bikqVcZo3DGWolZapKuQZytAFB1Eo+dh7Dcne3oBEfqER\ns1VBVVUkSabcZCW/sBydTkKnkygpN5N65mpzn3abUDkgq/cSMluUir+7RsbrutRIJEkS3bt35+rV\nq0RGts56kyTBM+Oia12nqipGk7XWdXq9zJpPCgjIzMG3U0e8Q4K5UVBGrqpSUm7BVvFUFZXsG6WE\nBvkjSWLu26ZSPSAVlSq9hI6duerw716XxhoN6HIr7iOPPMKMGTNITEwkPDy8ynjFln4fFCq+hPx8\nHL89/r5etS4/nJaFtX0Q3r4G/EJvQaWipddsVW+OHf1jBvkyo4XrBaV0DPYHxNy3TaHy5WtBcTn7\nj/xGgL83cPNL8tdc4qK08yXpcoBu3rwZgDVr1lRZ3poaiZxV22XxjYIyTBYrJrMVqtUvVUUlK7eE\njkH+ILJoo6ve+JNfZCK/sJw7u3jbM6DWviTFfVAPqn5ZrNwcQ1hROymrdVYEnSyhotobi7T2AWlN\nKmdPVVUpKCzHYlW4YSgjVKOP2XC6kejSpUtMnjyZ/v37k5iYSGZmZmOUq0WyXRbbfn6+kEtJmZmC\nIiOyVBGC1X+s1ooZ5MUkYY2reva8bijHbFWQJIn8IiOVZ67R0kyKTmfQZcuWERkZycyZM9m9ezfv\nvPMOH374YWOUrUWzfyB0Mt1vDapzWzGDfOOrLXvab5FZrJrNok4H6OnTpzlw4AB+fn7ExsYyYsSI\nxihXi1f5A6HT1f1HFjPIN666sidgz6K3BPppri7q9CWuyWSyjwENCAjAZDJ5vFAtnb2xSCP30tq6\nykPGqmdPG1sWtdHKrP5OZ1CLxcLWrVvtv5tMpiq/A0yaNMn9krVgdd1Dre91gmfVlz1ttJpFnQ7Q\nmJiYKl3+oqKiqvwuSZII0HruoQpNp3rdM89QDkiotSRGk9nK9YJSbgnUziMfnf4U2caBCoLWVa9q\nqKrKP0XUPWujr4+e58b31UyDnfiaF1otV6savt46zczkLwJUaLVaQ1WjZc80LQitnAhQQdAwEaCC\noGEiQAVBw0SACoKGiQAVBA0TASoIGiYCVBA0TASoIGiYCFBB0DARoIKgYSJABUHDRIAKgoaJABUE\nDRMBKggaJgJUEDRMBKggaJgIUEHQMBGggqBhIkAFQcNEgAqChokAFQQNEwEqCBomAlQQNEwEqCBo\nmAhQQdAwEaCCoGEiQAVBw5r1yTK5ublYLBaGDRvWnMUQhCaXnZ2NTqerd7tmDVBvb2/U2p6kKgit\nnF6vx9vbu97tJFVEiCBolqiDCoKGiQAVBA0TASoIGiYCVBA0TASoIGiYCFBB0DARoC4aNWoUhw8f\nbtL9DB06lJSUFLePKbQcIkBvSkxMpG/fvvTv35977rmH6dOnc+nSJYfb79mzh0GDBrl9XE/tpyG+\n/vprJk2aRL9+/YiPj2fKlCkcOHCgSY6tFQ35ktu0aROPPvoo0dHRvPTSS01UstqJAK1k0aJFHD9+\nnAMHDhAYGMirr75aY5uW2q/j73//O0uXLuXpp5/mH//4BykpKTz//PN8//33zV00zQkLC+P5559n\n0qRJzV0UEaC18fPzY8yYMZw/fx6o+NZdt24dEyZM4K677iI3N7fKN/HQoUP59NNPmTBhArGxsTz3\n3HMUFRXZ95eTk8O8efO49957ufvuu5k1a5Z9XfX9fPzxx4wePZqBAwcye/ZsiouLay3j9evXmTt3\nLvHx8TzwwAOsXLnS4fkUFxfz/vvvs2TJEh5++GH8/f2RJIl77rmHt99+G4DffvuNp59+mri4OBIS\nEti+fXuVfQwdOpT169czYcIE+vXrx+zZsyksLOSVV14hNjaWMWPGcOHChSrb13UudR2vvvezrnOv\n67UvvfQS2dnZzJo1i/79+/P+++/X+n4NHz6cYcOGERQU5PA9ddaJEydcep0I0FoUFxeze/du+vTp\nY1+2e/duPvzwQ44fP05ISEiN1yQlJfHRRx+RkpKCwWBgw4YNACiKwowZMwgODmb//v384x//YNq0\naQ6PvXPnTtasWcPBgwcxmUy89dZbNbZRVZWZM2fSrVs3kpOT+eKLL/jhhx/Ytm1brfs8ceIEZrOZ\nBx98sNb1FouF5557jv79+3Po0CH++te/8te//rVG3Xjfvn2sXbuW5ORkzp07x2OPPcaYMWM4duwY\ncXFxLF++vEHn0pDjOXo/G3Lujl773nvvER4ezqpVqzh+/Dhz5sxx+HfwtPLyck6ePOn060SAVvLO\nO+9w9913M2LECMxmM++88459XWJiIpGRkeh0ulpHIUyZMoWwsDD8/PxISEggPT0dgJ9//pnMzExe\nffVV/P390ev1DBgwwGEZnnzySSIjI/H392fOnDns3bu3xjZpaWlcvXqVF198ES8vLzp27MjUqVPZ\ns2dPrfssKCggKCjI4eiJU6dOUVhYyAsvvIBerycqKoqJEyeya9euKtslJibSsWNHAgMDGTJkCF26\ndCE+Ph5Jkhg5ciRnzpyp81z27dsHwMmTJ+s9nqP309G5V36fHL3WpjmqKYMGDSI9Pd3hFZEjzTqa\nRWsWLFjgsN4RHh5e52tDQ0Pt//f19aW0tBSoGFYUERGBXt+wt7rycSIjI7FYLOTl5VXJ2pmZmeTl\n5REXFwdUfOBUVXVYxqCgIAoKCrBarbUG6bVr1wgPD0eSpCrHTktLc3iOfn5+NX63nXN955KTk1Pv\n8Ry9nw05d0evbUxZWVl8+eWXdW6jqiqPP/44y5YtIzo6ukH7FQHaQJU/TM6IiIggKyvLYXBUl52d\nbf9/ZmYmXl5eBAcH19hn586dG9zA069fP7y9vfmv//ovEhISaqwPCwvj6tWrqKpqP8/MzEzCwsIa\ntH9Hqp+LXq8nODjYreM5e+7Vufp3rE9ERESVtoXarFixgokTJxIVFdXg/YpL3EYWHR1NZGQky5cv\np6SkBIvFQmpqqsPtN2/ezJUrVyguLuaDDz5g5MiRNT5U0dHRhISEsHr1asrKylBVlYyMDIf7DQgI\nYM6cObz11lt8++23lJaWoigKR48eZfHixcTExNC+fXtWrVqF2WwmPT2d7du3M27cOKfOtfqlo6Nz\nced4zp57daGhoVy+fLnObaxWK0ajEYvFgtVqxWQyYbFYGrR/RzIyMtDpdCQmJjr1JSEC9Ka63rTa\n1kmSZF9e12tlWeajjz4iJyeH4cOHEx8fz2effeZw32PHjmXmzJk88MADeHl5sXDhwhrbyrLMmjVr\nyMjI4KGHHiIuLo65c+eSm5vrsBxTpkzh9ddf55NPPiE+Pp4hQ4awatUqhg0bhl6vZ82aNRw7dozB\ngwczb9485s2bV+X+bEM+VA09l/qOV9/7Wde511fOZ599lnXr1hEXF8eKFStq3eajjz4iJiaGtWvX\n8u233xITE8Prr79e7/nXJTk5mSeeeMLp14kB2xoydOhQli5dyr333tvcRXFbazqX5iQyqCBomAhQ\nDWmsBozm0JrOpTmJS1xB0DCRQQVBw0SACoKGiQAVBA0TASoIGiYCVBA0TASoIGiYCFBB0DARoIKg\nYSJABUHDRIAKgob9H5x/ZelEozQJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd8681b3940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = clone(clf)  # reset model\n",
    "\n",
    "additional_benchmarks = [\n",
    "    \"shoc-1.1.5-FFT-ifft1D_512\",\n",
    "    \"shoc-1.1.5-S3D-ratt6_kernel\",\n",
    "    \"shoc-1.1.5-S3D-ratx2_kernel\",\n",
    "    \"shoc-1.1.5-S3D-ratt9_kernel\",\n",
    "]\n",
    "\n",
    "additional = nvidia_benchmarks[nvidia_benchmarks[\"benchmark\"].str.contains(\n",
    "    \"|\".join(additional_benchmarks))]\n",
    "parboil_and_additional = nvidia_benchmarks[nvidia_benchmarks[\"benchmark\"].str.contains(\n",
    "    r\"^{}|{}\".format(\"parboil\", \"|\".join(additional_benchmarks)))]\n",
    "\n",
    "B_out = cgo13.xval_benchmarks(clf, parboil_and_additional, prefix=\"parboil\")\n",
    "X = pca.transform(cgo13.raw_features(parboil))\n",
    "\n",
    "ax = plot_pca(X, B_out, additional, pca)\n",
    "\n",
    "ax.set_xlim(xlim)  # use same axis as before\n",
    "ax.set_ylim(ylim)\n",
    "\n",
    "viz.finalise(figsize=(2.5, 2.5), tight=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The addition of these observations (and the information they provide about that\n",
    "part of the feature space) causes the two outliers to be correctly optimized. We\n",
    "found such outliers in all of the benchmark suites.\n",
    "\n",
    "These results highlight the significant affect that the number and distribution\n",
    "of training programs has on the quality of predictive models. Without good\n",
    "coverage of the feature space, any machine learning methodology is unlikely to\n",
    "produce high quality heuristics, suitable for general use on arbitrary real\n",
    "applications. Our novel approach, described in the next section, solves this\n",
    "problem by generating an unbounded number of programs to cover the feature space\n",
    "at any desired granularity.\n",
    "\n",
    "# 3. Overview of Our Approach\n",
    "\n",
    "In this paper we present CLgen, a tool for synthesizing OpenCL benchmarks, and\n",
    "an accompanying host driver for executing synthetic benchmarks for gathering\n",
    "performance data for predictive modeling. While we demonstrate our approach\n",
    "using OpenCL, it is language agnostic. Our tool CLgen learns the semantics and\n",
    "structure from over a million lines of hand-written code from GitHub, and\n",
    "synthesizes programs through a process of iterative model sampling. We then use\n",
    "a host driver to execute the synthesized programs to gather performance data for\n",
    "use in predictive modeling. Our approach extends the state of the art by\n",
    "providing a general-purpose solution for benchmark synthesis, leading to better\n",
    "and more accurate predictive models.\n",
    "\n",
    "In the course of evaluating our technique against prior work we discovered that\n",
    "it is also useful for evaluating the quality of features. Since we are able to\n",
    "cover the space so much more finely than the prior work, which only used\n",
    "standard benchmark suites, we are able to find multiple programs with identical\n",
    "feature values but different best heuristic values. This indicates that the\n",
    "features are not sufficiently discriminative and should be extended with more\n",
    "information to allow those programs to be separated. We go on to show that doing\n",
    "this significantly increases the performance of the learned heuristics. We\n",
    "expect that our technique will be valuable for feature designers.\n",
    "\n",
    "# 4. CLgen: Benchmark Synthesis\n",
    "\n",
    "CLgen is an undirected, general-purpose program synthesizer for OpenCL. It\n",
    "adopts and augments recent advanced techniques from deep learning to learn over\n",
    "massive codebases. In contrast to existing grammar and template based\n",
    "approaches, CLgen is entirely probabilistic. It *learns* to program using neural\n",
    "networks which model the semantics and usage of a huge corpus of code fragments\n",
    "in the target programming language. This section describes the assembly of an\n",
    "OpenCL language corpus, the application of deep learning over this corpus, and\n",
    "the process of synthesizing programs.\n",
    "\n",
    "## 4.1. An OpenCL Language Corpus\n",
    "\n",
    "Deep learning requires large datasets. For the purpose of modeling a programming\n",
    "language, this means assembling a very large collection of real, hand-written\n",
    "source codes. We assembled OpenCL codes by mining public repositories on the\n",
    "popular code hosting site GitHub.\n",
    "\n",
    "This is itself a challenging task since OpenCL is an embedded language, meaning\n",
    "device code is often difficult to untangle and GitHub does not presently\n",
    "recognize it as a searchable programming language. We developed a search engine\n",
    "for the GitHub API which attempts to identify and download standalone OpenCL\n",
    "files through a process of file scraping and recursive header inlining.\n",
    "\n",
    "The dataset we mined (from June 2016):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# content files: 8078\n",
      "line count:      2852707\n"
     ]
    }
   ],
   "source": [
    "import clgen.dbutil\n",
    "\n",
    "data = \"../data/github.db\"\n",
    "print(\"# content files:\", clgen.dbutil.num_rows_in(data, \"ContentFiles\"))\n",
    "print(\"line count:     \", clgen.dbutil.lc(data, \"ContentFiles\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Randomly selected file *(replay cell to select a new file)*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "//pass\n",
      "//--local_size=16 --num_groups=1 --no-inline\n",
      "\n",
      "__kernel void foo(__global int* A) {\n",
      "  __assert(__enabled());\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "db = clgen.dbutil.connect(\"../data/github.db\").cursor()\n",
    "db.execute(\"SELECT Contents FROM ContentFiles ORDER BY RANDOM() LIMIT 1\")\n",
    "print(db.fetchone()[0])\n",
    "db.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We prune the raw dataset extracted from GitHub using a custom toolchain we\n",
    "developed for rejection filtering and code rewriting, built on LLVM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# To replicate the mining of GitHub for OpenCL files:\n",
    "#\n",
    "# Generate an API token by following the instructions here:\n",
    "#   https://help.github.com/articles/creating-an-access-token-for-command-line-use/>\n",
    "# \n",
    "# From the command line, run:\n",
    "#\n",
    "#   $ export GITHUB_USERNAME=[your-username]\n",
    "#   $ export GITHUB_PW=[your-password]\n",
    "#   $ export GITHUB_TOKEN=[your-api-token]\n",
    "#   $ clgen-create-db --github github.db\n",
    "#   $ clgen-fetch-github github.db\n",
    "#\n",
    "# This process issues thousands of GitHub API requests per minute over the course of several hours.\n",
    "# Please exercise restrained in minimizing your use of this program -- we don't want to upset\n",
    "# the nice folks at GH :-)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Rejection Filter\n",
    "\n",
    "The rejection filter accepts as input a content file and returns whether or not\n",
    "it contains compilable, executable OpenCL code. To do this we attempt to compile\n",
    "the input to NVIDIA PTX bytecode and perform static analysis to ensure a minimum\n",
    "static instruction count of three. We discard any inputs which do not compile or\n",
    "contain fewer than three instructions.\n",
    "\n",
    "Bytecode features of an example program:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'And_insts': 1,\n",
       " 'Br_insts': 2,\n",
       " 'Call_insts': 1,\n",
       " 'FAdd_insts': 1,\n",
       " 'FMul_insts': 1,\n",
       " 'GetElementPtr_insts': 2,\n",
       " 'ICmp_insts': 1,\n",
       " 'Load_insts': 2,\n",
       " 'Ret_insts': 1,\n",
       " 'Store_insts': 1,\n",
       " 'Trunc_insts': 1,\n",
       " 'basic_blocks': 3,\n",
       " 'instructions_of_all_types': 14,\n",
       " 'memory_instructions': 6,\n",
       " 'non_external_functions': 1,\n",
       " 'ratio_And_insts': 0.07142857142857142,\n",
       " 'ratio_Br_insts': 0.14285714285714285,\n",
       " 'ratio_Call_insts': 0.07142857142857142,\n",
       " 'ratio_FAdd_insts': 0.07142857142857142,\n",
       " 'ratio_FMul_insts': 0.07142857142857142,\n",
       " 'ratio_GetElementPtr_insts': 0.14285714285714285,\n",
       " 'ratio_ICmp_insts': 0.07142857142857142,\n",
       " 'ratio_Load_insts': 0.14285714285714285,\n",
       " 'ratio_Ret_insts': 0.07142857142857142,\n",
       " 'ratio_Store_insts': 0.07142857142857142,\n",
       " 'ratio_Trunc_insts': 0.07142857142857142,\n",
       " 'ratio_basic_blocks': 0.21428571428571427,\n",
       " 'ratio_memory_instructions': 0.42857142857142855,\n",
       " 'ratio_non_external_functions': 0.07142857142857142}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bc = clgen.preprocess.compile_cl_bytecode(\"\"\"\n",
    "#define DTYPE float\n",
    "#define ALPHA(a) 3.5f * a\n",
    "inline DTYPE ax(DTYPE x) { return ALPHA(x); }\n",
    "\n",
    "__kernel void saxpy( /* SAXPY kernel */\n",
    "    __global DTYPE *input1,\n",
    "    __global DTYPE *input2,\n",
    "    const int nelem)\n",
    "{\n",
    "    unsigned int idx = get_global_id(0);\n",
    "    // = ax + y\n",
    "    if (idx < nelem) {\n",
    "        input2[idx] += ax(input1[idx]); }}\n",
    "\"\"\")\n",
    "        \n",
    "# extract features\n",
    "clgen.preprocess.bytecode_features(bc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "During initial development it became apparent that isolating the OpenCL device\n",
    "code leads to a higher-than-expected discard rate (that is, seemingly valid\n",
    "OpenCL files being rejected). Through analyzing 148k lines of compilation\n",
    "errors, we discovered a large number of failures caused by undeclared\n",
    "identifiers — a result of isolating device code — 50% of undeclared identifier\n",
    "errors in the GitHub dataset were caused by only 60 unique identifiers. To\n",
    "address this, we developed a *shim header* which contains inferred values for\n",
    "common type definitions (e.g. `FLOAT_T`), and common constants (e.g. `WGSIZE`).\n",
    "\n",
    "**Listing 1:** The shim header file, providing inferred type aliases and constants for OpenCL on GitHub."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "// Enable OpenCL features and implementation.\n",
      "#ifndef CLGEN_FEATURES\n",
      "#define cl_clang_storage_class_specifiers\n",
      "#define cl_khr_fp64\n",
      "#include <clc/clc.h>\n",
      "#pragma OPENCL EXTENSION cl_khr_fp64 : enable\n",
      "\n",
      "#define __CL_VERSION_1_0__\n",
      "#define __CL_VERSION_1_1__\n",
      "#define __CL_VERSION_1_2__\n",
      "#define __ENDIAN_LITTLE__\n",
      "#define __FAST_RELAXED_MATH__\n",
      "#define __IMAGE_SUPPORT__\n",
      "#define __OPENCL_VERSION__ 1\n",
      "#endif  /* CLGEN_FEATURES */\n",
      "\n",
      "// Unsupported OpenCL storage classes and qualifiers.\n",
      "#define static\n",
      "#define generic\n",
      "#define AS\n",
      "\n",
      "// Common typedefs\n",
      "typedef float CONVT;\n",
      "typedef float DATA_TYPE;\n",
      "typedef float DATATYPE;\n",
      "typedef float FLOAT_T;\n",
      "typedef float FLOAT_TYPE;\n",
      "typedef float FPTYPE;\n",
      "typedef float hmc_float;\n",
      "typedef float inType;\n",
      "typedef float outType;\n",
      "typedef float real;\n",
      "typedef float REAL;\n",
      "/* typedef float T; */\n",
      "typedef float Ty;\n",
      "typedef float TyOut;\n",
      "typedef float TYPE;\n",
      "typedef float VALTYPE;\n",
      "typedef float VALUE_TYPE;\n",
      "typedef float VECTYPE;\n",
      "typedef float WORKTYPE;\n",
      "typedef float2 hmc_complex;\n",
      "typedef float2 mixed2;\n",
      "typedef float2 real2;\n",
      "typedef float2 REAL2;\n",
      "typedef float3 mixed3;\n",
      "typedef float3 real3;\n",
      "typedef float3 REAL3;\n",
      "typedef float4 FPVECTYPE;\n",
      "typedef float4 mixed4;\n",
      "typedef float4 real4;\n",
      "typedef float4 REAL4;\n",
      "typedef float4 T4;\n",
      "typedef int BITMAP_INDEX_TYPE;\n",
      "typedef int INDEX_TYPE;\n",
      "typedef int Ix;\n",
      "typedef int KParam;\n",
      "typedef int Tp;\n",
      "typedef int3 Pixel;\n",
      "typedef unsigned int uint32_t;\n",
      "\n",
      "// Common constants\n",
      "#define ACCESSES 16\n",
      "#define AVER 2\n",
      "#define BETA 0.5\n",
      "#define BINS_PER_BLOCK 8\n",
      "#define BITMAP_SIZE 1024\n",
      "#define BLK_X 8\n",
      "#define BLK_Y 8\n",
      "#define BLOCK 32\n",
      "#define BLOCK_DIM 2\n",
      "#define BLOCK_SIZE 64\n",
      "#define BLOCK_SIZE_WITH_PAD 64\n",
      "#define BLOCK_X 8\n",
      "#define BLOCK_Y 8\n",
      "#define BLOCKSNUM 64\n",
      "#define BUCKETS 8\n",
      "#define CHARS 16\n",
      "#define CLASS 'A'  // Used in npb-3.3\n",
      "#define COLS 64\n",
      "#define COLUMNS_BLOCKDIM_X 16\n",
      "#define COLUMNS_BLOCKDIM_Y 8\n",
      "#define COLUMNS_HALO_STEPS 1\n",
      "#define COLUMNS_RESULT_STEPS 4\n",
      "#define CONCURRENT_THREADS 128\n",
      "#define CUTOFF_VAL 0.5\n",
      "#define DEF_DIM 3\n",
      "#define DIAMETER 16\n",
      "#define DIM 3\n",
      "#define DIMX 128\n",
      "#define DIMY 64\n",
      "#define DIMZ 8\n",
      "#define DIRECTIONS 4\n",
      "#define DISPATCH_SIZE 64\n",
      "#define ELEMENTS 1024\n",
      "#define EPSILON 0.5\n",
      "#define EUCLID 1\n",
      "#define EXPRESSION\n",
      "#define EXTRA 4\n",
      "#define FILTER_LENGTH 128\n",
      "#define FLEN 100\n",
      "#define FOCALLENGTH 100\n",
      "#define FORCE_WORK_GROUP_SIZE 32\n",
      "#define GAUSS_RADIUS 5\n",
      "#define GLOBALSIZE_LOG2 10\n",
      "#define GROUP 128\n",
      "#define GROUPSIZE 128\n",
      "#define HEIGHT 128\n",
      "#define HISTOGRAM64_WORKGROUP_SIZE 64\n",
      "#define IMAGEH 512\n",
      "#define IMAGEW 1024\n",
      "#define INITLUT 0\n",
      "#define INITSTEP 0\n",
      "#define INPUT_WIDTH 256\n",
      "#define INVERSE -1\n",
      "#define ITERATIONS 1000\n",
      "#define KAPPA 4\n",
      "#define KERNEL_RADIUS 8\n",
      "#define KEY 8\n",
      "#define KITERSNUM 64\n",
      "#define KVERSION 1\n",
      "#define LAMBDA .5\n",
      "#define LENGTH 1024\n",
      "#define LIGHTBUFFERDIM 128\n",
      "#define LOCAL_H 8\n",
      "#define LOCAL_MEM_SIZE 2048\n",
      "#define LOCAL_MEMORY_BANKS 16\n",
      "#define LOCAL_SIZE 128\n",
      "#define LOCAL_SIZE_LIMIT 1024\n",
      "#define LOCAL_W 128\n",
      "#define LOCALSIZE_LOG2 5\n",
      "#define LOG2_WARP_SIZE 5\n",
      "#define LOOKUP_GAP 16\n",
      "#define LROWS 16\n",
      "#define LSIZE 64  // Used in npb-3.3\n",
      "#define LUTSIZE 1024\n",
      "#define LUTSIZE_LOG2 10\n",
      "#define LWS 128\n",
      "#define M_PI 3.14\n",
      "#define MASS 100\n",
      "#define MAX 100\n",
      "#define MAX_PARTITION_SIZE 1024\n",
      "#define MAXWORKX 8\n",
      "#define MAXWORKY 8\n",
      "#define MERGE_WORKGROUP_SIZE 32\n",
      "#define MOD 16\n",
      "#define MULT 4\n",
      "#define N_CELL_ENTRIES 128\n",
      "#define N_GP 8\n",
      "#define N_PER_THREAD 16\n",
      "#define NCHAINLENGTH 100\n",
      "#define NDIM 4\n",
      "#define NEEDMEAN 1\n",
      "#define NMIXTURES\n",
      "#define NROUNDS 100\n",
      "#define NSIEVESIZE 64\n",
      "#define NSPACE 512\n",
      "#define NSPIN 8\n",
      "#define NTIME 100\n",
      "#define NUM_OF_THREADS 1024\n",
      "#define NUMBER_THREADS 32\n",
      "#define OFFSET 2\n",
      "#define ONE 1\n",
      "#define OP_WARPSIZE 32\n",
      "#define PADDING 8\n",
      "#define PADDINGX 4\n",
      "#define PADDINGY 2\n",
      "#define PI 3.14\n",
      "#define PRESCAN_THREADS 128  /* Used in parboil-0.2 histo */\n",
      "#define PULSELOCALOFFSET 8\n",
      "#define PULSEOFF 16\n",
      "#define QPEX 1\n",
      "#define QUEUE_SIZE 128\n",
      "#define RADIUS 8\n",
      "#define RADIUSX 16\n",
      "#define RADIUSY 16\n",
      "#define RADIX 2\n",
      "#define REGION_WIDTH 16\n",
      "#define RESULT_SIZE 512\n",
      "#define ROWS_BLOCKDIM_X 16\n",
      "#define ROWS_BLOCKDIM_Y 4\n",
      "#define ROWS_HALO_STEPS 1\n",
      "#define ROWS_RESULT_STEPS 4\n",
      "#define ROWSIZE 128\n",
      "#define SAT .5\n",
      "#define SCALE\n",
      "#define SCREENHEIGHT 1920\n",
      "#define SCREENWIDTH 1080\n",
      "#define SHAREMASK 0\n",
      "#define SIGN 1\n",
      "#define SIMD_WIDTH 32\n",
      "#define SIMDROWSIZE\n",
      "#define SINGLE_PRECISION 1\n",
      "#define SIZE 1024\n",
      "#define SLICE 32\n",
      "#define STACK_SIZE 1024\n",
      "#define STEP 8\n",
      "#define SUBWAVE_SIZE 32\n",
      "#define TDIR 1\n",
      "#define THREADBUNCH 32\n",
      "#define THREADS 2048\n",
      "#define THREADS_H 16\n",
      "#define THREADS_W 128\n",
      "#define THREADS_X 128\n",
      "#define THREADS_Y 16\n",
      "#define THRESHOLD 0.5\n",
      "#define TILE_COLS 16\n",
      "#define TILE_COLS 16\n",
      "#define TILE_DIM 16\n",
      "#define TILE_HEIGHT 16\n",
      "#define TILE_M 16\n",
      "#define TILE_N 16\n",
      "#define TILE_ROWS 16\n",
      "#define TILE_SIZE 16\n",
      "#define TILE_TB_HEIGHT 16\n",
      "#define TILE_WIDTH 16\n",
      "#define TILEH 16\n",
      "#define TILESH 64\n",
      "#define TILESW 16\n",
      "#define TILEW 16\n",
      "#define TRANSPOSEX 1\n",
      "#define TRANSPOSEY -1\n",
      "#define TREE_DEPTH 3\n",
      "#define TX 8\n",
      "#define TY 16\n",
      "#define UNROLL 8\n",
      "#define VECSIZE 128\n",
      "#define VOLSPACE 4\n",
      "#define WARP_COUNT 8\n",
      "#define WARPS_PER_GROUP 8\n",
      "#define WDEPTH 16\n",
      "#define WDIM 2\n",
      "#define WG_H 8\n",
      "#define WG_SIZE 128\n",
      "#define WG_SIZE_X 32\n",
      "#define WG_SIZE_Y 8\n",
      "#define WG_W 32\n",
      "#define WIDTH 16\n",
      "#define WINDOW 16\n",
      "#define WORK_GROUP_SIZE 256\n",
      "#define WORK_ITEMS 128\n",
      "#define WORKGROUP_SIZE 256\n",
      "#define WORKGROUPSIZE 256\n",
      "#define WORKSIZE 128\n",
      "#define WSIZE 1024\n",
      "#define XDIR 0\n",
      "#define XSIZE 128\n",
      "#define YDIR 1\n",
      "#define YSIZE 64\n",
      "#define ZDIR 2\n",
      "#define ZERO 0\n",
      "#define ZSIZE 128\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import clgen.native\n",
    "print(readfile(clgen.native.SHIMFILE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Code fails to compile without shim header\n"
     ]
    }
   ],
   "source": [
    "# without shim:\n",
    "try:\n",
    "    clgen.preprocess.preprocess(\"\"\"\n",
    "        __kernel void foo(__global float* a) {\n",
    "            a[get_global_id(0)] = ZERO;\n",
    "        }\n",
    "    \"\"\", use_shim=False)\n",
    "except clgen.preprocess.BadCodeException:\n",
    "    print(\"Code fails to compile without shim header\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__kernel void A(__global float* a) {\n",
      "  a[get_global_id(0)] = 0;\n",
      "}\n",
      "Code compiles with shim header\n"
     ]
    }
   ],
   "source": [
    "print(clgen.preprocess.preprocess(\"\"\"\n",
    "    __kernel void foo(__global float* a) {\n",
    "        a[get_global_id(0)] = ZERO;\n",
    "    }\n",
    "\"\"\", use_shim=True))\n",
    "print(\"Code compiles with shim header\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Injecting the shim decreases the discard rate from 40% to 32%, responsible for\n",
    "an additional 88k lines of code in the final language corpus. The resulting\n",
    "dataset is 2.0 million lines of compilable OpenCL source code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# files in final dataset: 5390\n",
      "# rejected files:         2688 (33%)\n",
      "line count of good files: 2014761\n"
     ]
    }
   ],
   "source": [
    "def linecount_of_good_files(data):\n",
    "    db = clgen.dbutil.connect(data).cursor()\n",
    "    db.execute(\"\"\"SELECT LC(ContentFiles.Contents) FROM ContentFiles\n",
    "               LEFT JOIN PreprocessedFiles ON ContentFiles.id=PreprocessedFiles.id\n",
    "               WHERE PreprocessedFiles.status=0\"\"\")\n",
    "    return db.fetchone()[0]\n",
    "\n",
    "data = \"../data/github.db\"\n",
    "\n",
    "# preprocess dataset\n",
    "# note results are cached. empty cache by uncommenting the next line (will take >30min):\n",
    "# clgen.dbutil.remove_preprocessed(data)\n",
    "clgen.preprocess.preprocess_db(data)\n",
    "\n",
    "num_good_files = clgen.dbutil.num_rows_in(data, \"PreprocessedFiles\", condition=\"WHERE status=0\")\n",
    "num_bad_files = clgen.dbutil.num_rows_in(data, \"PreprocessedFiles\", condition=\"WHERE status!=0\")\n",
    "ratio_bad = num_bad_files / (num_good_files + num_bad_files)\n",
    "\n",
    "print(\"# files in final dataset:\", num_good_files)\n",
    "print(\"# rejected files:        \", num_bad_files, \"({:.0f}%)\".format(ratio_bad * 100))\n",
    "print(\"line count of good files:\", linecount_of_good_files(data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code Rewriting\n",
    "\n",
    "Programming languages have few of the issues of semantic interpretation present\n",
    "in natural language, though there remains many sources of variance at the\n",
    "syntactic level. For example, the presence and content of comments in code, and\n",
    "the choice of identifying names given to variables. We consider these\n",
    "ambiguities to be *non-functional variance*, and developed a tool to normalize\n",
    "code of these variances so as to make the code more amenable to machine\n",
    "learning. This is a three step process:\n",
    "\n",
    "1.  The source is pre-processed to remove macros, conditional compilation, and\n",
    "    source comments.\n",
    "\n",
    "2.  Identifiers are rewritten to have a short but unique name based on their\n",
    "    order of appearance, using the sequential series $\\{a, b, c,\n",
    "    \\ldots, aa, ab, ac, \\ldots\\}$\n",
    "    for variables and $\\{A, B, C,\n",
    "    \\ldots, AA, AB, AC, \\ldots\\}$\n",
    "    for functions. This process isolates the syntactic structure of the code,\n",
    "    and unlike prior work, our rewrite method preserves program behavior.\n",
    "    Language built-ins (e.g. `get_global_id`, `asin`) are not rewritten.\n",
    "\n",
    "3.  A variant of the Google C++ code style is enforced to ensure consistent use\n",
    "    of braces, parentheses, and white space.\n",
    "    \n",
    "**Figure 5:**  The code rewriting process, which transforms code to make it more amenable to language modeling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inline float A(float a) {\n",
      "  return 3.5f * a;\n",
      "}\n",
      "\n",
      "__kernel void B(__global float* b, __global float* c, const int d) {\n",
      "  unsigned int e = get_global_id(0);\n",
      "\n",
      "  if (e < d) {\n",
      "    c[e] += A(b[e]);\n",
      "  }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "print(clgen.preprocess.preprocess(\"\"\"\n",
    "#define DTYPE float\n",
    "#define ALPHA(a) 3.5f * a\n",
    "inline DTYPE ax(DTYPE x) { return ALPHA(x); }\n",
    "\n",
    "__kernel void saxpy( /* SAXPY kernel */\n",
    "    __global DTYPE *input1,\n",
    "    __global DTYPE *input2,\n",
    "    const int nelem)\n",
    "{\n",
    "    unsigned int idx = get_global_id(0);\n",
    "    // = ax + y\n",
    "    if (idx < nelem) {\n",
    "        input2[idx] += ax(input1[idx]); }}\n",
    "\"\"\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Randomly selected preprocessed file from dataset *(replay cell to select a new file)*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__kernel void A(__global ushort8* a) {\n",
      "  typedef union {\n",
      "    ushort8 vec;\n",
      "    ushort array[8];\n",
      "  } vec_array;\n",
      "\n",
      "  uint b, c;\n",
      "  uint d = 1;\n",
      "  vec_array e, f, g;\n",
      "\n",
      "  g.vec = a[0];\n",
      "\n",
      "  for (int h = 0; h < 3; h++) {\n",
      "    c = 0;\n",
      "    b = 0;\n",
      "\n",
      "    for (int i = 0; i < 8; i++) {\n",
      "      if (g.array[i] & d)\n",
      "\n",
      "        f.array[b++] = g.array[i];\n",
      "      else {\n",
      "        e.array[c++] = i;\n",
      "      }\n",
      "    }\n",
      "\n",
      "    for (int i = c; i < 8; i++)\n",
      "      e.array[i] = 8 - c + i;\n",
      "    g.vec = shuffle2(g.vec, f.vec, e.vec);\n",
      "    d <<= 1;\n",
      "  }\n",
      "  a[0] = g.vec;\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "db = clgen.dbutil.connect(\"../data/github.db\").cursor()\n",
    "db.execute(\"SELECT Contents FROM PreprocessedFiles WHERE status=0 ORDER BY RANDOM() LIMIT 1\")\n",
    "print(db.fetchone()[0])\n",
    "db.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A side effect of this process is a reduction in code size, largely due to the\n",
    "removal of comments and excess white space. The final language corpus contains\n",
    "1.3 million lines of transformed OpenCL, consisting of 9487 kernel functions.\n",
    "Identifier rewriting reduces the bag-of-words vocabulary size by 84%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating DNN corpus ../data/corpus.cl ...\n",
      "writing file ../data/corpus.cl\n",
      "  #. lines: 1283985\n",
      "  #. words: 7394398\n",
      "  #. chars: 32557439\n"
     ]
    }
   ],
   "source": [
    "import clgen.train\n",
    "\n",
    "db = clgen.dbutil.connect(\"../data/github.db\")\n",
    "clgen.train.create_corpus(db, \"../data/corpus.cl\", gh=True)\n",
    "\n",
    "lc, wc, cc = line_word_char_count(\"../data/corpus.cl\")\n",
    "print(\"  #. lines:\", lc)\n",
    "print(\"  #. words:\", wc)\n",
    "print(\"  #. chars:\", cc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2. Learning OpenCL\n",
    "\n",
    "Generating valid, executable program code is an ambitious and\n",
    "challenging goal for machine learning. We employ state of the art deep\n",
    "language modeling techniques to achieve this task.\n",
    "\n",
    "We use the Long Short-Term Memory (LSTM) architecture of Recurrent Neural\n",
    "Network to learn a character-level language model over the corpus of OpenCL\n",
    "compute kernels. The LSTM network architecture comprises recurrent layers of\n",
    "*memory cells*, each consisting of an input, output, and forget gate, and an\n",
    "output layer providing normalized probability values from a 1-of-K coded\n",
    "vocabulary.\n",
    "\n",
    "We use a 3-layer LSTM network with 2048 nodes per layer. We train this\n",
    "17-million parameter model using *Stochastic Gradient Descent* for 50 epochs,\n",
    "using an initial learning rate of 0.002, decaying by a factor of one half every\n",
    "5 epochs. Training took three weeks on a single machine using an NVIDIA GTX\n",
    "Titan, with a final model size of 648MB. Training the network is a one-off cost,\n",
    "and can be parallelized across devices. The trained network can be deployed to\n",
    "lower-compute machines for use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training a tiny network on a tiny dataset:\n",
      "corpus of 73 files\n",
      "Running with CUDA on GPU 0\t\n",
      "Epoch 1.06 / 1, i = 10 / 175, loss = 2.711993\t\n",
      "Epoch 1.11 / 1, i = 20 / 175, loss = 2.559526\t\n",
      "Epoch 1.17 / 1, i = 30 / 175, loss = 2.449289\t\n",
      "Epoch 1.23 / 1, i = 40 / 175, loss = 2.308948\t\n",
      "Epoch 1.29 / 1, i = 50 / 175, loss = 2.015696\t\n",
      "Epoch 1.34 / 1, i = 60 / 175, loss = 1.782664\t\n",
      "Epoch 1.40 / 1, i = 70 / 175, loss = 1.585669\t\n",
      "Epoch 1.46 / 1, i = 80 / 175, loss = 1.326936\t\n",
      "Epoch 1.51 / 1, i = 90 / 175, loss = 1.051463\t\n",
      "Epoch 1.57 / 1, i = 100 / 175, loss = 0.923106\t\n",
      "val_loss = \t1.6305290630886\t\n",
      "Epoch 1.63 / 1, i = 110 / 175, loss = 0.775296\t\n",
      "Epoch 1.69 / 1, i = 120 / 175, loss = 0.597679\t\n",
      "Epoch 1.74 / 1, i = 130 / 175, loss = 0.501275\t\n",
      "Epoch 1.80 / 1, i = 140 / 175, loss = 0.399302\t\n",
      "Epoch 1.86 / 1, i = 150 / 175, loss = 0.322211\t\n",
      "Epoch 1.91 / 1, i = 160 / 175, loss = 0.411278\t\n",
      "Epoch 1.97 / 1, i = 170 / 175, loss = 0.231095\t\n",
      "val_loss = \t1.1247653279986\t\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "bdd197a1b629faae3255602c88482a7cc6169939: {\n",
       "  \"max_epochs\": 1,\n",
       "  \"model_type\": \"lstm\",\n",
       "  \"num_layers\": 2,\n",
       "  \"rnn_size\": 128\n",
       "}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import clgen.model\n",
    "\n",
    "print(\"Training a tiny network on a tiny dataset:\")\n",
    "tiny_model = clgen.model.from_json({\n",
    "        \"corpus\": {\n",
    "            \"path\": \"../data/tiny-corpus\"\n",
    "        },\n",
    "        \"train_opts\": {\n",
    "            \"model_type\": \"lstm\",\n",
    "            \"rnn_size\": 128,\n",
    "            \"num_layers\": 2,\n",
    "            \"max_epochs\": 1\n",
    "        }\n",
    "    })\n",
    "tiny_model.cache.empty()\n",
    "print(tiny_model.corpus)\n",
    "tiny_model.train()\n",
    "tiny_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model used in the paper (pre-trained):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "f2fb3ad753896d54fe284c138eaa703db3518bbb: {\n",
       "  \"max_epochs\": 50,\n",
       "  \"model_type\": \"lstm\",\n",
       "  \"num_layers\": 3,\n",
       "  \"rnn_size\": 2048\n",
       "}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"The model used in the paper (pre-trained):\")\n",
    "model = clgen.model.from_tar(\"../data/clgen-github-model-2016-nov-2048x3.tar.bz2\")\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3. Synthesizing OpenCL\n",
    "\n",
    "We synthesize OpenCL compute kernels by iteratively sampling the learned\n",
    "language model. We implemented two modes for model sampling: the first involves\n",
    "providing an *argument specification*, stating the data types and modifiers of\n",
    "all kernel arguments. When an argument specification is provided, the model\n",
    "synthesizes kernels matching this signature. In the second sampling mode this\n",
    "argument specification is omitted, allowing the model to synthesize compute\n",
    "kernels of arbitrary signatures, dictated by the distribution of argument types\n",
    "within the language corpus.\n",
    "\n",
    "In either mode we generate a *seed* text:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__kernel void A(__global float* a, __global float* b, __global float* c, const int d) {\n"
     ]
    }
   ],
   "source": [
    "print(clgen.sampler.serialize_argspec([\n",
    "    '__global float*',\n",
    "    '__global float*',\n",
    "    '__global float*',\n",
    "    'const int'\n",
    "]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__kernel void A(__global int* a, const int b) {\n"
     ]
    }
   ],
   "source": [
    "print(clgen.sampler.serialize_argspec([\n",
    "    '__global int*',\n",
    "    'const int'\n",
    "]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__kernel void A(__global int* a, const int b) {\n"
     ]
    }
   ],
   "source": [
    "print(clgen.sampler.serialize_argspec([\n",
    "    '__global int*',\n",
    "    'const int'\n",
    "]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then sample the model, character by character, until the end of the compute kernel is reached, or until a\n",
    "predetermined maximum number of characters is reached.\n",
    "\n",
    "Live demos (re-run to re-sample):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample from the tiny model we just trained:\n",
      "beginning batch 1 ...\n",
      "__kernel void A(__global float* a, __global float* b, __global float* c, const int d) {\n",
      "  h = ((f + j) & (i a - 1))) + ((e * a + a * k) & (i - 1);\n",
      "  if (b[h] 0= 0;\n",
      "  i g += 1;\n",
      "  h != ((e * (a - 1)) + (e * a + a * k) & (i - 1));\n",
      "  if (b[h] != 0) {\n",
      "    g += 1;\n",
      "  }\n",
      "\n",
      "  j = 13;\n",
      "  j = -13;\n",
      "  j = 1\n",
      ";\n",
      "  h = ((f k) & j - 0) + 1);\n",
      "  a = ((f + * a & (i - 1));\n",
      "  if (b[h] != 0) {\n",
      "   g += 1;\n",
      "  }\n",
      "\n",
      "  j = -1;\n",
      "  k = -5;\n",
      "  h = ((f + j) & (a * ((e * a + a * k) + j * 1)) + (a - 1)) + ((e * a + (a * k) & (f - 1));\n",
      "  if ((f (b[h = if 0) {\n",
      "    g != 1;\n",
      "  k = h2;\n",
      "  h = ((f + j) & (a + a * k) & (i - 1));\n",
      "  if (f ((f + (a * (i - 1)) {\n",
      "    if -[b4[Q]= 1;\n",
      "   = (b[h] != + ((e * a + a * k) & (i - 1));\n",
      "  if (b[h] != 0) {\n",
      "\n",
      "      = + 1;\n",
      "  }\n",
      "\n",
      "  j = -3;\n",
      "  k = 7;\n",
      "  h = ((f + j) & (i - 1))) 1)) + ((e * (a + a * k) & (i - 1));\n",
      "  if (b[h] != 0) {\n",
      "     = 2;\n",
      "  }\n",
      "\n",
      "  j = -+;\n",
      "  k = -1;\n",
      "  h = ((f + j) & (a - 1)) + ((e * (a * 1) + a * k) & (i - {) 1;\n",
      "  }\n",
      "  j = -2;\n",
      "  }\n",
      "\n",
      "  j = -;\n",
      "  k = -1;\n",
      "  h = ((f + j) & (a i (a - 1)) + (a - 1)) + (\n",
      "\n",
      "Number of content files:            1\n",
      "\n",
      "Number of unique content files:     1 (100%)\n",
      "Total content line count:           51\n",
      "Content file line counts:           min: 51, med: 51, avg: 51, max: 51\n",
      "\n",
      "Number of good preprocessed files:  0 (0%)\n",
      "Lines of good preprocessed code:    0 (0%)\n",
      "Good preprocessed line counts:      min: 0, med: 0, avg: 0, max: 0\n",
      "\n",
      "beginning batch 2 ...\n"
     ]
    }
   ],
   "source": [
    "import clgen.sampler\n",
    "\n",
    "argspec = ['__global float*', '__global float*', '__global float*', 'const int']\n",
    "\n",
    "sampler = clgen.sampler.from_json({\n",
    "        \"kernels\": { \n",
    "            \"args\": argspec,\n",
    "            \"max_length\": 1000,\n",
    "        },\n",
    "        \"sampler\": {\n",
    "            \"batch_size\": 1,\n",
    "            \"max_batches\": 1\n",
    "        }\n",
    "    })\n",
    "\n",
    "print(\"Sample from the tiny model we just trained:\")\n",
    "sampler.sample(tiny_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample from the model used in the paper:\n",
      "beginning batch 1 ...\n",
      "__kernel void A(__global float* a, __global float* b, __global float* c, const int d) {\n",
      "  int e = get_global_id(0);\n",
      "\n",
      "  if (e < d) {\n",
      "    c[e] = a[e] + b[e];\n",
      "  }\n",
      "}\n",
      "\n",
      "Number of content files:            2\n",
      "\n",
      "Number of unique content files:     2 (100%)\n",
      "Total content line count:           14\n",
      "Content file line counts:           min: 7, med: 7, avg: 7, max: 7\n",
      "\n",
      "Number of good preprocessed files:  1 (50%)\n",
      "Lines of good preprocessed code:    7 (50%)\n",
      "Good preprocessed line counts:      min: 7, med: 7, avg: 7, max: 7\n",
      "\n",
      "beginning batch 2 ...\n"
     ]
    }
   ],
   "source": [
    "print(\"Sample from the model used in the paper:\")\n",
    "sampler.sample(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rejection filter then either accepts or rejects the sample as a candidate synthetic benchmark\n",
    "\n",
    "Live demo which generates a single statically-verified OpenCL kernel using the pre-trained network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generate a statically checked kernel using the pre-trained network:\n",
      "beginning batch 1 ...\n",
      "\n",
      "\n",
      "/* === SAMPLE 1 === */\t\n",
      "__kernel void A(__global float* a, __global float* b, __global float* c, const int d) {\n",
      "  int e = get_global_id(0);\n",
      "  int f = c / d;\n",
      "  int g = e * d;\n",
      "\n",
      "  if (g < c && g < c) {\n",
      "    e += g;\n",
      "    e[0] = f;\n",
      "  }\n",
      "\n",
      "  if (h < e) {\n",
      "    b[0] = 2 * a[0] + c[0] * c[0];\n",
      "  }\n",
      "  b[g * get_global_size(0) + d] = g;\n",
      "}\n",
      "\n",
      "/* === SAMPLE 2 === */\t\n",
      "__kernel void A(__global float* a, __global float* b, __global float* c, const int d) {\n",
      "  int e = get_global_id(0);\n",
      "\n",
      "  if (e < d)\n",
      "    b[e] = a[e] + a[e];\n",
      "}\n",
      "\n",
      "/* === SAMPLE 3 === */\t\n",
      "__kernel void A(__global float* a, __global float* b, __global float* c, const int d) {\n",
      "  int e = get_global_id(0);\n",
      "  int f = f / e;\n",
      "  c[e] = lgain(e, f, d, f);\n",
      "}\n",
      "\n",
      "/* === SAMPLE 4 === */\t\n",
      "__kernel void A(__global float* a, __global float* b, __global float* c, const int d) {\n",
      "  int e = get_global_id(0);\n",
      "  if (e < d) {\n",
      "    float f = a[e];\n",
      "    float g = a[e];\n",
      "    c[e] = f;\n",
      "  }\n",
      "}\n",
      "\n",
      "/* === SAMPLE 5 === */\t\n",
      "__kernel void A(__global float* a, __global float* b, __global float* c, const int d) {\n",
      "  int e = get_global_id(0);\n",
      "  int f = 0;\n",
      "  c[e] = a[e] + d;\n",
      "\n",
      "  barrier(1);\n",
      "\n",
      "  e = c + 1;\n",
      "  barrier(1);\n",
      "\n",
      "  for (int g = 1; g < b; g <<= 1) {\n",
      "    c >>= 1;\n",
      "    barrier(1);\n",
      "\n",
      "    if (e < g) {\n",
      "      int h = f * (2 * e + 1) - 1;\n",
      "      int i = f * (2 * e + 2) - 1;\n",
      "\n",
      "      u32 j = a[i];\n",
      "      a[i] = a[j];\n",
      "      a[j] += j;\n",
      "    }\n",
      "  }\n",
      "\n",
      "  return;\n",
      "}\n",
      "\n",
      "/* === SAMPLE 6 === */\t\n",
      "__kernel void A(__global float* a, __global float* b, __global float* c, const int d) {\n",
      "  int e = get_global_id(0);\n",
      "\n",
      "  if (e < d)\n",
      "    c[e] = a[e] + b[e] * c[e];\n",
      "}\n",
      "\n",
      "/* === SAMPLE 7 === */\t\n",
      "__kernel void A(__global float* a, __global float* b, __global float* c, const int d) {\n",
      "  int e = get_global_id(0);\n",
      "\n",
      "  if (e < d) {\n",
      "    a[b] = a[e] * b[e];\n",
      "  }\n",
      "}\n",
      "\n",
      "/* === SAMPLE 8 === */\t\n",
      "__kernel void A(__global float* a, __global float* b, __global float* c, const int d) {\n",
      "  int e = get_global_id(0);\n",
      "\n",
      "  sampler_t f = 0x0000 | 0x0002 | 0x0000;\n",
      "\n",
      "  float4 g = read_imagef(b, d, g);\n",
      "  g += read_imagef(b, d, g + 0.f);\n",
      "  write_imagef(b, (int2)(0.f, + b);\n",
      "}\n",
      "\n",
      "/* === SAMPLE 9 === */\t\n",
      "__kernel void A(__global float* a, __global float* b, __global float* c, const int d) {\n",
      "  int e = get_global_id(0);\n",
      "\n",
      "  if (e < c) {\n",
      "    a[e] += 1;\n",
      "  }\n",
      "  b[e].vector_x[0] = c[0];\n",
      "}\n",
      "\n",
      "/* === SAMPLE 10 === */\t\n",
      "__kernel void A(__global float* a, __global float* b, __global float* c, const int d) {\n",
      "  int e = get_global_id(0);\n",
      "\n",
      "  if (e >= d) {\n",
      "    return;\n",
      "  }\n",
      "\n",
      "  int f = 0;\n",
      "  const int g = h >> 1;\n",
      "  const int h = ((f + d) >= h & ((1 << g) - 1));\n",
      "\n",
      "  float4 m = (float4)(0);\n",
      "  for (int n = 0; n < c; n++) {\n",
      "    for (int o = 0; o < f; o++) {\n",
      "      float p = (float)((float)m / l) - A(a, b, c, n);\n",
      "      float2 q = __clc_floor(p);\n",
      "      float2 r = n[q];\n",
      "      float s = cos(n);\n",
      "      float2 t = (float2)((float)ubr;\n",
      "\n",
      "      float u = (j[q]) * n * p;\n",
      "      float v = cos((o[q]) / (p[m]));\n",
      "\n",
      "      r += r;\n",
      "      r *= t;\n",
      "    }\n",
      "    t = max(max(convert_float3(convert_float4(r[i][j], l)) > o));\n",
      "\n",
      "    b[g * e + c] = q;\n",
      "    m[f * 4 + 0] = w;\n",
      "  }\n",
      "}\n",
      "\n",
      "/* === SAMPLE 11 === */\t\n",
      "__kernel void A(__global float* a, __global float* b, __global float* c, const int d) {\n",
      "  int e = get_global_id(0);\n",
      "  if (e < d) {\n",
      "    const int f = b + d * c;\n",
      "    const int g = h - 1 - d * b + d;\n",
      "    const int h = f + b;\n",
      "    const int i = (f + 1 - c) * d;\n",
      "\n",
      "    volatile __local int j;\n",
      "    if (j == e - 1)\n",
      "      a[g + 1] = 0;\n",
      "  }\n",
      "\n",
      "  for (int i = get_global_id(0); i < d; i += get_global_size(0)) {\n",
      "    if (a[i].x == d)\n",
      "      f[j] = 0;\n",
      "\n",
      "    a[i] = 1.0 * convert_int(a[i * c + i]) - i;\n",
      "  }\n",
      "}\n",
      "\n",
      "/* === SAMPLE 12 === */\t\n",
      "__kernel void A(__global float* a, __global float* b, __global float* c, const int d) {\n",
      "  int e = get_local_id(0);\n",
      "\n",
      "  if (e < 2 && f < c) {\n",
      "    c[e] += b[e];\n",
      "  }\n",
      "  b[d] = c[0];\n",
      "}\n",
      "\n",
      "/* === SAMPLE 13 === */\t\n",
      "__kernel void A(__global float* a, __global float* b, __global float* c, const int d) {\n",
      "  int e = get_global_id(0);\n",
      "\n",
      "  if (e < d) {\n",
      "    a[e] += a[e];\n",
      "  }\n",
      "}\n",
      "\n",
      "/* === SAMPLE 14 === */\t\n",
      "__kernel void A(__global float* a, __global float* b, __global float* c, const int d) {\n",
      "  int e = get_global_id(0);\n",
      "\n",
      "  if (e < d) {\n",
      "    float k = 0.0f;\n",
      "    int l = 0;\n",
      "    int m = 0;\n",
      "    int n = 0, o = 0;\n",
      "\n",
      "    for (int o = -1; o <= 1; o++) {\n",
      "      float4 p = (o * p) - (n * c[o]);\n",
      "\n",
      "      if (k != 0 && k < e - 1) {\n",
      "        b = fmax(l, p);\n",
      "        a[m] = n;\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "\n",
      "  barrier(1);\n",
      "\n",
      "  for (int p = 0; p < e; p++) {\n",
      "    for (int q = -(f << 1); f >>= 1) {\n",
      "      barrier(1);\n",
      "      if (j < m) {\n",
      "        e[m + q] = c[o + p];\n",
      "      }\n",
      "      barrier(1);\n",
      "    }\n",
      "\n",
      "    for (int r = 1; r < p; ++r) {\n",
      "      uint s = l * d;\n",
      "      A(a, b, c, d, e, n);\n",
      "      B(a, b, c, d, e, f, g, g);\n",
      "      q[a] = B(b, c, d, n);\n",
      "    }\n",
      "  }\n",
      "}\n",
      "\n",
      "/* === SAMPLE 15 === */\t\n",
      "__kernel void A(__global float* a, __global float* b, __global float* c, const int d) {\n",
      "  int e = get_global_id(0);\n",
      "\n",
      "  if (e < c) {\n",
      "    a[e + c] = b[e].z;\n",
      "  }\n",
      "\n",
      "  barrier(1);\n",
      "\n",
      "  B(e, d, e);\n",
      "}\n",
      "\n",
      "Number of content files:            15\n",
      "\n",
      "Number of unique content files:     15 (100%)\n",
      "Total content line count:           206\n",
      "Content file line counts:           min: 5, med: 8, avg: 14, max: 38\n",
      "\n",
      "Number of good preprocessed files:  5 (33%)\n",
      "Lines of good preprocessed code:    32 (16%)\n",
      "Good preprocessed line counts:      min: 5, med: 6, avg: 6, max: 8\n",
      "\n",
      "beginning batch 2 ...\n",
      "done.\n"
     ]
    }
   ],
   "source": [
    "print(\"Generate a statically checked kernel using the pre-trained network:\")\n",
    "sampler = clgen.sampler.from_json({\n",
    "        \"kernels\": { \n",
    "            \"args\": argspec,\n",
    "            \"max_length\": 1000,\n",
    "        },\n",
    "        \"sampler\": {\n",
    "            \"batch_size\": 15,\n",
    "            \"max_kernels\": 5\n",
    "        }\n",
    "    })\n",
    "\n",
    "sampler.cache(model).empty()\n",
    "sampler.sample(model)\n",
    "print(\"done.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Benchmark Execution\n",
    "\n",
    "We developed a host driver to gather performance data from synthesized CLgen\n",
    "code. The driver accepts as input an OpenCL kernel, generates *payloads* of\n",
    "user-configurable sizes, and executes the kernel using the generated payloads,\n",
    "providing dynamic checking of kernel behavior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__kernel void A(__global float* a, __global float* b, const float c, const int d) {\n",
      "  const int e = get_global_id(0);\n",
      "  if (e < d)\n",
      "    a[e] += c * b[e];\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "import clgen.cldrive, clgen.preprocess\n",
    "\n",
    "src = clgen.preprocess.preprocess(\"\"\"\n",
    "__kernel void benchmark(__global float* a, __global float* b, const float c, const int n) {\n",
    "   const int i = get_global_id(0);\n",
    "   if (i < n)\n",
    "     a[i] += c * b[i];\n",
    "}\n",
    "\"\"\")\n",
    "\n",
    "if has_opencl():\n",
    "    ctx, queue = clgen.cldrive.init_opencl()\n",
    "    driver = clgen.cldrive.KernelDriver(ctx, src)\n",
    "    print(driver)\n",
    "else:\n",
    "    print(\"no OpenCL support, skipped\", file=sys.stderr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1. Generating Payloads\n",
    "\n",
    "A *payload* encapsulates all of the arguments of an OpenCL compute kernel. After\n",
    "parsing the input kernel to derive argument types, a rule-based approach is used\n",
    "to generate synthetic payloads. For a given global size $S_g$: host buffers of\n",
    "$S_g$ elements are allocated and populated with random values for global pointer\n",
    "arguments, device-only buffers of $S_g$ elements are allocated for local pointer\n",
    "arguments, integral arguments are given the value $S_g$, and all other scalar\n",
    "arguments are given random values. Host to device data transfers are enqueued\n",
    "for all non-write-only global buffers, and all non-read-only global buffers are\n",
    "transferred back to the host after kernel execution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Payload on host:\n",
      "  arg 0: ndarray size: 4096 [ 0.7320469   0.55817854  0.5408082  ...,  0.8732034   0.25715524\n",
      "  0.83498967]\n",
      "  arg 1: ndarray size: 4096 [ 0.05223281  0.23842625  0.14364108 ...,  0.18417674  0.00829434\n",
      "  0.04460072]\n",
      "  arg 2: None\n",
      "  arg 3: None\n",
      "\n",
      "Payload on device:\n",
      "  arg 0: Buffer size: 16384 <pyopencl.cffi_cl.Buffer object at 0x7fd868c0c9e8>\n",
      "  arg 1: Buffer size: 16384 <pyopencl.cffi_cl.Buffer object at 0x7fd868c0c978>\n",
      "  arg 2: float32 val: 4096.0\n",
      "  arg 3: int32 val: 4096\n",
      "\n"
     ]
    }
   ],
   "source": [
    "if has_opencl():\n",
    "    size = 4096\n",
    "    payload = clgen.cldrive.KernelPayload.create_random(driver, size)\n",
    "    print(payload)\n",
    "else:\n",
    "    print(\"no OpenCL support, skipped\", file=sys.stderr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2. Dynamic Checker\n",
    "\n",
    "For the purpose of performance benchmarking we are not interested in the\n",
    "correctness of computed values, but we define a class of programs as performing\n",
    "*useful work* if they predictably compute some result. We devised a low-overhead\n",
    "runtime behavior check to validate that a synthesized program does useful work\n",
    "based on the outcome of four executions of a tested program:\n",
    "\n",
    "**Step 1:** Create 4 equal size payloads $A_{1in}$, $B_{1in}$, $A_{2in}$, $B_{2in}$,\n",
    "    subject to restrictions: $A_{1in}=A_{2in}$, $B_{1in}=B_{2in}$, $A_{1in} \\ne\n",
    "    B_{1in}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generated payloads\n"
     ]
    }
   ],
   "source": [
    "from copy import deepcopy\n",
    "\n",
    "if has_opencl():\n",
    "    # create payloads\n",
    "    A1in = clgen.cldrive.KernelPayload.create_sequential(driver, size)\n",
    "    A2in = deepcopy(A1in)\n",
    "\n",
    "    B1in = clgen.cldrive.KernelPayload.create_random(driver, size)\n",
    "    B2in = deepcopy(B1in)\n",
    "\n",
    "    # check contstraints\n",
    "    assert(A1in == A2in)\n",
    "    assert(B1in == B2in)\n",
    "    assert(A1in != B1in)\n",
    "    print(\"generated payloads\")\n",
    "else:\n",
    "    print(\"no OpenCL support, skipped\", file=sys.stderr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 2:** Execute kernel $k$ 4 times: $k(A_{1in}) \\rightarrow A_{1out}$, $k(B_{1in})\n",
    "   \\rightarrow B_{1out}$, $k(A_{2in}) \\rightarrow A_{2out}$, $k(B_{2in})\n",
    "   \\rightarrow B_{2out}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "executed kernel with paylods\n"
     ]
    }
   ],
   "source": [
    "if has_opencl():\n",
    "    k = partial(driver, queue)\n",
    "    A1out = k(A1in)\n",
    "    B1out = k(B1in)\n",
    "    A2out = k(A2in)\n",
    "    B2out = k(B2in)\n",
    "    print(\"executed kernel with paylods\")\n",
    "else:\n",
    "    print(\"no OpenCL support, skipped\", file=sys.stderr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 3:**  Assert:\n",
    "\n",
    "* $A_{1out} \\ne A_{1in}$ and $B_{1out} \\ne B_{1in}$, else $k$ has no output (for these inputs).\n",
    "\n",
    "* $A_{1out} \\ne B_{1out}$ and $A_{2out} \\ne B_{2out}$, else $k$ is input insensitive (for these inputs).\n",
    "\n",
    "* $A_{1out}=A_{2out}$ and $B_{1out}=B_{2out}$, else $k$ is non-deterministic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kernel passed validation\n"
     ]
    }
   ],
   "source": [
    "if has_opencl():\n",
    "    # outputs must be consistent across runs:\n",
    "    assert(A1out == A2out)\n",
    "    assert(B1out == B2out)\n",
    "\n",
    "    # outputs must depend on inputs:\n",
    "    if any(not x.is_const for x in driver.prototype.args):\n",
    "        assert(A1out != B1out)\n",
    "\n",
    "    # outputs must be different from inputs:\n",
    "    assert(A1in != A1out)\n",
    "    assert(B1in != B1out)\n",
    "    print(\"kernel passed validation\")\n",
    "else:\n",
    "    print(\"no OpenCL support, skipped\", file=sys.stderr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Equality checks for floating point values are performed with an appropriate\n",
    "epsilon to accommodate rounding errors, and a timeout threshold is also used to\n",
    "catch kernels which are non-terminating. Our method is based on random\n",
    "differential testing, though we emphasize that this is not a general purpose\n",
    "approach and is tailored specifically for our use case. For example, we\n",
    "anticipate a false positive rate for kernels with subtle sources of\n",
    "non-determinism which more thorough methods may expose, however we deemed such\n",
    "methods unnecessary for our purpose of performance modeling.\n",
    "\n",
    "Examples of programs which are our dynamic checker catches:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "caught kernel with no outputs\n"
     ]
    }
   ],
   "source": [
    "if has_opencl():\n",
    "    driver = clgen.cldrive.KernelDriver(ctx, \"\"\"\n",
    "    __kernel void A(__global int* a) {}\n",
    "    \"\"\")\n",
    "\n",
    "    try:\n",
    "        driver.validate(queue, 8192)\n",
    "    except clgen.cldrive.E_NO_OUTPUTS:\n",
    "        print(\"caught kernel with no outputs\")\n",
    "    except clgen.cldrive.OpenCLNotSupported:\n",
    "        print(\"no OpenCL support, skipped\", file=sys.stderr)\n",
    "else:\n",
    "    print(\"no OpenCL support, skipped\", file=sys.stderr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "caught input-insensitive kernel\n"
     ]
    }
   ],
   "source": [
    "if has_opencl():\n",
    "    driver = clgen.cldrive.KernelDriver(ctx, \"\"\"\n",
    "    __kernel void A(__global int* a) {\n",
    "      int b = get_global_id(0);\n",
    "      a[b] = 0;\n",
    "    }\"\"\")\n",
    "\n",
    "    try:\n",
    "        driver.validate(queue, 8192)\n",
    "    except clgen.cldrive.E_INPUT_INSENSITIVE:\n",
    "        print(\"caught input-insensitive kernel\")\n",
    "    except clgen.cldrive.OpenCLNotSupported:\n",
    "        print(\"no OpenCL support, skipped\", file=sys.stderr)\n",
    "else:\n",
    "    print(\"no OpenCL support, skipped\", file=sys.stderr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "caught non-deterministic kernel\n"
     ]
    }
   ],
   "source": [
    "if has_opencl():\n",
    "    driver = clgen.cldrive.KernelDriver(ctx, \"\"\"\n",
    "    __kernel void A(__global float* a) {\n",
    "      int b = get_global_id(0);\n",
    "\n",
    "      a[b] = a[b-1] + a[b+1]; /* race condition */\n",
    "    }\"\"\")\n",
    "\n",
    "    try:\n",
    "        driver.validate(queue, 8192)\n",
    "    except clgen.cldrive.E_NONDETERMINISTIC:\n",
    "        print(\"caught non-deterministic kernel\")\n",
    "    except clgen.cldrive.OpenCLNotSupported:\n",
    "        print(\"no OpenCL support, skipped\", file=sys.stderr)\n",
    "else:\n",
    "    print(\"no OpenCL support, skipped\", file=sys.stderr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Evaluation of Synthetic Programs\n",
    "\n",
    "In this section we evaluate the quality of programs synthesized by CLgen\n",
    "by their likeness to hand-written code, and discuss limitations of the\n",
    "synthesis and execution pipeline.\n",
    "\n",
    "## 6.1. Likeness to hand-written code\n",
    "\n",
    "Judging whether a source code was written by a human is a challenging task for a\n",
    "machine, so we adopt a methodology from machine learning research based on the\n",
    "*Turing Test*. We reason that if the output of CLgen is human like code, then a\n",
    "human judge will be unable to distinguish it from hand-written code.\n",
    "\n",
    "We devised a double blind test in which 15 volunteer OpenCL developers from\n",
    "industry and academia were shown 10 OpenCL kernels each. Participants were\n",
    "tasked with judging whether, for each kernel, they believed it to have been\n",
    "written by hand or by machine. Kernels were randomly selected for each\n",
    "participant from two equal sized pools of synthetically generated and hand-\n",
    "written code from GitHub.\n",
    "\n",
    "The participants were divided into two groups, with 10 of them receiving code\n",
    "generated by CLgen, and 5 of them acting as a control group, receiving code\n",
    "generated by CLSmith, a program generator for differential testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Test ID</th>\n",
       "      <th>Synthetic Source</th>\n",
       "      <th>Affiliation</th>\n",
       "      <th>True Positive</th>\n",
       "      <th>False Positive</th>\n",
       "      <th>True Negative</th>\n",
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       "      <th>Score (%)</th>\n",
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       "      <td>University of Edinburgh</td>\n",
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       "      <td>2</td>\n",
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       "      <th>3</th>\n",
       "      <td>5e69</td>\n",
       "      <td>CLSmith</td>\n",
       "      <td>University of Edinburgh</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5ddf</td>\n",
       "      <td>CLSmith</td>\n",
       "      <td>University of Edinburgh</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>97ac</td>\n",
       "      <td>CLgen</td>\n",
       "      <td>University of Edinburgh</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>b2ce</td>\n",
       "      <td>CLgen</td>\n",
       "      <td>University of Edinburgh</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5d56</td>\n",
       "      <td>CLSmith</td>\n",
       "      <td>University of Edinburgh</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>b2d5</td>\n",
       "      <td>CLgen</td>\n",
       "      <td>Codeplay</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>b2d3</td>\n",
       "      <td>CLgen</td>\n",
       "      <td>Codeplay</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>b31d</td>\n",
       "      <td>CLgen</td>\n",
       "      <td>Codeplay</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5e42</td>\n",
       "      <td>CLSmith</td>\n",
       "      <td>Codeplay</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>b2d8</td>\n",
       "      <td>CLgen</td>\n",
       "      <td>Codeplay</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>b310</td>\n",
       "      <td>CLgen</td>\n",
       "      <td>Codeplay</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>b318</td>\n",
       "      <td>CLgen</td>\n",
       "      <td>Codeplay</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Test ID Synthetic Source              Affiliation  True Positive  \\\n",
       "0     5e8f          CLSmith  University of Edinburgh              5   \n",
       "1     4da2            CLgen  University of Edinburgh              3   \n",
       "2     b323            CLgen                 Codeplay              5   \n",
       "3     5e69          CLSmith  University of Edinburgh              5   \n",
       "4     5ddf          CLSmith  University of Edinburgh              4   \n",
       "5     97ac            CLgen  University of Edinburgh              1   \n",
       "6     b2ce            CLgen  University of Edinburgh              4   \n",
       "7     5d56          CLSmith  University of Edinburgh              5   \n",
       "8     b2d5            CLgen                 Codeplay              2   \n",
       "9     b2d3            CLgen                 Codeplay              2   \n",
       "10    b31d            CLgen                 Codeplay              0   \n",
       "11    5e42          CLSmith                 Codeplay              5   \n",
       "12    b2d8            CLgen                 Codeplay              2   \n",
       "13    b310            CLgen                 Codeplay              3   \n",
       "14    b318            CLgen                 Codeplay              2   \n",
       "\n",
       "    False Positive  True Negative  False Negative  Score (%)  \n",
       "0                0              5               0        100  \n",
       "1                2              4               1         70  \n",
       "2                2              3               0         80  \n",
       "3                0              3               2         80  \n",
       "4                0              6               0        100  \n",
       "5                3              3               3         40  \n",
       "6                1              3               2         70  \n",
       "7                0              5               0        100  \n",
       "8                3              2               3         40  \n",
       "9                2              2               4         40  \n",
       "10               4              4               2         40  \n",
       "11               0              5               0        100  \n",
       "12               2              4               2         60  \n",
       "13               2              2               3         50  \n",
       "14               5              1               2         30  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"../data/turing-test.csv\")\n",
    "data[\"Score (%)\"] = (data[\"True Positive\"] + data[\"True Negative\"]) * 10\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis of participant’s answers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean CLgen score:   52.0 %, stdev: 16.9 %\n",
      "Mean CLSmith score: 96.0 %, stdev: 8.9 %\n"
     ]
    }
   ],
   "source": [
    "clgen_data   = data[data[\"Synthetic Source\"].str.contains(\"CLgen\")]\n",
    "clsmith_data = data[data[\"Synthetic Source\"].str.contains(\"CLSmith\")]\n",
    "\n",
    "print(\"Mean CLgen score:  \", round(  clgen_data[\"Score (%)\"].mean(), 1), \"%, stdev:\",\n",
    "      round(  clgen_data[\"Score (%)\"].std(), 1), \"%\")\n",
    "print(\"Mean CLSmith score:\", round(clsmith_data[\"Score (%)\"].mean(), 1), \"%, stdev:\",\n",
    "      round(clsmith_data[\"Score (%)\"].std(), 1), \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This an unsurprising outcome as generated programs for\n",
    "testing have multiple “tells”, for example, their only input is a single `ulong`\n",
    "pointer. This demonstrates that CLgen code is indistinguishable from hand-written\n",
    "programs, with human judges scoring no better than random chance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An online game of this test is available [here](http://humanorrobot.uk/game/?g=opencl&m=nitt)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.2. Limitations\n",
    "\n",
    "Our new approach enables the synthesis of more human-like programs than current\n",
    "state of the art program generators, and without the expert guidance required by\n",
    "template based generators, but it has limitations. Currently we only run single-\n",
    "kernel benchmarks, and our method of seeding the language models with the start\n",
    "of a function means that we cannot support user defined types, or calls to user-\n",
    "defined functions. The first limitation will be overcome by extending the host\n",
    "driver to explore multi-kernel schedules and interleaving of kernel executions.\n",
    "The second limitation can be addressed through recursive program synthesis,\n",
    "whereby a call to a user-defined function or type will trigger candidate\n",
    "functions and types to be synthesized."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Experimental Methodology\n",
    "\n",
    "## 7.1. Experimental Setup \n",
    "\n",
    "#### Predictive Model\n",
    "\n",
    "We reproduce the predictive model from Grewe et al. (2013). The predictive model\n",
    "is used to determine the optimal mapping of a given OpenCL kernel to either a\n",
    "GPU or CPU. It uses supervised learning to construct a decision tree with a\n",
    "combination of static and dynamic kernel features extracted from source code and\n",
    "the OpenCL runtime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table width=100%><tr><td><b>atomic</b></td><td>0</td></tr><tr><td><b>kernel</b></td><td>foobar</td></tr><tr><td><b>coalesced</b></td><td>2</td></tr><tr><td><b>F4:comp/mem</b></td><td>0.5</td></tr><tr><td><b>file</b></td><td>kernel.cl</td></tr><tr><td><b>comp</b></td><td>1</td></tr><tr><td><b>rational</b></td><td>1</td></tr><tr><td><b>mem</b></td><td>2</td></tr><tr><td><b>F2:coalesced/mem</b></td><td>1</td></tr><tr><td><b>localmem</b></td><td>0</td></tr></table>"
      ],
      "text/plain": [
       "{'F2:coalesced/mem': '1',\n",
       " 'F4:comp/mem': '0.5',\n",
       " 'atomic': '0',\n",
       " 'coalesced': '2',\n",
       " 'comp': '1',\n",
       " 'file': 'kernel.cl',\n",
       " 'kernel': 'foobar',\n",
       " 'localmem': '0',\n",
       " 'mem': '2',\n",
       " 'rational': '1'}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def features_to_dict(path):\n",
    "    import clgen.features\n",
    "    buf = StringIO()\n",
    "    clgen.features.feature_headers(file=buf)  # feature names\n",
    "    keys = buf.getvalue().rstrip().split(\",\")\n",
    "    buf = StringIO()\n",
    "    clgen.features.features(path, file=buf)  # extract features\n",
    "    values = buf.getvalue().rstrip().split(\",\")\n",
    "    return dict((k, v) for k, v in zip(keys, values))\n",
    "\n",
    "# kernel to get features from:\n",
    "kernel = \"\"\"\n",
    "__kernel void foobar(__global float *a, __global float *b, const int n) {\n",
    "    unsigned int i = get_global_id(0);\n",
    "    if (i < n)\n",
    "        b[i] += a[i] * 2; \n",
    "}\n",
    "\"\"\"\n",
    "    \n",
    "with open(\"../data/kernel.cl\", \"w\") as outfile:\n",
    "    print(kernel, file=outfile)\n",
    "\n",
    "DictTable(features_to_dict(\"../data/kernel.cl\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Benchmarks\n",
    "\n",
    "As in Grewe et al., we test our model on the NAS Parallel Benchmarks (NPB). We\n",
    "use the hand-optimized OpenCL implementation of Seo (2011). In Grewe et al. the\n",
    "authors augment the training set of the predictive model with 47 additional\n",
    "kernels taken from 4 GPGPU benchmark suites. To more fully sample the program\n",
    "space, we use a much larger collection of 142 programs. These additional\n",
    "programs are taken from all 7 of the most frequently used benchmark suites\n",
    "identified in the motivation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "256 benchmark kernels:\n",
      "  amd-app-sdk-3.0-BinomialOption-binomial_options\n",
      "  amd-app-sdk-3.0-BitonicSort-bitonicSort\n",
      "  amd-app-sdk-3.0-BlackScholes-blackScholes\n",
      "  amd-app-sdk-3.0-FastWalshTransform-fastWalshTransform\n",
      "  amd-app-sdk-3.0-FloydWarshall-floydWarshallPass\n",
      "  amd-app-sdk-3.0-MatrixMultiplication-mmmKernel\n",
      "  amd-app-sdk-3.0-MatrixTranspose-matrixTranspose\n",
      "  amd-app-sdk-3.0-PrefixSum-group_prefixSum\n",
      "  amd-app-sdk-3.0-Reduction-reduce\n",
      "  amd-app-sdk-3.0-ScanLargeArrays-ScanLargeArrays\n",
      "  amd-app-sdk-3.0-ScanLargeArrays-blockAddition\n",
      "  amd-app-sdk-3.0-ScanLargeArrays-prefixSum\n",
      "  amd-app-sdk-3.0-SimpleConvolution-simpleNonSeparableConvolution\n",
      "  amd-app-sdk-3.0-SimpleConvolution-simpleSeparableConvolutionPass1\n",
      "  amd-app-sdk-3.0-SimpleConvolution-simpleSeparableConvolutionPass2\n",
      "  amd-app-sdk-3.0-SobelFilter-sobel_filter\n",
      "  npb-3.3-BT-add\n",
      "  npb-3.3-BT-compute_rhs1\n",
      "  npb-3.3-BT-compute_rhs2\n",
      "  npb-3.3-BT-compute_rhs3\n",
      "  npb-3.3-BT-compute_rhs4\n",
      "  npb-3.3-BT-compute_rhs5\n",
      "  npb-3.3-BT-compute_rhs6\n",
      "  npb-3.3-BT-error_norm\n",
      "  npb-3.3-BT-exact_rhs1\n",
      "  npb-3.3-BT-exact_rhs2\n",
      "  npb-3.3-BT-exact_rhs3\n",
      "  npb-3.3-BT-exact_rhs4\n",
      "  npb-3.3-BT-exact_rhs5\n",
      "  npb-3.3-BT-initialize1\n",
      "  npb-3.3-BT-initialize2\n",
      "  npb-3.3-BT-initialize3\n",
      "  npb-3.3-BT-initialize4\n",
      "  npb-3.3-BT-initialize5\n",
      "  npb-3.3-BT-rhs_norm\n",
      "  npb-3.3-BT-x_solve\n",
      "  npb-3.3-BT-x_solve1\n",
      "  npb-3.3-BT-x_solve2\n",
      "  npb-3.3-BT-x_solve3\n",
      "  npb-3.3-BT-y_solve\n",
      "  npb-3.3-BT-y_solve1\n",
      "  npb-3.3-BT-y_solve2\n",
      "  npb-3.3-BT-y_solve3\n",
      "  npb-3.3-BT-z_solve\n",
      "  npb-3.3-BT-z_solve1\n",
      "  npb-3.3-BT-z_solve2\n",
      "  npb-3.3-BT-z_solve3\n",
      "  npb-3.3-CG-conj_grad_0\n",
      "  npb-3.3-CG-init_mem_0\n",
      "  npb-3.3-CG-init_mem_1\n",
      "  npb-3.3-CG-main_0\n",
      "  npb-3.3-CG-main_1\n",
      "  npb-3.3-CG-main_2\n",
      "  npb-3.3-CG-makea_0\n",
      "  npb-3.3-CG-makea_1\n",
      "  npb-3.3-CG-makea_2\n",
      "  npb-3.3-CG-makea_3\n",
      "  npb-3.3-CG-makea_4\n",
      "  npb-3.3-CG-makea_5\n",
      "  npb-3.3-CG-makea_6\n",
      "  npb-3.3-CG-makea_7\n",
      "  npb-3.3-EP-embar\n",
      "  npb-3.3-FT-cffts1\n",
      "  npb-3.3-FT-cffts2\n",
      "  npb-3.3-FT-cffts3\n",
      "  npb-3.3-FT-checksum\n",
      "  npb-3.3-FT-compute_indexmap\n",
      "  npb-3.3-FT-compute_initial_conditions\n",
      "  npb-3.3-FT-evolve\n",
      "  npb-3.3-FT-init_ui\n",
      "  npb-3.3-LU-blts\n",
      "  npb-3.3-LU-buts\n",
      "  npb-3.3-LU-erhs1\n",
      "  npb-3.3-LU-erhs2\n",
      "  npb-3.3-LU-erhs3\n",
      "  npb-3.3-LU-erhs4\n",
      "  npb-3.3-LU-error\n",
      "  npb-3.3-LU-l2norm\n",
      "  npb-3.3-LU-pintgr1\n",
      "  npb-3.3-LU-pintgr2\n",
      "  npb-3.3-LU-pintgr3\n",
      "  npb-3.3-LU-pintgr_reduce\n",
      "  npb-3.3-LU-rhs\n",
      "  npb-3.3-LU-rhsx\n",
      "  npb-3.3-LU-rhsy\n",
      "  npb-3.3-LU-rhsz\n",
      "  npb-3.3-LU-setbv1\n",
      "  npb-3.3-LU-setbv2\n",
      "  npb-3.3-LU-setbv3\n",
      "  npb-3.3-LU-setiv\n",
      "  npb-3.3-LU-ssor2\n",
      "  npb-3.3-LU-ssor3\n",
      "  npb-3.3-MG-kernel_comm3_1\n",
      "  npb-3.3-MG-kernel_comm3_2\n",
      "  npb-3.3-MG-kernel_comm3_3\n",
      "  npb-3.3-MG-kernel_interp_1\n",
      "  npb-3.3-MG-kernel_norm2u3\n",
      "  npb-3.3-MG-kernel_psinv\n",
      "  npb-3.3-MG-kernel_resid\n",
      "  npb-3.3-MG-kernel_rprj3\n",
      "  npb-3.3-MG-kernel_zero3\n",
      "  npb-3.3-MG-kernel_zran3_1\n",
      "  npb-3.3-MG-kernel_zran3_2\n",
      "  npb-3.3-MG-kernel_zran3_3\n",
      "  npb-3.3-SP-add\n",
      "  npb-3.3-SP-compute_rhs1\n",
      "  npb-3.3-SP-compute_rhs2\n",
      "  npb-3.3-SP-compute_rhs3\n",
      "  npb-3.3-SP-compute_rhs4\n",
      "  npb-3.3-SP-compute_rhs5\n",
      "  npb-3.3-SP-compute_rhs6\n",
      "  npb-3.3-SP-error_norm\n",
      "  npb-3.3-SP-exact_rhs1\n",
      "  npb-3.3-SP-exact_rhs2\n",
      "  npb-3.3-SP-exact_rhs3\n",
      "  npb-3.3-SP-exact_rhs4\n",
      "  npb-3.3-SP-exact_rhs5\n",
      "  npb-3.3-SP-initialize1\n",
      "  npb-3.3-SP-initialize2\n",
      "  npb-3.3-SP-initialize3\n",
      "  npb-3.3-SP-initialize4\n",
      "  npb-3.3-SP-initialize5\n",
      "  npb-3.3-SP-ninvr\n",
      "  npb-3.3-SP-pinvr\n",
      "  npb-3.3-SP-rhs_norm\n",
      "  npb-3.3-SP-txinvr\n",
      "  npb-3.3-SP-tzetar\n",
      "  npb-3.3-SP-x_solve\n",
      "  npb-3.3-SP-y_solve\n",
      "  npb-3.3-SP-z_solve\n",
      "  nvidia-4.2-DotProduct-DotProduct\n",
      "  nvidia-4.2-FDTD3d-FiniteDifferences\n",
      "  nvidia-4.2-MatVecMul-MatVecMulCoalesced0\n",
      "  nvidia-4.2-MatVecMul-MatVecMulCoalesced1\n",
      "  nvidia-4.2-MatVecMul-MatVecMulCoalesced2\n",
      "  nvidia-4.2-MatVecMul-MatVecMulCoalesced3\n",
      "  nvidia-4.2-MatVecMul-MatVecMulUncoalesced0\n",
      "  nvidia-4.2-MatVecMul-MatVecMulUncoalesced1\n",
      "  nvidia-4.2-MatrixMul-matrixMul\n",
      "  nvidia-4.2-MersenneTwister-BoxMuller\n",
      "  nvidia-4.2-MersenneTwister-MersenneTwister\n",
      "  nvidia-4.2-VectorAdd-VectorAdd\n",
      "  parboil-0.2-bfs-BFS_kernel\n",
      "  parboil-0.2-cutcp-opencl_cutoff_potential_lattice\n",
      "  parboil-0.2-lbm-performStreamCollide_kernel\n",
      "  parboil-0.2-sad-larger_sad_calc_16\n",
      "  parboil-0.2-sad-larger_sad_calc_8\n",
      "  parboil-0.2-sad-mb_sad_calc\n",
      "  parboil-0.2-spmv-A\n",
      "  parboil-0.2-stencil-naive_kernel\n",
      "  polybench-gpu-1.0-2DConvolution-Convolution2D_kernel\n",
      "  polybench-gpu-1.0-2mm-mm2_kernel1\n",
      "  polybench-gpu-1.0-2mm-mm2_kernel2\n",
      "  polybench-gpu-1.0-3DConvolution-Convolution3D_kernel\n",
      "  polybench-gpu-1.0-3mm-mm3_kernel1\n",
      "  polybench-gpu-1.0-3mm-mm3_kernel2\n",
      "  polybench-gpu-1.0-3mm-mm3_kernel3\n",
      "  polybench-gpu-1.0-atax-atax_kernel1\n",
      "  polybench-gpu-1.0-atax-atax_kernel2\n",
      "  polybench-gpu-1.0-bicg-bicgKernel1\n",
      "  polybench-gpu-1.0-bicg-bicgKernel2\n",
      "  polybench-gpu-1.0-correlation-corr_kernel\n",
      "  polybench-gpu-1.0-correlation-mean_kernel\n",
      "  polybench-gpu-1.0-correlation-reduce_kernel\n",
      "  polybench-gpu-1.0-correlation-std_kernel\n",
      "  polybench-gpu-1.0-covariance-covar_kernel\n",
      "  polybench-gpu-1.0-covariance-mean_kernel\n",
      "  polybench-gpu-1.0-covariance-reduce_kernel\n",
      "  polybench-gpu-1.0-gemm-gemm\n",
      "  polybench-gpu-1.0-gesummv-gesummv_kernel\n",
      "  polybench-gpu-1.0-gramschmidt-gramschmidt_kernel1\n",
      "  polybench-gpu-1.0-gramschmidt-gramschmidt_kernel2\n",
      "  polybench-gpu-1.0-gramschmidt-gramschmidt_kernel3\n",
      "  polybench-gpu-1.0-mvt-mvt_kernel1\n",
      "  polybench-gpu-1.0-mvt-mvt_kernel2\n",
      "  polybench-gpu-1.0-syr2k-syr2k_kernel\n",
      "  polybench-gpu-1.0-syrk-syrk_kernel\n",
      "  rodinia-3.1-backprop-bpnn_adjust_weights_ocl\n",
      "  rodinia-3.1-backprop-bpnn_layerforward_ocl\n",
      "  rodinia-3.1-bfs-BFS_1\n",
      "  rodinia-3.1-bfs-BFS_2\n",
      "  rodinia-3.1-cfd-compute_flux\n",
      "  rodinia-3.1-cfd-compute_step_factor\n",
      "  rodinia-3.1-cfd-initialize_variables\n",
      "  rodinia-3.1-cfd-memset_kernel\n",
      "  rodinia-3.1-cfd-time_step\n",
      "  rodinia-3.1-gaussian-Fan1\n",
      "  rodinia-3.1-gaussian-Fan2\n",
      "  rodinia-3.1-hotspot-hotspot\n",
      "  rodinia-3.1-kmeans-kmeans_kernel_c\n",
      "  rodinia-3.1-kmeans-kmeans_swap\n",
      "  rodinia-3.1-lavaMD-kernel_gpu_opencl\n",
      "  rodinia-3.1-leukocyte-GICOV_kernel\n",
      "  rodinia-3.1-leukocyte-IMGVF_kernel\n",
      "  rodinia-3.1-leukocyte-dilate_kernel\n",
      "  rodinia-3.1-lud-lud_diagonal\n",
      "  rodinia-3.1-lud-lud_internal\n",
      "  rodinia-3.1-lud-lud_perimeter\n",
      "  rodinia-3.1-nn-NearestNeighbor\n",
      "  rodinia-3.1-nw-nw_kernel1\n",
      "  rodinia-3.1-nw-nw_kernel2\n",
      "  rodinia-3.1-particlefilter-find_index_kernel\n",
      "  rodinia-3.1-particlefilter-likelihood_kernel\n",
      "  rodinia-3.1-particlefilter-normalize_weights_kernel\n",
      "  rodinia-3.1-particlefilter-sum_kernel\n",
      "  rodinia-3.1-pathfinder-dynproc_kernel\n",
      "  rodinia-3.1-streamcluster-memset_kernel\n",
      "  rodinia-3.1-streamcluster-pgain_kernel\n",
      "  shoc-1.1.5-BFS-BFS_kernel_warp\n",
      "  shoc-1.1.5-FFT-chk1D_512\n",
      "  shoc-1.1.5-FFT-fft1D_512\n",
      "  shoc-1.1.5-FFT-ifft1D_512\n",
      "  shoc-1.1.5-GEMM-sgemmNN\n",
      "  shoc-1.1.5-GEMM-sgemmNT\n",
      "  shoc-1.1.5-MD-compute_lj_force\n",
      "  shoc-1.1.5-MD5Hash-FindKeyWithDigest_Kernel\n",
      "  shoc-1.1.5-Reduction-reduce\n",
      "  shoc-1.1.5-S3D-gr_base\n",
      "  shoc-1.1.5-S3D-qssa2_kernel\n",
      "  shoc-1.1.5-S3D-qssa_kernel\n",
      "  shoc-1.1.5-S3D-qssab_kernel\n",
      "  shoc-1.1.5-S3D-ratt10_kernel\n",
      "  shoc-1.1.5-S3D-ratt2_kernel\n",
      "  shoc-1.1.5-S3D-ratt3_kernel\n",
      "  shoc-1.1.5-S3D-ratt4_kernel\n",
      "  shoc-1.1.5-S3D-ratt5_kernel\n",
      "  shoc-1.1.5-S3D-ratt6_kernel\n",
      "  shoc-1.1.5-S3D-ratt7_kernel\n",
      "  shoc-1.1.5-S3D-ratt8_kernel\n",
      "  shoc-1.1.5-S3D-ratt9_kernel\n",
      "  shoc-1.1.5-S3D-ratt_kernel\n",
      "  shoc-1.1.5-S3D-ratx2_kernel\n",
      "  shoc-1.1.5-S3D-ratx4_kernel\n",
      "  shoc-1.1.5-S3D-ratx_kernel\n",
      "  shoc-1.1.5-S3D-ratxb_kernel\n",
      "  shoc-1.1.5-S3D-rdsmh_kernel\n",
      "  shoc-1.1.5-S3D-rdwdot10_kernel\n",
      "  shoc-1.1.5-S3D-rdwdot2_kernel\n",
      "  shoc-1.1.5-S3D-rdwdot3_kernel\n",
      "  shoc-1.1.5-S3D-rdwdot6_kernel\n",
      "  shoc-1.1.5-S3D-rdwdot7_kernel\n",
      "  shoc-1.1.5-S3D-rdwdot8_kernel\n",
      "  shoc-1.1.5-S3D-rdwdot9_kernel\n",
      "  shoc-1.1.5-S3D-rdwdot_kernel\n",
      "  shoc-1.1.5-Scan-bottom_scan\n",
      "  shoc-1.1.5-Scan-reduce\n",
      "  shoc-1.1.5-Scan-top_scan\n",
      "  shoc-1.1.5-Sort-bottom_scan\n",
      "  shoc-1.1.5-Sort-reduce\n",
      "  shoc-1.1.5-Sort-top_scan\n",
      "  shoc-1.1.5-Spmv-spmv_csr_scalar_kernel\n",
      "  shoc-1.1.5-Spmv-spmv_csr_vector_kernel\n",
      "  shoc-1.1.5-Spmv-spmv_ellpackr_kernel\n",
      "  shoc-1.1.5-Stencil2D-CopyRect\n",
      "  shoc-1.1.5-Stencil2D-StencilKernel\n",
      "  shoc-1.1.5-Triad-Triad\n"
     ]
    }
   ],
   "source": [
    "benchmarks = sorted(set(amd_benchmarks[\"benchmark\"]))\n",
    "\n",
    "print(len(benchmarks), \"benchmark kernels:\")\n",
    "for benchmark in benchmarks:\n",
    "    print(\" \", benchmark)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We synthesized 1,000 kernels with CLgen to use as additional benchmarks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1001 CLgen kernels:\n",
      "  005ef03ac277e7418bb806ad623f5066de5031da.cl\n",
      "  00b2cd9176f8fd8ff41df5853ccc8ba77620d81f.cl\n",
      "  0101fc457f7bea5c550493f1c84afc5ca59c0db8.cl\n",
      "  011cb6eaff0d8431d380597d216780676df68344.cl\n",
      "  013bbfdef9395b6f598f54a7f699fec3c66a4ef1.cl\n",
      "  0145badf5ab7f25067ff7dfd29a5fe9500e6d084.cl\n",
      "  016850a54105db85a861274ce636cb826e85fd18.cl\n",
      "  016f294a014fc8fc8043fe264c36ebcf0e419309.cl\n",
      "  018beeb5fc6e4dc479cf1e8066396300461086b1.cl\n",
      "  019a0128d0c5fe33678335a7cf7050cad56bbae0.cl\n",
      "  02538b137813c54a4f3855406dc7fc3f6202a8b5.cl\n",
      "  026bd8a2758e37c22d6dd654b8fc29bd6343e16b.cl\n",
      "  0290dc84bc0dbf9da66cbb2063dd131c40fc72cc.cl\n",
      "  0325c19c2255bccaec91b7b6c01d60d602bb0506.cl\n",
      "  032c346c131d082394d8309653810498ae92ab2f.cl\n",
      "  0368bd6f883e6cbed4d5051a3ca04cd5d55b25ff.cl\n",
      "  03a9a8739001a8f6bf36e0174a5bc890e4e56181.cl\n",
      "  03b327c2d613cd448f4f1b8717ae32691d317c11.cl\n",
      "  042528ced29fb273e1a7002ca798c81bdebd7ade.cl\n",
      "  046615f0b47a769e4ca7384b859d0713dceeddce.cl\n",
      "  046a6b468106f6c7580b2928498aafe93e020c5f.cl\n",
      "  049a87070bab06278aea34e5b825fe1a38307764.cl\n",
      "  04de5682c54255d01500cd5e2c17cc65d1745d04.cl\n",
      "  050caf8366122275d07c3a2a788a69af6fbef83d.cl\n",
      "  051aab6a4b7097b4ffa5b95123a8c642f03bbd96.cl\n",
      "  053880507eb6fc2ed40d8979225ea74ea8ad71e8.cl\n",
      "  054408afc5bc3fe54255f049d4044e1a7066fb2d.cl\n",
      "  05812607c42186b6e3108dc11feb9f48ed7908c9.cl\n",
      "  05ac0aa2e8fd338dba0a8e56acb77c16aeafc293.cl\n",
      "  05b002f67e6120464b6ba01ce1979557c3e06ad4.cl\n",
      "  05f9da49391b169dacff8d1701d3529294a6fa5a.cl\n",
      "  0603539de69893971d8e9ba35c2fd6380c8dc25e.cl\n",
      "  06abe76c4bcc72960978769232f70b5e252457e4.cl\n",
      "  06fd3f17c5cdb4b8fa9d700e79d1b7155b250704.cl\n",
      "  07084b7fa7ea7b0763fcdc3abf5ead30cbbef492.cl\n",
      "  0743e00807734bbd32753b27c7f113cdef20f9c9.cl\n",
      "  074c7e3dba512a4c47220c4bdfec5a24e7652935.cl\n",
      "  0763772018a0bccd1325bb41eeabd6bed096a6ef.cl\n",
      "  07b0896864f3d446047307d30fa22e2d85583bec.cl\n",
      "  0815cf75122579976034583d77dc2df96c4daa3d.cl\n",
      "  0816d24fcccd8615b80fbc7b7693dfbdec6436b9.cl\n",
      "  0832a306da3f0b341ceba9ee1fe1c0b4d4f22246.cl\n",
      "  08839359b6dd18dc6d6d20fdc112647f0cdbd95b.cl\n",
      "  0894cacc41e0c431eb10bd8d94d8b74938f2d9c6.cl\n",
      "  091269cb5d5c9e48a7f4f3b62b8b9b637e72ca72.cl\n",
      "  092906c85b86187e8007c675667a087a26c64612.cl\n",
      "  096b0c459f0a731a0b7ecf58b634c996e2a85e72.cl\n",
      "  097e99486c4dc7754f0d8cfa7fb870a65ace18c9.cl\n",
      "  09daf6f964fb35eea6391360d726943bb588103b.cl\n",
      "  0a3d5703860f36e727f1023ce46856aac743bdc7.cl\n",
      "  0a722873f3c36a2425faad03d191755474d56369.cl\n",
      "  0a8d3ab97e3ec03208abc7f28cbcd4a13f126cc2.cl\n",
      "  0a8ed33a6ef084d2c9340a8888256c0a50ce753a.cl\n",
      "  0a9bd258202bee8f9febd6d36f87e6c2c6073515.cl\n",
      "  0ae2b1a4df0503ac1ac002820a5764a6be15d54a.cl\n",
      "  0ae6889bd441e29c73a9ed181d21907ea0001f1c.cl\n",
      "  0afe26c225707a18316faa955143263dbf2f0ed8.cl\n",
      "  0afe27100028eea11cd93d3b5a6ddd878232907e.cl\n",
      "  0b8378709bfad11440680a23164b2428ac273023.cl\n",
      "  0b959adc622ceeb8bba7d591fa883f846381b67b.cl\n",
      "  0bc6f4b166dc6a2b167d909bbde55261ba51fbf4.cl\n",
      "  0bf6082ae1b4a71b0940b3d3a32bca2a925ba1fa.cl\n",
      "  0c0d90126c9fa815f3e31f6b2e8ece9a944c2e7e.cl\n",
      "  0c0fd8af5b2faee0c6ead70353e90d5da583e258.cl\n",
      "  0c499b7009594fb7ac386a4518b4f673035ef5c6.cl\n",
      "  0c96133bafbaeecdbbb52089d908e6cde4a5bc02.cl\n",
      "  0cb3bc433962bda07b1df7a606897624e09b5ed0.cl\n",
      "  0dd5f3839d106a026a897d1daa84f2cada22fb36.cl\n",
      "  0dec230d2dadb5cad6febb249fd6d30f2bdb93f3.cl\n",
      "  0df6f8d549c980ce33710fb3a040d498119d67c9.cl\n",
      "  0e1e2c13d582f64f189c00586961fec4874cceb4.cl\n",
      "  0e2e92a0a7c2e789b1568a09b80dc6b18e4c9d01.cl\n",
      "  0e341d853869e634c5fb408807df7715ca9e0745.cl\n",
      "  0e4998f4046c5d3a535314e3d56f0ad08705375f.cl\n",
      "  0e99e608cd0dce43b464a81d7dc6979b01295df4.cl\n",
      "  0ed43e57432a3011ecb64071a05bb82efe61fd5e.cl\n",
      "  0eeab96485fb8d32ad685ae3373b015efc22c74f.cl\n",
      "  0f0ab95f2b892ed0265aa118138cec048541f3e5.cl\n",
      "  0f2b4492e1b802449bf1ee0e9d55f57069f60003.cl\n",
      "  0f467ce881e298cc0512aa69c88cdae407967054.cl\n",
      "  0fa40d8179584862095d19c05ed35871c72e2c89.cl\n",
      "  0fbc216ebcce9c168aae88f73d29e1a05da93d3c.cl\n",
      "  0fee943888f25ac9dcaf233f1f112155c8bbe0ae.cl\n",
      "  1023702485ec028b9dec15ccbc93aaaa887f27ec.cl\n",
      "  1061bd9e77d9d6a421fb2fe6698545b39bd8ce82.cl\n",
      "  106524a8d56499ad3634ef39188fa32f5da48546.cl\n",
      "  1072cc73037590af519c9d6e8e43b3aed6cecc70.cl\n",
      "  1080094dad2b9cd0e759030072d3fc05be0b349e.cl\n",
      "  10960275986b8e6fe4634048830d990204c8078f.cl\n",
      "  10c7f08da79a0cd3bd21cdde97230b97fba88b0e.cl\n",
      "  10ceb7769657ac3a1c0d95031b5377a6d5539286.cl\n",
      "  10f336abcf2ab99c7d6033d0a16fe31d9b79c482.cl\n",
      "  10f4999014804108aa9f88c6f4cbe110f8a41f95.cl\n",
      "  110e36a13d645c2914237e66deccff6a7c997fc6.cl\n",
      "  1111580a366d54027184376c255b1ab93772bd20.cl\n",
      "  1125e45df7f08b9106b6b02108de7f0d560e76ec.cl\n",
      "  113b2b8f1709189313c81e02e962e69fe9409105.cl\n",
      "  1179d149becb8ea0169bb276b55125e5267e47af.cl\n",
      "  11927e307f998eba97bdde3c160a62f38432c714.cl\n",
      "  11ab0a768a7baf0d1e5128b7552df5e7abfce355.cl\n",
      "  11c5cac1f4351b42d23b5402964544e3953a3998.cl\n",
      "  11d5450cc086206f2cc3b67e8ec16d190f747a72.cl\n",
      "  11e18656fb6974e0b2f7012513b395cd52a4fe79.cl\n",
      "  128f7af4868d24746cf370896c893b4122861ddd.cl\n",
      "  12a443257d25b8e667ac1330a321314b32174656.cl\n",
      "  12bce06d026d45b5752dfd3100f2f1546d5abcc8.cl\n",
      "  12ff754446f819158c6a4227b8cc60b6811bb764.cl\n",
      "  138f7130f6f95154a5ebe28b32d6878d1333faea.cl\n",
      "  13eb9874583a12876add866e786edf7ee5246345.cl\n",
      "  142a0164a855f2b29b47eed5537066464e11b466.cl\n",
      "  145801ea96442ac11ba308d8c97e9921673a07f3.cl\n",
      "  1458ad60ffa679bd56ee6a8728867aba4683c19d.cl\n",
      "  1485d0168a7e8c317292b51e6ef802f7027119f9.cl\n",
      "  14bd28d346d9432c91fb15baa70881142d2b0d3c.cl\n",
      "  14e0086470a08c1adcbb98976bbe1a177c6c6a5b.cl\n",
      "  14e4402918b6ab094936a50980050e4d70fb6464.cl\n",
      "  14f06050ee14d219d4b1d27650ee28041aedd4a1.cl\n",
      "  15199a5da9f074209db6014af94607ff129a71be.cl\n",
      "  151ab5912bb7760af7f0a5b5b3f73cece31ff724.cl\n",
      "  151b48cf4a90debe6dce21ed1bec50982586bcbf.cl\n",
      "  153f537b112d3ba57e166ec2e29dc3babe45ac55.cl\n",
      "  159fe8bd272295329a4b99b2067bfa812b10ce94.cl\n",
      "  15b1502fb4f9d642380b42d637d2bfaf0927e197.cl\n",
      "  15c3aecfecbcb6ff2881671d8341226724676df5.cl\n",
      "  15e73b9306a47ae66e7f2134a3db6adcf26af06a.cl\n",
      "  16098702ca28cf46e0ad182d4b9b7e5be2516b7d.cl\n",
      "  161f01fc5dd7ebd2a45100900ce5be684231733d.cl\n",
      "  162fcf7bf99ed080314ae2f13837fc944b55123a.cl\n",
      "  163a09d5c222504d1313df795a2feedd5d83adee.cl\n",
      "  16762e040d5ef87e04f31d118eb91619f1d2b613.cl\n",
      "  170ab6db3bbe1b36197ecca55ad0f5d1054a5a1f.cl\n",
      "  170dbc21405f34855550d9bcb35f51e63e31b33a.cl\n",
      "  17616769a00bbe915b034c928039552974dee411.cl\n",
      "  1769925f2e36379f4c5f536ec953d75590ec4c73.cl\n",
      "  1775f271e156c60308c5a4cba5826fe1968afb2e.cl\n",
      "  178cf959e9b991458fabb1edbd28cbf5666900c3.cl\n",
      "  17e72dca86e46fec4ba0353563d703656299b22e.cl\n",
      "  17f5986a4ab40cebfbf99853b824f00760cf9121.cl\n",
      "  181f0ff244e4a0423658422d1e08e4c27db7e0e7.cl\n",
      "  182d2ed5c98cb9f4688b554ed1fdc1e419ea5073.cl\n",
      "  1834a2e8bcfaeb9a22ee8558ed2f56fefe49e998.cl\n",
      "  1839618a50e1f330c3afa4c55069c445d14225b1.cl\n",
      "  18502444384be57e32b3b2fa17a2c04adb545175.cl\n",
      "  186d1ee0cab0b127668a9ef922b38933738b11d4.cl\n",
      "  186d381a259987ee0a6a32db4aa0e04559fe04b8.cl\n",
      "  187f1497c9b579871a2f6777a46c015a1d9030cc.cl\n",
      "  189a104f0c5979507a8af9e733b5e7cdbdf9c98c.cl\n",
      "  18b536b1a57a954e24c2c1c33d9f9241a9f98b52.cl\n",
      "  18c09feb4419bb4f596450939a856f7eccaf76d4.cl\n",
      "  18c2413c3f6d75aa8faa553734915a87d765b80e.cl\n",
      "  18d3fe01c0bb10b829c85d5b91eacfcaa5860aae.cl\n",
      "  18dc0a172f6d9a81398646257304cb2a625487f4.cl\n",
      "  18e3ad8a2925b0fec0aa41bddaa6efbe71cd52fc.cl\n",
      "  18f534d15cef15c776e7c114c3d6a552b8a094d8.cl\n",
      "  191a36333cfa7024dd82ea767caf81d202d7ecb8.cl\n",
      "  192b9f9ecc6b813f8c5bc58dd56d612ebbd27369.cl\n",
      "  192e2d84aa7f3f9324c75beea2119ca7029b3572.cl\n",
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      "  a670e8ff362eaae5eb70bcbaed23cd018fdaeb7b.cl\n",
      "  features.csv\n"
     ]
    }
   ],
   "source": [
    "clgen_kernels = fs.ls(\"../data/clgen-1000/kernels\")\n",
    "\n",
    "print(len(clgen_kernels), \"CLgen kernels:\")\n",
    "for kernel in clgen_kernels:\n",
    "    print(\" \", kernel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Platforms\n",
    "\n",
    "We evaluate our approach on two 64-bit CPU-GPU systems. One system has an AMD\n",
    "GPU and uses OpenSUSE 12.3; the other is equipped with an NVIDIA GPU and uses\n",
    "Ubuntu 16.04. Both platforms were unloaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Platform:   Linux\n",
      "Memory:     15954 MB\n",
      "\n",
      "Device:     CPU Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz\n",
      "Compute #.: 8\n",
      "Frequency:  3400 HZ\n",
      "Memory:     15954 MB\n",
      "Driver:     1.2.0.25\n",
      "\n",
      "Device:     GPU GeForce GTX TITAN\n",
      "Compute #.: 14\n",
      "Frequency:  980 HZ\n",
      "Memory:     6080 MB\n",
      "Driver:     367.57\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    import clgen.clutil\n",
    "    clgen.clutil.platform_info()\n",
    "except ImportError:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Datasets\n",
    "\n",
    "The NPB and Parboil benchmark suites are packaged with multiple datasets. We use\n",
    "all of the packaged datasets (5 per program in NPB, 1-4 per program in Parboil).\n",
    "For all other benchmarks, the default datasets are used. We configured the CLgen\n",
    "host driver to synthesize payloads between 128B-130MB, approximating that of the\n",
    "dataset sizes found in the benchmark programs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.2. Methodology\n",
    "\n",
    "We replicated the methodology of Grewe et al. Each experiment is repeated five\n",
    "times and the average execution time is recorded. The execution time includes\n",
    "both device compute time and the data transfer overheads.\n",
    "\n",
    "We use *leave-one-out cross-validatation* to evaluate predictive models. For\n",
    "each benchmark, a model is trained on data from all other benchmarks and used to\n",
    "predict the mapping for each kernel and dataset in the excluded program. We\n",
    "repeat this process with and without the addition of synthetic benchmarks in the\n",
    "training data. We do not test model prediction on synthetic benchmarks.\n",
    "\n",
    "Reproducing experiments on the AMD APP SDK benchmark suites:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mBuild amd-app-sdk-3.0 for CPU\u001b[m\n",
      "\u001b[1m\u001b[32mITERATION 1 of 10 amd-app-sdk-3.0 CPU\u001b[m ...\n",
      "\u001b[1m\u001b[32mITERATION 2 of 10 amd-app-sdk-3.0 CPU\u001b[m ...\n",
      "\u001b[1m\u001b[32mITERATION 3 of 10 amd-app-sdk-3.0 CPU\u001b[m ...\n",
      "\u001b[1m\u001b[32mITERATION 4 of 10 amd-app-sdk-3.0 CPU\u001b[m ...\n",
      "\u001b[1m\u001b[32mITERATION 5 of 10 amd-app-sdk-3.0 CPU\u001b[m ...\n",
      "\u001b[1m\u001b[32mITERATION 6 of 10 amd-app-sdk-3.0 CPU\u001b[m ...\n",
      "\u001b[1m\u001b[32mITERATION 7 of 10 amd-app-sdk-3.0 CPU\u001b[m ...\n",
      "\u001b[1m\u001b[32mITERATION 8 of 10 amd-app-sdk-3.0 CPU\u001b[m ...\n",
      "\u001b[1m\u001b[32mITERATION 9 of 10 amd-app-sdk-3.0 CPU\u001b[m ...\n",
      "\u001b[1m\u001b[32mITERATION 10 of 10 amd-app-sdk-3.0 CPU\u001b[m ...\n",
      "\u001b[32mBuild amd-app-sdk-3.0 for GPU\u001b[m\n",
      "\u001b[1m\u001b[32mITERATION 1 of 10 amd-app-sdk-3.0 GPU\u001b[m ...\n",
      "\u001b[1m\u001b[32mITERATION 2 of 10 amd-app-sdk-3.0 GPU\u001b[m ...\n",
      "\u001b[1m\u001b[32mITERATION 3 of 10 amd-app-sdk-3.0 GPU\u001b[m ...\n",
      "\u001b[1m\u001b[32mITERATION 4 of 10 amd-app-sdk-3.0 GPU\u001b[m ...\n",
      "\u001b[1m\u001b[32mITERATION 5 of 10 amd-app-sdk-3.0 GPU\u001b[m ...\n",
      "\u001b[1m\u001b[32mITERATION 6 of 10 amd-app-sdk-3.0 GPU\u001b[m ...\n",
      "\u001b[1m\u001b[32mITERATION 7 of 10 amd-app-sdk-3.0 GPU\u001b[m ...\n",
      "\u001b[1m\u001b[32mITERATION 8 of 10 amd-app-sdk-3.0 GPU\u001b[m ...\n",
      "\u001b[1m\u001b[32mITERATION 9 of 10 amd-app-sdk-3.0 GPU\u001b[m ...\n",
      "\u001b[1m\u001b[32mITERATION 10 of 10 amd-app-sdk-3.0 GPU\u001b[m ...\n",
      "done\n",
      "libcecl logs:\n",
      "    # files: 300\n",
      "    # runs: 10 (1: 30, 10: 30, 2: 30, 3: 30, 4: 30, 5: 30, 6: 30, 7: 30, 8: 30, 9: 30)\n",
      "    # devices: 2 (GPU: 150, CPU: 150)\n",
      "common subset:\n",
      "    # files: 300 (100.0%)\n",
      "    # common: 15\n",
      "    # devices: 2 (GPU: 150, CPU: 150)\n",
      "\n",
      "spawning 12 worker threads to process 150 jobs for GPU ...\n",
      "writing 'logs/GPU-dynamic.csv'\n",
      "spawning 12 worker threads to process 150 jobs for CPU ...\n",
      "writing 'logs/CPU-dynamic.csv'\n",
      "loading cpu results ... 17\n",
      "loading gpu results ... 17\n",
      "common results ... 17\n",
      "    # benchmarks ... 17\n",
      "    # datasets ... 1\n",
      "done\n",
      "completed 00:08:37 11/21/16\n"
     ]
    }
   ],
   "source": [
    "if can_reproduce_experiments():\n",
    "    !rm -f ../data/benchmarks/*.csv\n",
    "    !cd benchmarks && ./mkdata\n",
    "else:\n",
    "    print(\"platform does not have the requirements to run this experiment\", file=sys.stderr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0H3/8MdavX49Lly5h+fLlqF69uq6nTah27drB2dkZWq1WdywjIwM3b96Ej48PAMDb2xsP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s7OMDL6e39JNzc30WJiQUeSUVxcDJlMhl69eokdChERERG9BanmdSqVCsePHxc7DD1cckkGr6Cg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\nHRERERERkUT9D/pqy/fAUDWYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd868c50390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>benchmark</th>\n",
       "      <th>dataset</th>\n",
       "      <th>comp</th>\n",
       "      <th>rational</th>\n",
       "      <th>mem</th>\n",
       "      <th>localmem</th>\n",
       "      <th>coalesced</th>\n",
       "      <th>atomic</th>\n",
       "      <th>transfer</th>\n",
       "      <th>wgsize</th>\n",
       "      <th>F1:transfer/(comp+mem)</th>\n",
       "      <th>F2:coalesced/mem</th>\n",
       "      <th>F3:(localmem/mem)*avgws</th>\n",
       "      <th>F4:comp/mem</th>\n",
       "      <th>oracle</th>\n",
       "      <th>runtime</th>\n",
       "      <th>speedup</th>\n",
       "      <th>penalty</th>\n",
       "      <th>runtime_cpu</th>\n",
       "      <th>runtime_gpu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>amd-app-sdk-3.0-Reduction-reduce</td>\n",
       "      <td>default</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8208</td>\n",
       "      <td>256</td>\n",
       "      <td>631.384615</td>\n",
       "      <td>0.2860</td>\n",
       "      <td>146.285714</td>\n",
       "      <td>0.857</td>\n",
       "      <td>CPU</td>\n",
       "      <td>0.059780</td>\n",
       "      <td>5.34</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.059780</td>\n",
       "      <td>0.319085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>amd-app-sdk-3.0-BinomialOption-binomial_options</td>\n",
       "      <td>default</td>\n",
       "      <td>98</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2048</td>\n",
       "      <td>255</td>\n",
       "      <td>18.450450</td>\n",
       "      <td>0.1540</td>\n",
       "      <td>215.769231</td>\n",
       "      <td>7.540</td>\n",
       "      <td>GPU</td>\n",
       "      <td>0.199280</td>\n",
       "      <td>14.82</td>\n",
       "      <td>0.07</td>\n",
       "      <td>2.952338</td>\n",
       "      <td>0.199280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>amd-app-sdk-3.0-SimpleConvolution-simpleSepara...</td>\n",
       "      <td>default</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4206668</td>\n",
       "      <td>256</td>\n",
       "      <td>350555.666667</td>\n",
       "      <td>0.3330</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>CPU</td>\n",
       "      <td>1.108903</td>\n",
       "      <td>2.08</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1.108903</td>\n",
       "      <td>2.304333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>amd-app-sdk-3.0-FloydWarshall-floydWarshallPass</td>\n",
       "      <td>default</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>524288</td>\n",
       "      <td>256</td>\n",
       "      <td>32768.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.200</td>\n",
       "      <td>CPU</td>\n",
       "      <td>0.205475</td>\n",
       "      <td>3.04</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.205475</td>\n",
       "      <td>0.625134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>amd-app-sdk-3.0-MatrixTranspose-matrixTranspose</td>\n",
       "      <td>default</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>32768</td>\n",
       "      <td>256</td>\n",
       "      <td>564.965517</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>2.620</td>\n",
       "      <td>CPU</td>\n",
       "      <td>0.055525</td>\n",
       "      <td>1.77</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.055525</td>\n",
       "      <td>0.098051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>amd-app-sdk-3.0-ScanLargeArrays-prefixSum</td>\n",
       "      <td>default</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>263168</td>\n",
       "      <td>64</td>\n",
       "      <td>5263.360000</td>\n",
       "      <td>0.1580</td>\n",
       "      <td>43.789474</td>\n",
       "      <td>1.630</td>\n",
       "      <td>GPU</td>\n",
       "      <td>0.027667</td>\n",
       "      <td>1.42</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.039281</td>\n",
       "      <td>0.027667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>amd-app-sdk-3.0-BitonicSort-bitonicSort</td>\n",
       "      <td>default</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>131072</td>\n",
       "      <td>256</td>\n",
       "      <td>7281.777778</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>CPU</td>\n",
       "      <td>0.050971</td>\n",
       "      <td>1.41</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.050971</td>\n",
       "      <td>0.071969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>amd-app-sdk-3.0-MonteCarloAsian-calPriceVega_S...</td>\n",
       "      <td>default</td>\n",
       "      <td>154</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20971520</td>\n",
       "      <td>256</td>\n",
       "      <td>122640.467836</td>\n",
       "      <td>0.0588</td>\n",
       "      <td>180.705882</td>\n",
       "      <td>9.060</td>\n",
       "      <td>GPU</td>\n",
       "      <td>8.786853</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0.43</td>\n",
       "      <td>20.634580</td>\n",
       "      <td>8.786853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>amd-app-sdk-3.0-MatrixMultiplication-mmmKernel</td>\n",
       "      <td>default</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49152</td>\n",
       "      <td>64</td>\n",
       "      <td>280.868571</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.600</td>\n",
       "      <td>CPU</td>\n",
       "      <td>0.095313</td>\n",
       "      <td>3.17</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.095313</td>\n",
       "      <td>0.302550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>amd-app-sdk-3.0-PrefixSum-group_prefixSum</td>\n",
       "      <td>default</td>\n",
       "      <td>36</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4096</td>\n",
       "      <td>2560</td>\n",
       "      <td>85.333333</td>\n",
       "      <td>0.1670</td>\n",
       "      <td>1706.666667</td>\n",
       "      <td>3.000</td>\n",
       "      <td>GPU</td>\n",
       "      <td>0.059837</td>\n",
       "      <td>3.62</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.216853</td>\n",
       "      <td>0.059837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>amd-app-sdk-3.0-FastWalshTransform-fastWalshTr...</td>\n",
       "      <td>default</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4096</td>\n",
       "      <td>256</td>\n",
       "      <td>341.333333</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>CPU</td>\n",
       "      <td>0.070157</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.070157</td>\n",
       "      <td>0.073691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>amd-app-sdk-3.0-BlackScholes-blackScholes</td>\n",
       "      <td>default</td>\n",
       "      <td>220</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3145728</td>\n",
       "      <td>256</td>\n",
       "      <td>14106.403587</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>73.300</td>\n",
       "      <td>CPU</td>\n",
       "      <td>0.881157</td>\n",
       "      <td>2.52</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.881157</td>\n",
       "      <td>2.216768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>amd-app-sdk-3.0-SimpleConvolution-simpleSepara...</td>\n",
       "      <td>default</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4206668</td>\n",
       "      <td>256</td>\n",
       "      <td>420666.800000</td>\n",
       "      <td>0.3330</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.330</td>\n",
       "      <td>CPU</td>\n",
       "      <td>1.123802</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.123802</td>\n",
       "      <td>2.245626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>amd-app-sdk-3.0-SimpleConvolution-simpleNonSep...</td>\n",
       "      <td>default</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4206668</td>\n",
       "      <td>256</td>\n",
       "      <td>350555.666667</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>CPU</td>\n",
       "      <td>1.899387</td>\n",
       "      <td>1.21</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.899387</td>\n",
       "      <td>2.299702</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            benchmark  dataset  comp  \\\n",
       "0                    amd-app-sdk-3.0-Reduction-reduce  default     6   \n",
       "1     amd-app-sdk-3.0-BinomialOption-binomial_options  default    98   \n",
       "2   amd-app-sdk-3.0-SimpleConvolution-simpleSepara...  default     9   \n",
       "3     amd-app-sdk-3.0-FloydWarshall-floydWarshallPass  default    11   \n",
       "4     amd-app-sdk-3.0-MatrixTranspose-matrixTranspose  default    42   \n",
       "5           amd-app-sdk-3.0-ScanLargeArrays-prefixSum  default    31   \n",
       "6             amd-app-sdk-3.0-BitonicSort-bitonicSort  default    12   \n",
       "..                                                ...      ...   ...   \n",
       "10  amd-app-sdk-3.0-MonteCarloAsian-calPriceVega_S...  default   154   \n",
       "11     amd-app-sdk-3.0-MatrixMultiplication-mmmKernel  default   163   \n",
       "12          amd-app-sdk-3.0-PrefixSum-group_prefixSum  default    36   \n",
       "13  amd-app-sdk-3.0-FastWalshTransform-fastWalshTr...  default     8   \n",
       "14          amd-app-sdk-3.0-BlackScholes-blackScholes  default   220   \n",
       "15  amd-app-sdk-3.0-SimpleConvolution-simpleSepara...  default     7   \n",
       "16  amd-app-sdk-3.0-SimpleConvolution-simpleNonSep...  default     9   \n",
       "\n",
       "    rational  mem  localmem  coalesced  atomic  transfer  wgsize  \\\n",
       "0          3    7         4          2       0      8208     256   \n",
       "1          8   13        11          2       0      2048     255   \n",
       "2          3    3         0          1       0   4206668     256   \n",
       "3          1    5         0          0       0    524288     256   \n",
       "4          0   16         8          2       0     32768     256   \n",
       "5          3   19        13          3       0    263168      64   \n",
       "6          2    6         0          6       0    131072     256   \n",
       "..       ...  ...       ...        ...     ...       ...     ...   \n",
       "10        11   17        12          1       0  20971520     256   \n",
       "11         1   12         0          0       0     49152      64   \n",
       "12        10   12         8          2       0      4096    2560   \n",
       "13         0    4         0          4       0      4096     256   \n",
       "14         4    3         0          0       0   3145728     256   \n",
       "15         3    3         0          1       0   4206668     256   \n",
       "16         4    3         0          3       0   4206668     256   \n",
       "\n",
       "    F1:transfer/(comp+mem)  F2:coalesced/mem  F3:(localmem/mem)*avgws  \\\n",
       "0               631.384615            0.2860               146.285714   \n",
       "1                18.450450            0.1540               215.769231   \n",
       "2            350555.666667            0.3330                 0.000000   \n",
       "3             32768.000000            0.0000                 0.000000   \n",
       "4               564.965517            0.1250               128.000000   \n",
       "5              5263.360000            0.1580                43.789474   \n",
       "6              7281.777778            1.0000                 0.000000   \n",
       "..                     ...               ...                      ...   \n",
       "10           122640.467836            0.0588               180.705882   \n",
       "11              280.868571            0.0000                 0.000000   \n",
       "12               85.333333            0.1670              1706.666667   \n",
       "13              341.333333            1.0000                 0.000000   \n",
       "14            14106.403587            0.0000                 0.000000   \n",
       "15           420666.800000            0.3330                 0.000000   \n",
       "16           350555.666667            1.0000                 0.000000   \n",
       "\n",
       "    F4:comp/mem oracle   runtime  speedup  penalty  runtime_cpu  runtime_gpu  \n",
       "0         0.857    CPU  0.059780     5.34     0.19     0.059780     0.319085  \n",
       "1         7.540    GPU  0.199280    14.82     0.07     2.952338     0.199280  \n",
       "2         3.000    CPU  1.108903     2.08     0.48     1.108903     2.304333  \n",
       "3         2.200    CPU  0.205475     3.04     0.33     0.205475     0.625134  \n",
       "4         2.620    CPU  0.055525     1.77     0.57     0.055525     0.098051  \n",
       "5         1.630    GPU  0.027667     1.42     0.70     0.039281     0.027667  \n",
       "6         2.000    CPU  0.050971     1.41     0.71     0.050971     0.071969  \n",
       "..          ...    ...       ...      ...      ...          ...          ...  \n",
       "10        9.060    GPU  8.786853     2.35     0.43    20.634580     8.786853  \n",
       "11       13.600    CPU  0.095313     3.17     0.32     0.095313     0.302550  \n",
       "12        3.000    GPU  0.059837     3.62     0.28     0.216853     0.059837  \n",
       "13        2.000    CPU  0.070157     1.05     0.95     0.070157     0.073691  \n",
       "14       73.300    CPU  0.881157     2.52     0.40     0.881157     2.216768  \n",
       "15        2.330    CPU  1.123802     2.00     0.50     1.123802     2.245626  \n",
       "16        3.000    CPU  1.899387     1.21     0.83     1.899387     2.299702  \n",
       "\n",
       "[17 rows x 20 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = None\n",
    "if can_reproduce_experiments():\n",
    "    data = pd.read_csv(\"../data/benchmarks/training.csv\")\n",
    "    \n",
    "    ax = sns.barplot(x=\"benchmark\", y=\"speedup\", data=data)\n",
    "    plt.ylabel(\"Speedup best over worst mapping\")\n",
    "    plt.xlabel(\"AMD SDK Benchmark kernels\")\n",
    "    plt.axhline(y=1, color=\"k\", lw=1)  # speedup line\n",
    "    plt.setp(ax.get_xticklabels(), rotation=90)  # rotate x ticks\n",
    "    ax.set_xticklabels([shortbenchmark(x.get_text()) for x in ax.get_xticklabels()])\n",
    "    viz.finalise(figsize=(9,4))\n",
    "else:\n",
    "    print(\"no new data, skipped\", file=sys.stderr)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reproducing experiments on a 1% subset of CLgen kernels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building job list for ../../data/clgen-10/cpu.csv ... 254 jobs\n",
      "\u001b[1m\u001b[31m16-11-21 00:08:38 whz5\u001b[m job [1/255] 0.39% ... cpu 256 019a0128d0c5fe33678335a7cf7050cad56bbae0.cl\n",
      "\u001b[1m\u001b[31m16-11-21 00:08:48 whz5\u001b[m job [20/255] 7.84% ... cpu 4096 151b48cf4a90debe6dce21ed1bec50982586bcbf.cl\n",
      "\u001b[1m\u001b[31m16-11-21 00:08:59 whz5\u001b[m job [40/255] 15.69% ... cpu 262144 1de89a0610d53207c34254a635f13bdb8c7ba6d3.cl\n",
      "\u001b[1m\u001b[31m16-11-21 00:09:11 whz5\u001b[m job [60/255] 23.53% ... cpu 8388608 21aa71e015a7688c17007c226f92c76006480810.cl\n",
      "\u001b[1m\u001b[31m16-11-21 00:09:25 whz5\u001b[m job [80/255] 31.37% ... cpu 4096 39deb46abba298db227befd2afc435c522d18667.cl\n",
      "\u001b[1m\u001b[31m16-11-21 00:09:35 whz5\u001b[m job [100/255] 39.22% ... cpu 262144 5dc6cff63a01aef7707901b8a633ba3a7efda06a.cl\n",
      "\u001b[1m\u001b[31m16-11-21 00:09:46 whz5\u001b[m job [120/255] 47.06% ... cpu 8388608 6e903e54d0cac8224f4aec3e76fb440ab2ded815.cl\n",
      "\u001b[1m\u001b[31m16-11-21 00:09:57 whz5\u001b[m job [140/255] 54.90% ... cpu 4096 82f5b813eebddf1113303f3d3012dc47801e4fdf.cl\n",
      "\u001b[1m\u001b[31m16-11-21 00:10:08 whz5\u001b[m job [160/255] 62.75% ... cpu 262144 8b36efab73f8bd181d170a36faf6123dd67eda36.cl\n",
      "\u001b[1m\u001b[31m16-11-21 00:10:20 whz5\u001b[m job [180/255] 70.59% ... cpu 8388608 8dcaac8a2fb29825990e2f15f1878bdcbe8096f4.cl\n",
      "\u001b[1m\u001b[31m16-11-21 00:10:31 whz5\u001b[m job [200/255] 78.43% ... cpu 4096 91625993b16566bde25099105b8c142761b6601a.cl\n",
      "\u001b[1m\u001b[31m16-11-21 00:13:20 whz5\u001b[m job [220/255] 86.27% ... cpu 262144 933497ee1bf77e6f57fa06936c2211be9109d202.cl\n",
      "\u001b[1m\u001b[31m16-11-21 00:13:30 whz5\u001b[m job [240/255] 94.12% ... cpu 8388608 962364cd4b966b87acd1eecea790f4305c815cb8.cl\n",
      "\u001b[1m\u001b[31m16-11-21 00:13:38 whz5\u001b[m job [255/255] 100.00% ... cpu 8388608 9e72200ec991cd05cca51fd0eed70b62b8153521.cl\n",
      "building job list for ../../data/clgen-10/gpu.csv ... 254 jobs\n",
      "\u001b[1m\u001b[31m16-11-21 00:13:40 whz5\u001b[m job [1/255] 0.39% ... gpu 256 019a0128d0c5fe33678335a7cf7050cad56bbae0.cl\n",
      "\u001b[1m\u001b[31m16-11-21 00:13:56 whz5\u001b[m job [20/255] 7.84% ... gpu 4096 151b48cf4a90debe6dce21ed1bec50982586bcbf.cl\n",
      "\u001b[1m\u001b[31m16-11-21 00:14:12 whz5\u001b[m job [40/255] 15.69% ... gpu 262144 1de89a0610d53207c34254a635f13bdb8c7ba6d3.cl\n",
      "\u001b[1m\u001b[31m16-11-21 00:14:33 whz5\u001b[m job [60/255] 23.53% ... gpu 8388608 21aa71e015a7688c17007c226f92c76006480810.cl\n",
      "\u001b[1m\u001b[31m16-11-21 00:15:01 whz5\u001b[m job [80/255] 31.37% ... gpu 4096 39deb46abba298db227befd2afc435c522d18667.cl\n",
      "\u001b[1m\u001b[31m16-11-21 00:15:17 whz5\u001b[m job [100/255] 39.22% ... gpu 262144 5dc6cff63a01aef7707901b8a633ba3a7efda06a.cl\n",
      "\u001b[1m\u001b[31m16-11-21 00:15:32 whz5\u001b[m job [120/255] 47.06% ... gpu 8388608 6e903e54d0cac8224f4aec3e76fb440ab2ded815.cl\n",
      "\u001b[1m\u001b[31m16-11-21 00:15:51 whz5\u001b[m job [140/255] 54.90% ... gpu 4096 82f5b813eebddf1113303f3d3012dc47801e4fdf.cl\n",
      "\u001b[1m\u001b[31m16-11-21 00:16:09 whz5\u001b[m job [160/255] 62.75% ... gpu 262144 8b36efab73f8bd181d170a36faf6123dd67eda36.cl\n",
      "\u001b[1m\u001b[31m16-11-21 00:16:28 whz5\u001b[m job [180/255] 70.59% ... gpu 8388608 8dcaac8a2fb29825990e2f15f1878bdcbe8096f4.cl\n",
      "\u001b[1m\u001b[31m16-11-21 00:16:47 whz5\u001b[m job [200/255] 78.43% ... gpu 4096 91625993b16566bde25099105b8c142761b6601a.cl\n",
      "\u001b[1m\u001b[31m16-11-21 00:19:29 whz5\u001b[m job [220/255] 86.27% ... gpu 262144 933497ee1bf77e6f57fa06936c2211be9109d202.cl\n",
      "\u001b[1m\u001b[31m16-11-21 00:19:46 whz5\u001b[m job [240/255] 94.12% ... gpu 8388608 962364cd4b966b87acd1eecea790f4305c815cb8.cl\n",
      "\u001b[1m\u001b[31m16-11-21 00:20:00 whz5\u001b[m job [255/255] 100.00% ... gpu 8388608 9e72200ec991cd05cca51fd0eed70b62b8153521.cl\n",
      "loading kernel features ... 61584\n",
      "loading cpu results ... 248\n",
      "loading gpu results ... 245\n",
      "common results ... 242\n",
      "    # files ... 17\n",
      "    # kernels ... 17\n",
      "    # datasets ... 15\n",
      "done\n",
      "completed 00:20:05 11/21/16\n"
     ]
    }
   ],
   "source": [
    "if can_reproduce_experiments():\n",
    "    !rm -f ../data/clgen-10/*.csv\n",
    "    !cd bin && ./mkdata\n",
    "else:\n",
    "    print(\"platform does not have the requirements to run this experiment\", file=sys.stderr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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AutVrUZTKykodOHBAwcHBSkhIkNPp1PHjx72dDQAAAABwFoYLussuu0zJyckqLi7WNddc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\n3BpD8bFsVVT73vtwqPiwCvf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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd828037828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>benchmark</th>\n",
       "      <th>dataset</th>\n",
       "      <th>comp</th>\n",
       "      <th>rational</th>\n",
       "      <th>mem</th>\n",
       "      <th>localmem</th>\n",
       "      <th>coalesced</th>\n",
       "      <th>atomic</th>\n",
       "      <th>transfer</th>\n",
       "      <th>wgsize</th>\n",
       "      <th>...</th>\n",
       "      <th>speedup</th>\n",
       "      <th>penalty</th>\n",
       "      <th>runtime_cpu</th>\n",
       "      <th>ci_cpu</th>\n",
       "      <th>ci_mean_cpu</th>\n",
       "      <th>runtime_gpu</th>\n",
       "      <th>ci_gpu</th>\n",
       "      <th>ci_mean_gpu</th>\n",
       "      <th>kernel_nlines</th>\n",
       "      <th>kernel_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8dcaac8a2fb29825990e2f15f1878bdcbe8096f4.cl-A</td>\n",
       "      <td>131072</td>\n",
       "      <td>3</td>\n",
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       "      <td>4</td>\n",
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       "      <td>0</td>\n",
       "      <td>3145728</td>\n",
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       "      <td>...</td>\n",
       "      <td>6.60</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.161293</td>\n",
       "      <td>0.004020</td>\n",
       "      <td>0.024924</td>\n",
       "      <td>1.064342</td>\n",
       "      <td>0.000773</td>\n",
       "      <td>0.000726</td>\n",
       "      <td>15</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>962364cd4b966b87acd1eecea790f4305c815cb8.cl-A</td>\n",
       "      <td>2048</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>49152</td>\n",
       "      <td>256</td>\n",
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       "      <td>1.72</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.011555</td>\n",
       "      <td>0.001559</td>\n",
       "      <td>0.134920</td>\n",
       "      <td>0.019888</td>\n",
       "      <td>0.000767</td>\n",
       "      <td>0.038566</td>\n",
       "      <td>13</td>\n",
       "      <td>236</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>962364cd4b966b87acd1eecea790f4305c815cb8.cl-A</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6144</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>3.09</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.006656</td>\n",
       "      <td>0.001398</td>\n",
       "      <td>0.210036</td>\n",
       "      <td>0.020589</td>\n",
       "      <td>0.000894</td>\n",
       "      <td>0.043421</td>\n",
       "      <td>13</td>\n",
       "      <td>236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5dc6cff63a01aef7707901b8a633ba3a7efda06a.cl-A</td>\n",
       "      <td>16384</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>393216</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>4.02</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.030741</td>\n",
       "      <td>0.009388</td>\n",
       "      <td>0.305390</td>\n",
       "      <td>0.123565</td>\n",
       "      <td>0.001724</td>\n",
       "      <td>0.013952</td>\n",
       "      <td>15</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9e72200ec991cd05cca51fd0eed70b62b8153521.cl-A</td>\n",
       "      <td>16384</td>\n",
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       "      <td>2</td>\n",
       "      <td>7</td>\n",
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       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>393216</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.038831</td>\n",
       "      <td>0.012959</td>\n",
       "      <td>0.333728</td>\n",
       "      <td>0.124045</td>\n",
       "      <td>0.001745</td>\n",
       "      <td>0.014067</td>\n",
       "      <td>14</td>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>933497ee1bf77e6f57fa06936c2211be9109d202.cl-A</td>\n",
       "      <td>8388608</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>134217728</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>4.11</td>\n",
       "      <td>0.24</td>\n",
       "      <td>12.027560</td>\n",
       "      <td>0.074484</td>\n",
       "      <td>0.006193</td>\n",
       "      <td>49.397610</td>\n",
       "      <td>0.136228</td>\n",
       "      <td>0.002758</td>\n",
       "      <td>13</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8b36efab73f8bd181d170a36faf6123dd67eda36.cl-A</td>\n",
       "      <td>16384</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>393216</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>4.09</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.030349</td>\n",
       "      <td>0.007252</td>\n",
       "      <td>0.238954</td>\n",
       "      <td>0.124198</td>\n",
       "      <td>0.000994</td>\n",
       "      <td>0.008003</td>\n",
       "      <td>14</td>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>151b48cf4a90debe6dce21ed1bec50982586bcbf.cl-A</td>\n",
       "      <td>1024</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>24576</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.012903</td>\n",
       "      <td>0.005178</td>\n",
       "      <td>0.401302</td>\n",
       "      <td>0.017894</td>\n",
       "      <td>0.001114</td>\n",
       "      <td>0.062256</td>\n",
       "      <td>14</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>9e72200ec991cd05cca51fd0eed70b62b8153521.cl-A</td>\n",
       "      <td>2097152</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>50331648</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>3.44</td>\n",
       "      <td>0.29</td>\n",
       "      <td>5.282821</td>\n",
       "      <td>0.134342</td>\n",
       "      <td>0.025430</td>\n",
       "      <td>18.171021</td>\n",
       "      <td>0.055720</td>\n",
       "      <td>0.003066</td>\n",
       "      <td>14</td>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>9e72200ec991cd05cca51fd0eed70b62b8153521.cl-A</td>\n",
       "      <td>262144</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6291456</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>5.65</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.372485</td>\n",
       "      <td>0.022619</td>\n",
       "      <td>0.060725</td>\n",
       "      <td>2.104768</td>\n",
       "      <td>0.001079</td>\n",
       "      <td>0.000513</td>\n",
       "      <td>14</td>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>82f5b813eebddf1113303f3d3012dc47801e4fdf.cl-A</td>\n",
       "      <td>1048576</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>33554432</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>4.70</td>\n",
       "      <td>0.21</td>\n",
       "      <td>2.539115</td>\n",
       "      <td>0.006929</td>\n",
       "      <td>0.002729</td>\n",
       "      <td>11.927075</td>\n",
       "      <td>0.019830</td>\n",
       "      <td>0.001663</td>\n",
       "      <td>13</td>\n",
       "      <td>266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>019a0128d0c5fe33678335a7cf7050cad56bbae0.cl-A</td>\n",
       "      <td>1024</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>24576</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>1.22</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.015414</td>\n",
       "      <td>0.007740</td>\n",
       "      <td>0.502141</td>\n",
       "      <td>0.018806</td>\n",
       "      <td>0.000829</td>\n",
       "      <td>0.044082</td>\n",
       "      <td>23</td>\n",
       "      <td>374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>933497ee1bf77e6f57fa06936c2211be9109d202.cl-A</td>\n",
       "      <td>4194304</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>67108864</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>4.53</td>\n",
       "      <td>0.22</td>\n",
       "      <td>5.436517</td>\n",
       "      <td>0.109331</td>\n",
       "      <td>0.020110</td>\n",
       "      <td>24.646154</td>\n",
       "      <td>0.116577</td>\n",
       "      <td>0.004730</td>\n",
       "      <td>13</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>39deb46abba298db227befd2afc435c522d18667.cl-A</td>\n",
       "      <td>524288</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>12582912</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>3.44</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.224902</td>\n",
       "      <td>0.034503</td>\n",
       "      <td>0.028168</td>\n",
       "      <td>4.214330</td>\n",
       "      <td>0.000952</td>\n",
       "      <td>0.000226</td>\n",
       "      <td>14</td>\n",
       "      <td>268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>242 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         benchmark  dataset  comp  rational  \\\n",
       "0    8dcaac8a2fb29825990e2f15f1878bdcbe8096f4.cl-A   131072     3         2   \n",
       "1    962364cd4b966b87acd1eecea790f4305c815cb8.cl-A     2048     0         2   \n",
       "2    962364cd4b966b87acd1eecea790f4305c815cb8.cl-A      256     0         2   \n",
       "3    5dc6cff63a01aef7707901b8a633ba3a7efda06a.cl-A    16384     0         0   \n",
       "4    9e72200ec991cd05cca51fd0eed70b62b8153521.cl-A    16384     1         2   \n",
       "5    933497ee1bf77e6f57fa06936c2211be9109d202.cl-A  8388608     0         1   \n",
       "6    8b36efab73f8bd181d170a36faf6123dd67eda36.cl-A    16384     2         2   \n",
       "..                                             ...      ...   ...       ...   \n",
       "235  151b48cf4a90debe6dce21ed1bec50982586bcbf.cl-A     1024     2         2   \n",
       "236  9e72200ec991cd05cca51fd0eed70b62b8153521.cl-A  2097152     1         2   \n",
       "237  9e72200ec991cd05cca51fd0eed70b62b8153521.cl-A   262144     1         2   \n",
       "238  82f5b813eebddf1113303f3d3012dc47801e4fdf.cl-A  1048576     0         2   \n",
       "239  019a0128d0c5fe33678335a7cf7050cad56bbae0.cl-A     1024     7         5   \n",
       "240  933497ee1bf77e6f57fa06936c2211be9109d202.cl-A  4194304     0         1   \n",
       "241  39deb46abba298db227befd2afc435c522d18667.cl-A   524288     3         2   \n",
       "\n",
       "     mem  localmem  coalesced  atomic   transfer  wgsize     ...       \\\n",
       "0      4         0          3       0    3145728     256     ...        \n",
       "1      5         0          4       0      49152     256     ...        \n",
       "2      5         0          4       0       6144     256     ...        \n",
       "3      5         0          5       0     393216     256     ...        \n",
       "4      7         0          6       0     393216     256     ...        \n",
       "5      4         0          4       0  134217728     256     ...        \n",
       "6      6         0          6       0     393216     256     ...        \n",
       "..   ...       ...        ...     ...        ...     ...     ...        \n",
       "235    4         0          3       0      24576     256     ...        \n",
       "236    7         0          6       0   50331648     256     ...        \n",
       "237    7         0          6       0    6291456     256     ...        \n",
       "238    3         0          3       0   33554432     256     ...        \n",
       "239   16         0         15       0      24576     256     ...        \n",
       "240    4         0          4       0   67108864     256     ...        \n",
       "241    8         0          7       0   12582912     256     ...        \n",
       "\n",
       "     speedup  penalty  runtime_cpu    ci_cpu ci_mean_cpu  runtime_gpu  \\\n",
       "0       6.60     0.15     0.161293  0.004020    0.024924     1.064342   \n",
       "1       1.72     0.58     0.011555  0.001559    0.134920     0.019888   \n",
       "2       3.09     0.32     0.006656  0.001398    0.210036     0.020589   \n",
       "3       4.02     0.25     0.030741  0.009388    0.305390     0.123565   \n",
       "4       3.19     0.31     0.038831  0.012959    0.333728     0.124045   \n",
       "5       4.11     0.24    12.027560  0.074484    0.006193    49.397610   \n",
       "6       4.09     0.24     0.030349  0.007252    0.238954     0.124198   \n",
       "..       ...      ...          ...       ...         ...          ...   \n",
       "235     1.39     0.72     0.012903  0.005178    0.401302     0.017894   \n",
       "236     3.44     0.29     5.282821  0.134342    0.025430    18.171021   \n",
       "237     5.65     0.18     0.372485  0.022619    0.060725     2.104768   \n",
       "238     4.70     0.21     2.539115  0.006929    0.002729    11.927075   \n",
       "239     1.22     0.82     0.015414  0.007740    0.502141     0.018806   \n",
       "240     4.53     0.22     5.436517  0.109331    0.020110    24.646154   \n",
       "241     3.44     0.29     1.224902  0.034503    0.028168     4.214330   \n",
       "\n",
       "       ci_gpu  ci_mean_gpu  kernel_nlines  kernel_size  \n",
       "0    0.000773     0.000726             15          240  \n",
       "1    0.000767     0.038566             13          236  \n",
       "2    0.000894     0.043421             13          236  \n",
       "3    0.001724     0.013952             15          309  \n",
       "4    0.001745     0.014067             14          238  \n",
       "5    0.136228     0.002758             13          186  \n",
       "6    0.000994     0.008003             14          226  \n",
       "..        ...          ...            ...          ...  \n",
       "235  0.001114     0.062256             14          218  \n",
       "236  0.055720     0.003066             14          238  \n",
       "237  0.001079     0.000513             14          238  \n",
       "238  0.019830     0.001663             13          266  \n",
       "239  0.000829     0.044082             23          374  \n",
       "240  0.116577     0.004730             13          186  \n",
       "241  0.000952     0.000226             14          268  \n",
       "\n",
       "[242 rows x 26 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = None\n",
    "if can_reproduce_experiments():\n",
    "    data = pd.read_csv(\"../data/clgen-10/training.csv\")\n",
    "    \n",
    "    ax = sns.barplot(x=\"benchmark\", y=\"speedup\", ci=95, data=data)\n",
    "    plt.ylabel(\"Speedup (95% CI across datasets)\")\n",
    "    plt.xlabel(\"CLgen kernels\")\n",
    "    plt.axhline(y=1, color=\"k\", lw=1)  # speedup line\n",
    "    ax.set_xticklabels(range(1, len(data) + 1))\n",
    "    viz.finalise(figsize=(9,4))\n",
    "else:\n",
    "    print(\"no new data, skipped\", file=sys.stderr)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Experimental Results\n",
    "\n",
    "We evaluate the effectiveness of our approach on two heterogeneous systems. We\n",
    "first compare the performance of a state of the art predictive model Grewe et\n",
    "al. with and without the addition of synthetic benchmarks, then show how the\n",
    "synthetic benchmarks expose weaknesses in the model’s design and how these can\n",
    "be addressed to develop a better model. Finally we compare the ability of CLgen\n",
    "to explore the program feature space against a state of the art program\n",
    "generator.\n",
    "\n",
    "## 8.1. Performance Evaluation\n",
    "\n",
    "Performance data from 1,000 CLgen kernels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>benchmark</th>\n",
       "      <th>dataset</th>\n",
       "      <th>comp</th>\n",
       "      <th>rational</th>\n",
       "      <th>mem</th>\n",
       "      <th>localmem</th>\n",
       "      <th>coalesced</th>\n",
       "      <th>atomic</th>\n",
       "      <th>transfer</th>\n",
       "      <th>wgsize</th>\n",
       "      <th>...</th>\n",
       "      <th>speedup</th>\n",
       "      <th>penalty</th>\n",
       "      <th>runtime_cpu</th>\n",
       "      <th>ci_cpu</th>\n",
       "      <th>ci_mean_cpu</th>\n",
       "      <th>runtime_gpu</th>\n",
       "      <th>ci_gpu</th>\n",
       "      <th>ci_mean_gpu</th>\n",
       "      <th>kernel_nlines</th>\n",
       "      <th>kernel_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>005ef03ac277e7418bb806ad623f5066de5031da.cl-A</td>\n",
       "      <td>1024</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>24576</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.059825</td>\n",
       "      <td>0.027666</td>\n",
       "      <td>0.462449</td>\n",
       "      <td>0.046133</td>\n",
       "      <td>0.001591</td>\n",
       "      <td>0.034487</td>\n",
       "      <td>8</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>005ef03ac277e7418bb806ad623f5066de5031da.cl-A</td>\n",
       "      <td>1048576</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>25165824</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>1.46</td>\n",
       "      <td>0.68</td>\n",
       "      <td>3.144270</td>\n",
       "      <td>0.301743</td>\n",
       "      <td>0.095966</td>\n",
       "      <td>4.597703</td>\n",
       "      <td>0.821877</td>\n",
       "      <td>0.178758</td>\n",
       "      <td>8</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>005ef03ac277e7418bb806ad623f5066de5031da.cl-A</td>\n",
       "      <td>128</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3072</td>\n",
       "      <td>128</td>\n",
       "      <td>...</td>\n",
       "      <td>4.41</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.011245</td>\n",
       "      <td>0.007497</td>\n",
       "      <td>0.666696</td>\n",
       "      <td>0.049600</td>\n",
       "      <td>0.006548</td>\n",
       "      <td>0.132016</td>\n",
       "      <td>8</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>005ef03ac277e7418bb806ad623f5066de5031da.cl-A</td>\n",
       "      <td>131072</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3145728</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>2.12</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.339146</td>\n",
       "      <td>0.098638</td>\n",
       "      <td>0.290842</td>\n",
       "      <td>0.719252</td>\n",
       "      <td>0.261899</td>\n",
       "      <td>0.364127</td>\n",
       "      <td>8</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>005ef03ac277e7418bb806ad623f5066de5031da.cl-A</td>\n",
       "      <td>16384</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>393216</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.103308</td>\n",
       "      <td>0.002366</td>\n",
       "      <td>0.022902</td>\n",
       "      <td>0.096919</td>\n",
       "      <td>0.001569</td>\n",
       "      <td>0.016189</td>\n",
       "      <td>8</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>005ef03ac277e7418bb806ad623f5066de5031da.cl-A</td>\n",
       "      <td>2048</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>49152</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>1.51</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.072523</td>\n",
       "      <td>0.039571</td>\n",
       "      <td>0.545634</td>\n",
       "      <td>0.047970</td>\n",
       "      <td>0.001733</td>\n",
       "      <td>0.036127</td>\n",
       "      <td>8</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>005ef03ac277e7418bb806ad623f5066de5031da.cl-A</td>\n",
       "      <td>2097152</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>50331648</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>1.46</td>\n",
       "      <td>0.69</td>\n",
       "      <td>5.682239</td>\n",
       "      <td>1.689249</td>\n",
       "      <td>0.297286</td>\n",
       "      <td>8.287504</td>\n",
       "      <td>2.788553</td>\n",
       "      <td>0.336477</td>\n",
       "      <td>8</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15912</th>\n",
       "      <td>a670e8ff362eaae5eb70bcbaed23cd018fdaeb7b.cl-A</td>\n",
       "      <td>4096</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>131072</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>5.08</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.102624</td>\n",
       "      <td>0.153334</td>\n",
       "      <td>1.494134</td>\n",
       "      <td>0.020207</td>\n",
       "      <td>0.000497</td>\n",
       "      <td>0.024595</td>\n",
       "      <td>8</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15913</th>\n",
       "      <td>a670e8ff362eaae5eb70bcbaed23cd018fdaeb7b.cl-A</td>\n",
       "      <td>4194304</td>\n",
       "      <td>5</td>\n",
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       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>134217728</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>1.03</td>\n",
       "      <td>0.97</td>\n",
       "      <td>16.859569</td>\n",
       "      <td>2.117202</td>\n",
       "      <td>0.125579</td>\n",
       "      <td>16.314784</td>\n",
       "      <td>11.227493</td>\n",
       "      <td>0.688179</td>\n",
       "      <td>8</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15914</th>\n",
       "      <td>a670e8ff362eaae5eb70bcbaed23cd018fdaeb7b.cl-A</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>16384</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.018410</td>\n",
       "      <td>0.009306</td>\n",
       "      <td>0.505486</td>\n",
       "      <td>0.016919</td>\n",
       "      <td>0.000573</td>\n",
       "      <td>0.033867</td>\n",
       "      <td>8</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15915</th>\n",
       "      <td>a670e8ff362eaae5eb70bcbaed23cd018fdaeb7b.cl-A</td>\n",
       "      <td>524288</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>16777216</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>1.62</td>\n",
       "      <td>0.62</td>\n",
       "      <td>2.372446</td>\n",
       "      <td>0.681287</td>\n",
       "      <td>0.287166</td>\n",
       "      <td>3.836608</td>\n",
       "      <td>1.769274</td>\n",
       "      <td>0.461156</td>\n",
       "      <td>8</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15916</th>\n",
       "      <td>a670e8ff362eaae5eb70bcbaed23cd018fdaeb7b.cl-A</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2048</td>\n",
       "      <td>64</td>\n",
       "      <td>...</td>\n",
       "      <td>5.03</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.099202</td>\n",
       "      <td>0.232236</td>\n",
       "      <td>2.341042</td>\n",
       "      <td>0.019734</td>\n",
       "      <td>0.005711</td>\n",
       "      <td>0.289399</td>\n",
       "      <td>8</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15917</th>\n",
       "      <td>a670e8ff362eaae5eb70bcbaed23cd018fdaeb7b.cl-A</td>\n",
       "      <td>65536</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2097152</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>1.22</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.269124</td>\n",
       "      <td>0.099806</td>\n",
       "      <td>0.370855</td>\n",
       "      <td>0.327259</td>\n",
       "      <td>0.114561</td>\n",
       "      <td>0.350062</td>\n",
       "      <td>8</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15918</th>\n",
       "      <td>a670e8ff362eaae5eb70bcbaed23cd018fdaeb7b.cl-A</td>\n",
       "      <td>8192</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>262144</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>3.32</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.086840</td>\n",
       "      <td>0.008313</td>\n",
       "      <td>0.095728</td>\n",
       "      <td>0.026193</td>\n",
       "      <td>0.000642</td>\n",
       "      <td>0.024510</td>\n",
       "      <td>8</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15919 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           benchmark  dataset  comp  rational  \\\n",
       "0      005ef03ac277e7418bb806ad623f5066de5031da.cl-A     1024     2         2   \n",
       "1      005ef03ac277e7418bb806ad623f5066de5031da.cl-A  1048576     2         2   \n",
       "2      005ef03ac277e7418bb806ad623f5066de5031da.cl-A      128     2         2   \n",
       "3      005ef03ac277e7418bb806ad623f5066de5031da.cl-A   131072     2         2   \n",
       "4      005ef03ac277e7418bb806ad623f5066de5031da.cl-A    16384     2         2   \n",
       "5      005ef03ac277e7418bb806ad623f5066de5031da.cl-A     2048     2         2   \n",
       "6      005ef03ac277e7418bb806ad623f5066de5031da.cl-A  2097152     2         2   \n",
       "...                                              ...      ...   ...       ...   \n",
       "15912  a670e8ff362eaae5eb70bcbaed23cd018fdaeb7b.cl-A     4096     5         1   \n",
       "15913  a670e8ff362eaae5eb70bcbaed23cd018fdaeb7b.cl-A  4194304     5         1   \n",
       "15914  a670e8ff362eaae5eb70bcbaed23cd018fdaeb7b.cl-A      512     5         1   \n",
       "15915  a670e8ff362eaae5eb70bcbaed23cd018fdaeb7b.cl-A   524288     5         1   \n",
       "15916  a670e8ff362eaae5eb70bcbaed23cd018fdaeb7b.cl-A       64     5         1   \n",
       "15917  a670e8ff362eaae5eb70bcbaed23cd018fdaeb7b.cl-A    65536     5         1   \n",
       "15918  a670e8ff362eaae5eb70bcbaed23cd018fdaeb7b.cl-A     8192     5         1   \n",
       "\n",
       "       mem  localmem  coalesced  atomic   transfer  wgsize     ...       \\\n",
       "0        6         0          5       0      24576     256     ...        \n",
       "1        6         0          5       0   25165824     256     ...        \n",
       "2        6         0          5       0       3072     128     ...        \n",
       "3        6         0          5       0    3145728     256     ...        \n",
       "4        6         0          5       0     393216     256     ...        \n",
       "5        6         0          5       0      49152     256     ...        \n",
       "6        6         0          5       0   50331648     256     ...        \n",
       "...    ...       ...        ...     ...        ...     ...     ...        \n",
       "15912    7         0          6       0     131072     256     ...        \n",
       "15913    7         0          6       0  134217728     256     ...        \n",
       "15914    7         0          6       0      16384     256     ...        \n",
       "15915    7         0          6       0   16777216     256     ...        \n",
       "15916    7         0          6       0       2048      64     ...        \n",
       "15917    7         0          6       0    2097152     256     ...        \n",
       "15918    7         0          6       0     262144     256     ...        \n",
       "\n",
       "       speedup  penalty  runtime_cpu    ci_cpu ci_mean_cpu  runtime_gpu  \\\n",
       "0         1.30     0.77     0.059825  0.027666    0.462449     0.046133   \n",
       "1         1.46     0.68     3.144270  0.301743    0.095966     4.597703   \n",
       "2         4.41     0.23     0.011245  0.007497    0.666696     0.049600   \n",
       "3         2.12     0.47     0.339146  0.098638    0.290842     0.719252   \n",
       "4         1.07     0.94     0.103308  0.002366    0.022902     0.096919   \n",
       "5         1.51     0.66     0.072523  0.039571    0.545634     0.047970   \n",
       "6         1.46     0.69     5.682239  1.689249    0.297286     8.287504   \n",
       "...        ...      ...          ...       ...         ...          ...   \n",
       "15912     5.08     0.20     0.102624  0.153334    1.494134     0.020207   \n",
       "15913     1.03     0.97    16.859569  2.117202    0.125579    16.314784   \n",
       "15914     1.09     0.92     0.018410  0.009306    0.505486     0.016919   \n",
       "15915     1.62     0.62     2.372446  0.681287    0.287166     3.836608   \n",
       "15916     5.03     0.20     0.099202  0.232236    2.341042     0.019734   \n",
       "15917     1.22     0.82     0.269124  0.099806    0.370855     0.327259   \n",
       "15918     3.32     0.30     0.086840  0.008313    0.095728     0.026193   \n",
       "\n",
       "          ci_gpu  ci_mean_gpu  kernel_nlines  kernel_size  \n",
       "0       0.001591     0.034487              8          191  \n",
       "1       0.821877     0.178758              8          191  \n",
       "2       0.006548     0.132016              8          191  \n",
       "3       0.261899     0.364127              8          191  \n",
       "4       0.001569     0.016189              8          191  \n",
       "5       0.001733     0.036127              8          191  \n",
       "6       2.788553     0.336477              8          191  \n",
       "...          ...          ...            ...          ...  \n",
       "15912   0.000497     0.024595              8          222  \n",
       "15913  11.227493     0.688179              8          222  \n",
       "15914   0.000573     0.033867              8          222  \n",
       "15915   1.769274     0.461156              8          222  \n",
       "15916   0.005711     0.289399              8          222  \n",
       "15917   0.114561     0.350062              8          222  \n",
       "15918   0.000642     0.024510              8          222  \n",
       "\n",
       "[15919 rows x 26 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amd_clgen = pd.read_csv(\"../data/amd-clgen.csv\")\n",
    "amd_clgen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>benchmark</th>\n",
       "      <th>dataset</th>\n",
       "      <th>comp</th>\n",
       "      <th>rational</th>\n",
       "      <th>mem</th>\n",
       "      <th>localmem</th>\n",
       "      <th>coalesced</th>\n",
       "      <th>atomic</th>\n",
       "      <th>transfer</th>\n",
       "      <th>wgsize</th>\n",
       "      <th>...</th>\n",
       "      <th>speedup</th>\n",
       "      <th>penalty</th>\n",
       "      <th>runtime_cpu</th>\n",
       "      <th>ci_cpu</th>\n",
       "      <th>ci_mean_cpu</th>\n",
       "      <th>runtime_gpu</th>\n",
       "      <th>ci_gpu</th>\n",
       "      <th>ci_mean_gpu</th>\n",
       "      <th>kernel_nlines</th>\n",
       "      <th>kernel_size</th>\n",
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       "  </thead>\n",
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       "      <td>005ef03ac277e7418bb806ad623f5066de5031da.cl-A</td>\n",
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       "      <td>1.43</td>\n",
       "      <td>0.70</td>\n",
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       "      <td>0.016096</td>\n",
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       "      <td>191</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>005ef03ac277e7418bb806ad623f5066de5031da.cl-A</td>\n",
       "      <td>16384</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>393216</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.103308</td>\n",
       "      <td>0.002366</td>\n",
       "      <td>0.022902</td>\n",
       "      <td>0.111744</td>\n",
       "      <td>0.003750</td>\n",
       "      <td>0.033559</td>\n",
       "      <td>8</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>005ef03ac277e7418bb806ad623f5066de5031da.cl-A</td>\n",
       "      <td>2048</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>49152</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>3.77</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.072523</td>\n",
       "      <td>0.039571</td>\n",
       "      <td>0.545634</td>\n",
       "      <td>0.019219</td>\n",
       "      <td>0.004765</td>\n",
       "      <td>0.247932</td>\n",
       "      <td>8</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>005ef03ac277e7418bb806ad623f5066de5031da.cl-A</td>\n",
       "      <td>2097152</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>50331648</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>3.25</td>\n",
       "      <td>0.31</td>\n",
       "      <td>5.682239</td>\n",
       "      <td>1.689249</td>\n",
       "      <td>0.297286</td>\n",
       "      <td>18.494624</td>\n",
       "      <td>0.106484</td>\n",
       "      <td>0.005758</td>\n",
       "      <td>8</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15912</th>\n",
       "      <td>a670e8ff362eaae5eb70bcbaed23cd018fdaeb7b.cl-A</td>\n",
       "      <td>4096</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>131072</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>4.34</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.102624</td>\n",
       "      <td>0.153334</td>\n",
       "      <td>1.494134</td>\n",
       "      <td>0.023667</td>\n",
       "      <td>0.003586</td>\n",
       "      <td>0.151519</td>\n",
       "      <td>8</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15913</th>\n",
       "      <td>a670e8ff362eaae5eb70bcbaed23cd018fdaeb7b.cl-A</td>\n",
       "      <td>4194304</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>134217728</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>2.98</td>\n",
       "      <td>0.34</td>\n",
       "      <td>16.859569</td>\n",
       "      <td>2.117202</td>\n",
       "      <td>0.125579</td>\n",
       "      <td>50.267021</td>\n",
       "      <td>0.740593</td>\n",
       "      <td>0.014733</td>\n",
       "      <td>8</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15914</th>\n",
       "      <td>a670e8ff362eaae5eb70bcbaed23cd018fdaeb7b.cl-A</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>16384</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.018410</td>\n",
       "      <td>0.009306</td>\n",
       "      <td>0.505486</td>\n",
       "      <td>0.017414</td>\n",
       "      <td>0.002955</td>\n",
       "      <td>0.169691</td>\n",
       "      <td>8</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15915</th>\n",
       "      <td>a670e8ff362eaae5eb70bcbaed23cd018fdaeb7b.cl-A</td>\n",
       "      <td>524288</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>16777216</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>2.38</td>\n",
       "      <td>0.42</td>\n",
       "      <td>2.372446</td>\n",
       "      <td>0.681287</td>\n",
       "      <td>0.287166</td>\n",
       "      <td>5.653888</td>\n",
       "      <td>0.001324</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>8</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15916</th>\n",
       "      <td>a670e8ff362eaae5eb70bcbaed23cd018fdaeb7b.cl-A</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2048</td>\n",
       "      <td>64</td>\n",
       "      <td>...</td>\n",
       "      <td>5.90</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.099202</td>\n",
       "      <td>0.232236</td>\n",
       "      <td>2.341042</td>\n",
       "      <td>0.016806</td>\n",
       "      <td>0.003178</td>\n",
       "      <td>0.189099</td>\n",
       "      <td>8</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15917</th>\n",
       "      <td>a670e8ff362eaae5eb70bcbaed23cd018fdaeb7b.cl-A</td>\n",
       "      <td>65536</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2097152</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>2.68</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.269124</td>\n",
       "      <td>0.099806</td>\n",
       "      <td>0.370855</td>\n",
       "      <td>0.720154</td>\n",
       "      <td>0.002072</td>\n",
       "      <td>0.002877</td>\n",
       "      <td>8</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15918</th>\n",
       "      <td>a670e8ff362eaae5eb70bcbaed23cd018fdaeb7b.cl-A</td>\n",
       "      <td>8192</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>262144</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.086840</td>\n",
       "      <td>0.008313</td>\n",
       "      <td>0.095728</td>\n",
       "      <td>0.058579</td>\n",
       "      <td>0.003342</td>\n",
       "      <td>0.057051</td>\n",
       "      <td>8</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15919 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           benchmark  dataset  comp  rational  \\\n",
       "0      005ef03ac277e7418bb806ad623f5066de5031da.cl-A     1024     2         2   \n",
       "1      005ef03ac277e7418bb806ad623f5066de5031da.cl-A  1048576     2         2   \n",
       "2      005ef03ac277e7418bb806ad623f5066de5031da.cl-A      128     2         2   \n",
       "3      005ef03ac277e7418bb806ad623f5066de5031da.cl-A   131072     2         2   \n",
       "4      005ef03ac277e7418bb806ad623f5066de5031da.cl-A    16384     2         2   \n",
       "5      005ef03ac277e7418bb806ad623f5066de5031da.cl-A     2048     2         2   \n",
       "6      005ef03ac277e7418bb806ad623f5066de5031da.cl-A  2097152     2         2   \n",
       "...                                              ...      ...   ...       ...   \n",
       "15912  a670e8ff362eaae5eb70bcbaed23cd018fdaeb7b.cl-A     4096     5         1   \n",
       "15913  a670e8ff362eaae5eb70bcbaed23cd018fdaeb7b.cl-A  4194304     5         1   \n",
       "15914  a670e8ff362eaae5eb70bcbaed23cd018fdaeb7b.cl-A      512     5         1   \n",
       "15915  a670e8ff362eaae5eb70bcbaed23cd018fdaeb7b.cl-A   524288     5         1   \n",
       "15916  a670e8ff362eaae5eb70bcbaed23cd018fdaeb7b.cl-A       64     5         1   \n",
       "15917  a670e8ff362eaae5eb70bcbaed23cd018fdaeb7b.cl-A    65536     5         1   \n",
       "15918  a670e8ff362eaae5eb70bcbaed23cd018fdaeb7b.cl-A     8192     5         1   \n",
       "\n",
       "       mem  localmem  coalesced  atomic   transfer  wgsize     ...       \\\n",
       "0        6         0          5       0      24576     256     ...        \n",
       "1        6         0          5       0   25165824     256     ...        \n",
       "2        6         0          5       0       3072     128     ...        \n",
       "3        6         0          5       0    3145728     256     ...        \n",
       "4        6         0          5       0     393216     256     ...        \n",
       "5        6         0          5       0      49152     256     ...        \n",
       "6        6         0          5       0   50331648     256     ...        \n",
       "...    ...       ...        ...     ...        ...     ...     ...        \n",
       "15912    7         0          6       0     131072     256     ...        \n",
       "15913    7         0          6       0  134217728     256     ...        \n",
       "15914    7         0          6       0      16384     256     ...        \n",
       "15915    7         0          6       0   16777216     256     ...        \n",
       "15916    7         0          6       0       2048      64     ...        \n",
       "15917    7         0          6       0    2097152     256     ...        \n",
       "15918    7         0          6       0     262144     256     ...        \n",
       "\n",
       "       speedup  penalty  runtime_cpu    ci_cpu ci_mean_cpu  runtime_gpu  \\\n",
       "0         3.47     0.29     0.059825  0.027666    0.462449     0.017248   \n",
       "1         2.85     0.35     3.144270  0.301743    0.095966     8.950547   \n",
       "2         1.43     0.70     0.011245  0.007497    0.666696     0.016096   \n",
       "3         3.14     0.32     0.339146  0.098638    0.290842     1.063418   \n",
       "4         1.08     0.92     0.103308  0.002366    0.022902     0.111744   \n",
       "5         3.77     0.27     0.072523  0.039571    0.545634     0.019219   \n",
       "6         3.25     0.31     5.682239  1.689249    0.297286    18.494624   \n",
       "...        ...      ...          ...       ...         ...          ...   \n",
       "15912     4.34     0.23     0.102624  0.153334    1.494134     0.023667   \n",
       "15913     2.98     0.34    16.859569  2.117202    0.125579    50.267021   \n",
       "15914     1.06     0.95     0.018410  0.009306    0.505486     0.017414   \n",
       "15915     2.38     0.42     2.372446  0.681287    0.287166     5.653888   \n",
       "15916     5.90     0.17     0.099202  0.232236    2.341042     0.016806   \n",
       "15917     2.68     0.37     0.269124  0.099806    0.370855     0.720154   \n",
       "15918     1.48     0.67     0.086840  0.008313    0.095728     0.058579   \n",
       "\n",
       "         ci_gpu  ci_mean_gpu  kernel_nlines  kernel_size  \n",
       "0      0.003733     0.216431              8          191  \n",
       "1      0.010129     0.001132              8          191  \n",
       "2      0.003792     0.235586              8          191  \n",
       "3      0.000886     0.000833              8          191  \n",
       "4      0.003750     0.033559              8          191  \n",
       "5      0.004765     0.247932              8          191  \n",
       "6      0.106484     0.005758              8          191  \n",
       "...         ...          ...            ...          ...  \n",
       "15912  0.003586     0.151519              8          222  \n",
       "15913  0.740593     0.014733              8          222  \n",
       "15914  0.002955     0.169691              8          222  \n",
       "15915  0.001324     0.000234              8          222  \n",
       "15916  0.003178     0.189099              8          222  \n",
       "15917  0.002072     0.002877              8          222  \n",
       "15918  0.003342     0.057051              8          222  \n",
       "\n",
       "[15919 rows x 26 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nvidia_clgen = pd.read_csv(\"../data/nvidia-clgen.csv\")\n",
    "nvidia_clgen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 7:** Speedup of programs using *Grewe et al.* predictive model with and without synthetic benchmarks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results on AMD:\n",
      "  #. benchmarks:                   256 kernels, 680 observations\n",
      "  #. synthetic:                    1000 kernels, 15919 observations\n",
      "\n",
      "  ZeroR device:                    CPU\n",
      "\n",
      "  Speedup of Grewe et al.:         1.26 x\n",
      "  Speedup w. CLgen:                1.57 x\n"
     ]
    },
    {
     "data": {
      "image/png": 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UFGZmZgCAevXqITU1tVTBly5dWuzr3t7eqsfu7u5wd3cvVbmkmfzWXLMmZiWu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\nsH//fsTFxWHo0KE4ePCgpFeEGEM9cowDpPJDjq6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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd7f02b2e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results on NVIDIA:\n",
      "  #. benchmarks:                   256 kernels, 680 observations\n",
      "  #. synthetic:                    1000 kernels, 15919 observations\n",
      "\n",
      "  ZeroR device:                    GPU\n",
      "\n",
      "  Speedup of Grewe et al.:         2.50 x\n",
      "  Speedup w. CLgen:                3.26 x\n"
     ]
    },
    {
     "data": {
      "image/png": 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iIiLNKFu2LCwtLXH69Ol899O7FrqaNWti48aNBdq3MHcG8mrZ0jS5WuiIiEge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\nREREOur/A66TQlYkIWYzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd7f01f25c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def speedups_with_clgen(platform):\n",
    "    # datasets: B - benchmarks, S - synthetics, BS - benchmarks + synthetics:\n",
    "    B = pd.read_csv(\"../data/{platform}-benchmarks.csv\".format(platform=platform))\n",
    "    B[\"group\"] = [\"B\"] * len(B)\n",
    "\n",
    "    S = pd.read_csv(\"../data/{platform}-clgen.csv\".format(platform=platform))\n",
    "    S[\"group\"] = [\"S\"] * len(S)\n",
    "\n",
    "    BS = pd.concat((B, S))\n",
    "\n",
    "    # find the ZeroR. This is the device which is most frequently optimal\n",
    "    Bmask = B[B[\"benchmark\"].str.contains(\"npb-3.3-\")]\n",
    "    zeror = Counter(Bmask[\"oracle\"]).most_common(1)[0][0]\n",
    "    zeror_runtime = \"runtime_\" + zeror.lower()\n",
    "\n",
    "    # get the names of the benchmarks, in the form: $suite-$version-$benchmark\n",
    "    benchmark_names = sorted(set([\n",
    "        re.match(r\"^([^0-9]+-[0-9\\.]+-[^-]+)-\", b).group(1)\n",
    "        for b in B[\"benchmark\"] if b.startswith(\"npb-\")\n",
    "    ]))\n",
    "\n",
    "    B_out, BS_out = [], []\n",
    "    for benchmark in benchmark_names:\n",
    "        clf = cgo13.model()\n",
    "        features = get_cgo13_features\n",
    "        # cross validate on baseline\n",
    "        B_out += cgo13.leave_one_benchmark_out(clf, features, B, benchmark)\n",
    "        # reset model\n",
    "        clf = cgo13.model()\n",
    "        # repeate cross-validation with synthetic kernels\n",
    "        BS_out += cgo13.leave_one_benchmark_out(clf, features, BS, benchmark)\n",
    "\n",
    "    # create results frame\n",
    "    R_out = []\n",
    "    for b, bs in zip(B_out, BS_out):\n",
    "        # get runtimes of device using predicted device\n",
    "        b_p_runtime = b[\"runtime_\" + b[\"p\"].lower()]\n",
    "        bs_p_runtime = bs[\"runtime_\" + bs[\"p\"].lower()]\n",
    "\n",
    "        # speedup is the ratio of runtime using the predicted device\n",
    "        # over runtime using ZeroR device\n",
    "        b[\"p_speedup\"] = b_p_runtime / b[zeror_runtime]\n",
    "        bs[\"p_speedup\"] = bs_p_runtime / bs[zeror_runtime]\n",
    "\n",
    "        # get the group label, in the form $benchmark.$dataset\n",
    "        group = re.sub(r\"[^-]+-[0-9\\.]+-([^-]+)-.+\", r\"\\1\",\n",
    "                       b[\"benchmark\"]) + \".\" + b[\"dataset\"]\n",
    "        b[\"group\"] = group\n",
    "        bs[\"group\"] = group\n",
    "\n",
    "        # set the training data type\n",
    "        b[\"training\"] = \"Grewe et al.\"\n",
    "        bs[\"training\"] = \"w. CLgen\"\n",
    "\n",
    "        R_out.append(b)\n",
    "        R_out.append(bs)\n",
    "\n",
    "    R = pd.DataFrame(R_out)\n",
    "\n",
    "    b_mask = R[\"training\"] == \"Grewe et al.\"\n",
    "    bs_mask = R[\"training\"] == \"w. CLgen\"\n",
    "\n",
    "    B_speedup = labmath.mean(R[b_mask].groupby([\"group\"])[\"p_speedup\"].mean())\n",
    "    BS_speedup = labmath.mean(R[bs_mask].groupby([\"group\"])[\"p_speedup\"].mean())\n",
    "\n",
    "    print(\"Results on {}:\".format(platform.upper()))\n",
    "    print(\"  #. benchmarks:                  \",\n",
    "          len(set(B[\"benchmark\"])), \"kernels,\", len(B), \"observations\")\n",
    "    print(\"  #. synthetic:                   \",\n",
    "          len(set(S[\"benchmark\"])), \"kernels,\", len(S), \"observations\")\n",
    "    print()\n",
    "    print(\"  ZeroR device:                    {}\".format(zeror))\n",
    "    print()\n",
    "    print(\"  Speedup of Grewe et al.:         {:.2f} x\".format(B_speedup))\n",
    "    print(\"  Speedup w. CLgen:                {:.2f} x\".format(BS_speedup))\n",
    "\n",
    "    R = R.append({  # average bars\n",
    "        \"group\": \"Average\",\n",
    "        \"p_speedup\": B_speedup,\n",
    "        \"training\": \"Grewe et al.\"\n",
    "    }, ignore_index=True)\n",
    "    R = R.append({\n",
    "        \"group\": \"Average\",\n",
    "        \"p_speedup\": BS_speedup,\n",
    "        \"training\": \"w. CLgen\"\n",
    "    }, ignore_index=True)\n",
    "\n",
    "    R[\"p_speedup\"] -= 1  # negative offset so that bars start at 1\n",
    "\n",
    "    # colors\n",
    "    palette = sns.cubehelix_palette(len(set(R[\"training\"])),\n",
    "                                    rot=-.4, light=.85, dark=.35)\n",
    "\n",
    "    ax = sns.barplot(\n",
    "        x=\"group\", y=\"p_speedup\", data=R, ci=None, hue=\"training\",\n",
    "        palette=palette)\n",
    "    plt.ylabel(\"Speedup\")\n",
    "    plt.xlabel(\"\")\n",
    "\n",
    "    plt.axhline(y=0, color=\"k\", lw=1)  # speedup line\n",
    "    plt.axvline(x=plt.xlim()[1] - 1, color=\"k\", lw=1, linestyle=\"--\")  # average line\n",
    "\n",
    "    ax.get_legend().set_title(\"\")  # no legend title\n",
    "    plt.legend(loc='upper right')\n",
    "    ax.get_legend().draw_frame(True)\n",
    "\n",
    "    # plot shape and size\n",
    "    figsize = (9, 2.2)\n",
    "    if platform == \"nvidia\":\n",
    "        typecast = int; plt.ylim(-1, 16)\n",
    "    else:\n",
    "        typecast = float\n",
    "\n",
    "    # counter negative offset:\n",
    "    ax.set_yticklabels([typecast(i) + 1 for i in ax.get_yticks()])\n",
    "\n",
    "    plt.setp(ax.get_xticklabels(), rotation=90)\n",
    "\n",
    "    viz.finalise(figsize=figsize, tight=True)\n",
    "\n",
    "speedups_with_clgen(\"amd\")\n",
    "speedups_with_clgen(\"nvidia\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Speedups of the *Grewe et al. *predictive model over the NAS Parallel Benchmark\n",
    "suite with and without the addition of synthesized benchmarks for training.\n",
    "Speedups are calculated relative to the best single-device mapping for each\n",
    "experimental platform, which is CPU-only for AMD and GPU-only for NVIDIA. The\n",
    "fine grained coverage of the feature space which synthetic benchmarks provide\n",
    "improves performance dramatically for the NAS benchmarks. Across both systems,\n",
    "we achieve an average speedup of 2.42x with the addition of synthetic\n",
    "benchmarks, with prediction improvements over the baseline for 62.5% of\n",
    "benchmarks on AMD and 53.1% on NVIDIA.\n",
    "\n",
    "The strongest performance improvements are on NVIDIA with the `FT` benchmark, a\n",
    "benchmark which suffers greatly under a single-device mapping. However, the\n",
    "performance on AMD for the same benchmark slightly degrades after adding the\n",
    "synthetic benchmarks, which we address in the next section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Repeat this experiment on the small subset of data we produced earlier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results:\n",
      "  #. benchmarks:                   17 kernels, 17 observations\n",
      "  #. synthetic:                    17 kernels, 242 observations\n",
      "\n",
      "  ZeroR device:                    CPU\n",
      "\n",
      "  Speedup of Grewe et al.:         0.96 x\n",
      "  Speedup w. CLgen:                1.13 x\n"
     ]
    },
    {
     "data": {
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btm1r/PoatbCuX78eT58+xfLly2FgYICXL1/C29sbaWlpWL9+fY3DHD9+HMOGDSu3PSUl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\nhJUQQgghhHDt/wFCbsoc1Lm7fgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd7f025d400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if can_reproduce_experiments():\n",
    "    # datasets: B - benchmarks, S - synthetics, BS - benchmarks + synthetics:\n",
    "    B = pd.read_csv(\"../data/benchmarks/training.csv\")\n",
    "    B[\"group\"] = [\"B\"] * len(B)\n",
    "\n",
    "    S = pd.read_csv(\"../data/clgen-10/training.csv\")\n",
    "    S[\"group\"] = [\"S\"] * len(S)\n",
    "\n",
    "    BS = pd.concat((B, S))\n",
    "\n",
    "    # find the ZeroR. This is the device which is most frequently optimal\n",
    "    zeror = Counter(B[\"oracle\"]).most_common(1)[0][0]\n",
    "    zeror_runtime = \"runtime_\" + zeror.lower()\n",
    "\n",
    "    # get the names of the benchmarks, in the form: $suite-$version-$benchmark\n",
    "    benchmark_names = sorted(set([\n",
    "        re.match(r\"^([^0-9]+-[0-9\\.]+-[^-]+)-\", b).group(1)\n",
    "        for b in B[\"benchmark\"]\n",
    "    ]))\n",
    "\n",
    "    B_out, BS_out = [], []\n",
    "    for benchmark in benchmark_names:\n",
    "        clf = cgo13.model()\n",
    "        features = get_cgo13_features\n",
    "        # cross validate on baseline\n",
    "        B_out += cgo13.leave_one_benchmark_out(clf, features, B, benchmark)\n",
    "        # reset model\n",
    "        clf = cgo13.model()\n",
    "        # repeate cross-validation with synthetic kernels\n",
    "        BS_out += cgo13.leave_one_benchmark_out(clf, features, BS, benchmark)\n",
    "\n",
    "    # create results frame\n",
    "    R_out = []\n",
    "    for b, bs in zip(B_out, BS_out):\n",
    "        # get runtimes of device using predicted device\n",
    "        b_p_runtime = b[\"runtime_\" + b[\"p\"].lower()]\n",
    "        bs_p_runtime = bs[\"runtime_\" + bs[\"p\"].lower()]\n",
    "\n",
    "        # speedup is the ratio of runtime using the predicted device\n",
    "        # over runtime using ZeroR device\n",
    "        b[\"p_speedup\"] = b_p_runtime / b[zeror_runtime]\n",
    "        bs[\"p_speedup\"] = bs_p_runtime / bs[zeror_runtime]\n",
    "\n",
    "        # get the group label, in the form $benchmark.$dataset\n",
    "        group = escape_benchmark_name(b[\"benchmark\"])\n",
    "        b[\"group\"] = group\n",
    "        bs[\"group\"] = group\n",
    "\n",
    "        # set the training data type\n",
    "        b[\"training\"] = \"Grewe et al.\"\n",
    "        bs[\"training\"] = \"w. CLgen\"\n",
    "\n",
    "        R_out.append(b)\n",
    "        R_out.append(bs)\n",
    "\n",
    "    R = pd.DataFrame(R_out)\n",
    "    b_mask = R[\"training\"] == \"Grewe et al.\"\n",
    "    bs_mask = R[\"training\"] == \"w. CLgen\"\n",
    "\n",
    "    B_speedup = labmath.mean(R[b_mask].groupby([\"group\"])[\"p_speedup\"].mean())\n",
    "    BS_speedup = labmath.mean(R[bs_mask].groupby([\"group\"])[\"p_speedup\"].mean())\n",
    "\n",
    "    print(\"Results:\")\n",
    "    print(\"  #. benchmarks:                  \",\n",
    "          len(set(B[\"benchmark\"])), \"kernels,\", len(B), \"observations\")\n",
    "    print(\"  #. synthetic:                   \",\n",
    "          len(set(S[\"benchmark\"])), \"kernels,\", len(S), \"observations\")\n",
    "    print()\n",
    "    print(\"  ZeroR device:                    {}\".format(zeror))\n",
    "    print()\n",
    "    print(\"  Speedup of Grewe et al.:         {:.2f} x\".format(B_speedup))\n",
    "    print(\"  Speedup w. CLgen:                {:.2f} x\".format(BS_speedup))\n",
    "\n",
    "    R = R.append({  # average bars\n",
    "        \"group\": \"Average\",\n",
    "        \"p_speedup\": B_speedup,\n",
    "        \"training\": \"Grewe et al.\"\n",
    "    }, ignore_index=True)\n",
    "    R = R.append({\n",
    "        \"group\": \"Average\",\n",
    "        \"p_speedup\": BS_speedup,\n",
    "        \"training\": \"w. CLgen\"\n",
    "    }, ignore_index=True)\n",
    "\n",
    "    R[\"p_speedup\"] -= 1  # negative offset so that bars start at 1\n",
    "\n",
    "    # colors\n",
    "    palette = sns.cubehelix_palette(len(set(R[\"training\"])),\n",
    "                                    rot=-.4, light=.85, dark=.35)\n",
    "\n",
    "    ax = sns.barplot(\n",
    "        x=\"group\", y=\"p_speedup\", data=R, ci=None, hue=\"training\",\n",
    "        palette=palette)\n",
    "    plt.ylabel(\"Speedup\")\n",
    "    plt.xlabel(\"\")\n",
    "\n",
    "    plt.axhline(y=0, color=\"k\", lw=1)  # speedup line\n",
    "    plt.axvline(x=plt.xlim()[1] - 1, color=\"k\", lw=1, linestyle=\"--\")  # average line\n",
    "\n",
    "    ax.get_legend().set_title(\"\")  # no legend title\n",
    "    plt.legend(loc='upper right')\n",
    "    ax.get_legend().draw_frame(True)\n",
    "\n",
    "    # plot shape and size\n",
    "    figsize = (7, 3.2)\n",
    "    typecast = float\n",
    "\n",
    "    # counter negative offset:\n",
    "    ax.set_yticklabels([typecast(i) + 1 for i in ax.get_yticks()])\n",
    "\n",
    "    plt.setp(ax.get_xticklabels(), rotation=90)\n",
    "\n",
    "    viz.finalise(figsize=figsize, tight=True)\n",
    "else:\n",
    "    print(\"no new data, skipped\", file=sys.stderr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.3. Extending the Predictive Model\n",
    "\n",
    "Feature designers are bound to select as features only properties which are\n",
    "significant for the sparse benchmarks they test on, which can limit a model’s\n",
    "ability to generalize over a wider range of programs. We found this to be the\n",
    "case with the *Grewe et al.* model. The addition of automatically generated\n",
    "programs exposed two distinct cases where the model failed to generalize as a\n",
    "result of overspecializing to the NPB suite.\n",
    "\n",
    "The first case is that `F3` is sparse on many programs. This a result of the NPB\n",
    "implementation’s heavy exploitation of local memory buffers and the method by\n",
    "which they combined features (we speculate this was a necessary dimensionality\n",
    "reduction in the presence of sparse training programs). To counter this we\n",
    "extended the model to use the raw features in addition to the combined features.\n",
    "\n",
    "Distribution of feature values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ratio zero values feature 1: 0.006 %\n",
      "ratio zero values feature 2: 0.804 %\n",
      "ratio zero values feature 3: 99.497 %\n",
      "ratio zero values feature 4: 24.757 %\n"
     ]
    },
    {
     "data": {
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p0wEAPRt9HwAg0PyS0BUWFmrbtm0qLCxUVFRUmxjTpgMAeiL6PgBAMPBolsvc3FyVlZWp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\nAAAAAKBjQT3kEgAAAABwYSR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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd7f028a1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "F = [  # features\n",
    "    amd_clgen[\"F1:transfer/(comp+mem)\"],\n",
    "    amd_clgen[\"F2:coalesced/mem\"],\n",
    "    amd_clgen[\"F3:(localmem/mem)*avgws\"],\n",
    "    amd_clgen[\"F4:comp/mem\"]\n",
    "]\n",
    "nres = len(F[0])\n",
    "\n",
    "plt.subplots(1, 4)\n",
    "for i in range(len(F)):\n",
    "    plt.subplot(1, 4, i + 1)\n",
    "    plt.hist(F[i], bins=20)\n",
    "    plt.xlim(0,)\n",
    "    plt.xlabel(\"feature \" + str(i + 1))\n",
    "    nzeros = sum([1 for x in F[i] if x == 0])\n",
    "    print(\"ratio zero values feature {}: {:.3f} %\".format(i + 1, (nzeros / nres) * 100))\n",
    "\n",
    "viz.finalise(figsize=(9,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second case is that some of our generated programs had identical feature\n",
    "values as in the benchmark set, but had different *behavior* (i.e. optimal\n",
    "mappings):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of instances where CLgen kernels have identical features to benchmarks but different behaviour:\n",
      "{\n",
      "  \"npb-3.3-CG-conj_grad_0\": 1380,\n",
      "  \"npb-3.3-CG-init_mem_0\": 849,\n",
      "  \"npb-3.3-CG-init_mem_1\": 849,\n",
      "  \"npb-3.3-CG-main_0\": 126,\n",
      "  \"npb-3.3-CG-main_1\": 630,\n",
      "  \"npb-3.3-CG-main_2\": 395,\n",
      "  \"npb-3.3-FT-init_ui\": 928,\n",
      "  \"nvidia-4.2-MatVecMul-MatVecMulUncoalesced0\": 8,\n",
      "  \"nvidia-4.2-MatVecMul-MatVecMulUncoalesced1\": 8,\n",
      "  \"nvidia-4.2-VectorAdd-VectorAdd\": 370,\n",
      "  \"parboil-0.2-spmv-A\": 114,\n",
      "  \"polybench-gpu-1.0-atax-atax_kernel1\": 7,\n",
      "  \"polybench-gpu-1.0-atax-atax_kernel2\": 7,\n",
      "  \"polybench-gpu-1.0-bicg-bicgKernel1\": 110,\n",
      "  \"polybench-gpu-1.0-bicg-bicgKernel2\": 110,\n",
      "  \"polybench-gpu-1.0-mvt-mvt_kernel1\": 7,\n",
      "  \"polybench-gpu-1.0-mvt-mvt_kernel2\": 7,\n",
      "  \"rodinia-3.1-bfs-BFS_2\": 28,\n",
      "  \"rodinia-3.1-cfd-initialize_variables\": 8,\n",
      "  \"rodinia-3.1-cfd-memset_kernel\": 198,\n",
      "  \"rodinia-3.1-particlefilter-find_index_kernel\": 46,\n",
      "  \"rodinia-3.1-particlefilter-sum_kernel\": 250,\n",
      "  \"rodinia-3.1-streamcluster-memset_kernel\": 198,\n",
      "  \"shoc-1.1.5-Spmv-spmv_csr_scalar_kernel\": 402,\n",
      "  \"shoc-1.1.5-Spmv-spmv_ellpackr_kernel\": 38,\n",
      "  \"shoc-1.1.5-Triad-Triad\": 159\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "def same_features_different_behaviour(a, b, getfeatures):\n",
    "    \"\"\"returns true if features match but oracle device differs\"\"\"\n",
    "    af, bf = getfeatures(a), getfeatures(b)\n",
    "    ab, bb = a[\"oracle\"], b[\"oracle\"]\n",
    "    return ab != bb and np.array_equal(af, bf)\n",
    "\n",
    "print(\"Number of instances where CLgen kernels have identical features to benchmarks but different behaviour:\")\n",
    "matches, i, imax = defaultdict(int), 0, len(amd_benchmarks)\n",
    "for a in amd_benchmarks.to_dict('records'):\n",
    "    i += 1\n",
    "    print(\"\\r\", i, \"of\", imax, a[\"benchmark\"], end=\"\")\n",
    "    for b in amd_clgen.to_dict('records'):\n",
    "        if same_features_different_behaviour(a, b, cgo13.static_features):\n",
    "            matches[a[\"benchmark\"]] += 1\n",
    "\n",
    "print(\"\\r\" + clgen.format_json(matches))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We found this to be caused by the lack of discriminatory features for branching,\n",
    "since the NPB programs are implemented in a manner which aggressively minimized\n",
    "branching. To counter this we extended the predictive model with an additional\n",
    "feature with a static count of branching operations in a kernel.\n",
    "\n",
    "Speedups of our extended model across all seven of the benchmark suites used in Section 2:\n",
    "\n",
    "**Figure 8:** Speedups of predictions using our extended model over *Grewe et al.* on both experimental platforms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "analyzing 71 shoc-1.1.5-Triad\n",
      "  #. benchmarks:           256 kernels, 1360 observations\n",
      "  #. synthetic:            1000 kernels, 31838 observations\n",
      "\n",
      "  Speedup on AMD:          3.56 x\n",
      "  Speedup on NVIDIA:       5.04 x\n"
     ]
    },
    {
     "data": {
      "image/png": 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CAtjY2GDr1q2VxiGVShETEwNbW1tcv34d7dq1U1jPx8cHycnJ8PPzg4mJCTp37ow//vhD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\nlXM3ppUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd8681de908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def compare_clfs(clf1, get_features1, clf2, get_features2, D1, D2, benchmark):\n",
    "    \"\"\"cross-validate across all benchmarks using CGO13 model and our own, with\n",
    "    and without synthetic benchmarks. Report per-platform speedup of our model\n",
    "    over CGO13\"\"\"\n",
    "    test1_mask = D1[\"benchmark\"].str.contains(r\"^\" + benchmark)\n",
    "    test2_mask = D2[\"benchmark\"].str.contains(r\"^\" + benchmark)\n",
    "    assert(len(D1[test1_mask]) == len(D2[test2_mask]))\n",
    "\n",
    "    # create data masks. For training we exclude all results from benchmark\n",
    "    train1_mask = ~test1_mask\n",
    "    train2_mask = ~test2_mask\n",
    "\n",
    "    # create training and testing data\n",
    "    X1_train = get_features1(D1.loc[train1_mask])\n",
    "    X2_train = get_features2(D2.loc[train2_mask])\n",
    "    y1_train = cgo13.getlabels(D1[train1_mask])\n",
    "    y2_train = cgo13.getlabels(D2[train2_mask])\n",
    "\n",
    "    D1_test = D1[test1_mask]\n",
    "    D2_test = D2[test2_mask]\n",
    "    X1_test = get_features1(D1.loc[test1_mask])\n",
    "    X2_test = get_features2(D2.loc[test2_mask])\n",
    "    y1_test = cgo13.getlabels(D1_test)\n",
    "    y2_test = cgo13.getlabels(D2_test)\n",
    "\n",
    "    clf1.fit(X1_train, y1_train)  # train classifiers\n",
    "    clf2.fit(X2_train, y2_train)\n",
    "\n",
    "    predicted1 = clf1.predict(X1_test)  # make predictions\n",
    "    predicted2 = clf2.predict(X2_test)\n",
    "\n",
    "    D_out = []\n",
    "    for d, y, p1, p2 in zip(D1_test.to_dict('records'), y1_test,\n",
    "                            predicted1, predicted2):\n",
    "        d[\"p1\"], d[\"p2\"] = p1, p2\n",
    "        D_out.append(d)\n",
    "\n",
    "    return D_out  # return a list of dicts\n",
    "\n",
    "\n",
    "aB = pd.read_csv(\"../data/amd-benchmarks.csv\")\n",
    "aB[\"synthetic\"] = np.zeros(len(aB))\n",
    "bB = pd.read_csv(\"../data/nvidia-benchmarks.csv\")\n",
    "bB[\"synthetic\"] = np.zeros(len(bB))\n",
    "B = pd.concat((aB, bB))\n",
    "\n",
    "aS = pd.read_csv(\"../data/amd-clgen.csv\")\n",
    "aS[\"synthetic\"] = np.ones(len(aS))\n",
    "bS = pd.read_csv(\"../data/nvidia-clgen.csv\")\n",
    "bS[\"synthetic\"] = np.ones(len(bS))\n",
    "S = pd.concat((aS, bS))\n",
    "\n",
    "aBS = pd.concat((aB, aS))\n",
    "bBS = pd.concat((bB, bS))\n",
    "BS = pd.concat((B, S))\n",
    "\n",
    "assert(len(B) == len(aB) + len(bB))  # sanity checks\n",
    "assert(len(S) == len(aS) + len(bS))\n",
    "assert(len(BS) == len(aBS) + len(bBS))\n",
    "\n",
    "# get benchmark names: <suite>-<benchmark>\n",
    "benchmark_names = sorted(set([\n",
    "    re.match(r\"^([^0-9]+-[0-9\\.]+-[^-]+)\", b).group(1)\n",
    "    for b in B[\"benchmark\"]\n",
    "]))\n",
    "\n",
    "# perform cross-validation\n",
    "B_out = []\n",
    "for i, benchmark in enumerate(benchmark_names):\n",
    "    print(\"\\ranalyzing\", i + 1, benchmark, end=\"\")\n",
    "    cgo13_clf, our_clf = cgo13.model(), get_our_model()\n",
    "    cgo13_features, our_features = get_cgo13_features, get_our_features\n",
    "\n",
    "    # cross validate on Grewe et al. and our model\n",
    "    tmp = compare_clfs(cgo13_clf, cgo13_features, our_clf, our_features,\n",
    "                       aBS, aBS, benchmark)\n",
    "    for d in tmp: d[\"platform\"] = \"AMD Tahiti 7970\"\n",
    "    B_out += tmp\n",
    "\n",
    "    # reset models\n",
    "    cgo13_clf, our_clf = cgo13.model(), get_our_model()\n",
    "\n",
    "    # same as before, on other platform:\n",
    "    tmp = compare_clfs(cgo13_clf, cgo13_features, our_clf, our_features,\n",
    "                       bBS, bBS, benchmark)\n",
    "    for d in tmp: d[\"platform\"] = \"NVIDIA GTX 970\"\n",
    "    B_out += tmp\n",
    "print()\n",
    "\n",
    "# create results frame\n",
    "R_out = []\n",
    "# get runtimes of device using predicted device\n",
    "for b in B_out:\n",
    "    p1_runtime = b[\"runtime_\" + b[\"p1\"].lower()]\n",
    "    p2_runtime = b[\"runtime_\" + b[\"p2\"].lower()]\n",
    "\n",
    "    # speedup is the ratio of runtime using our predicted device\n",
    "    # over runtime using CGO13 predicted device.\n",
    "    b[\"p_speedup\"] = p2_runtime / p1_runtime\n",
    "\n",
    "    # get the benchmark name\n",
    "    b[\"group\"] = escape_benchmark_name(b[\"benchmark\"])\n",
    "\n",
    "    R_out.append(b)\n",
    "R = pd.DataFrame(R_out)\n",
    "\n",
    "improved = R[R[\"p_speedup\"] > 1]\n",
    "\n",
    "Amask = R[\"platform\"] == \"AMD Tahiti 7970\"\n",
    "Bmask = R[\"platform\"] == \"NVIDIA GTX 970\"\n",
    "a = R[Amask]\n",
    "b = R[Bmask]\n",
    "\n",
    "a_speedups = a.groupby([\"group\"])[\"p_speedup\"].mean()\n",
    "b_speedups = b.groupby([\"group\"])[\"p_speedup\"].mean()\n",
    "\n",
    "a_speedup = labmath.mean(a_speedups)\n",
    "b_speedup = labmath.mean(b_speedups)\n",
    "\n",
    "assert(len(R) == len(a) + len(b))  # sanity-check\n",
    "\n",
    "print(\"  #. benchmarks:          \",\n",
    "      len(set(B[\"benchmark\"])), \"kernels,\", len(B), \"observations\")\n",
    "print(\"  #. synthetic:           \",\n",
    "      len(set(S[\"benchmark\"])), \"kernels,\", len(S), \"observations\")\n",
    "print()\n",
    "print(\"  Speedup on AMD:          {:.2f} x\".format(a_speedup))\n",
    "print(\"  Speedup on NVIDIA:       {:.2f} x\".format(b_speedup))\n",
    "\n",
    "palette = sns.cubehelix_palette(\n",
    "    len(set(R[\"platform\"])), start=4, rot=.8, light=.8, dark=.3)\n",
    "\n",
    "R = R.append({  # average bars\n",
    "    \"group\": \"Average\",\n",
    "    \"p_speedup\": a_speedup,\n",
    "    \"platform\": \"AMD Tahiti 7970\"\n",
    "}, ignore_index=True)\n",
    "R = R.append({\n",
    "    \"group\": \"Average\",\n",
    "    \"p_speedup\": b_speedup,\n",
    "    \"platform\": \"NVIDIA GTX 970\"\n",
    "}, ignore_index=True)\n",
    "\n",
    "R[\"p_speedup\"] -= 1  # negative offset so that bars start at 1\n",
    "\n",
    "ax = sns.barplot(x=\"group\", y=\"p_speedup\", hue=\"platform\", data=R,\n",
    "                 palette=palette, ci=None)\n",
    "\n",
    "plt.ylabel(\"Speedup over Grewe et al.\"); plt.xlabel(\"\")\n",
    "\n",
    "plt.axhline(y=0, color=\"k\", lw=1)\n",
    "plt.axvline(x=plt.xlim()[1] - 1, color=\"k\", lw=1, linestyle=\"--\")\n",
    "plt.ylim(-1, 9)\n",
    "plt.setp(ax.get_xticklabels(), rotation=90)  # rotate x ticks\n",
    "ax.get_legend().set_title(\"\")  # legend\n",
    "plt.legend(loc='upper right')\n",
    "\n",
    "# counter negative offset\n",
    "ax.set_yticklabels([int(i) + 1 for i in ax.get_yticks()])\n",
    "\n",
    "ax.get_legend().draw_frame(True)\n",
    "\n",
    "viz.finalise(figsize=(9, 4), tight=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model performance, even on this tenfold increase of benchmarks, is good. There\n",
    "are three benchmarks on which the model performs poorly: `gemmv`, `hotspot`, and\n",
    "`pathfinder`. Each of those programs make heavy use of loops, which we believe\n",
    "the static code features of the model fail to capture. This could be addressed\n",
    "by extracting dynamic instruction counts using profiling, but we considered this\n",
    "beyond the scope of our work. It is not our goal to perfect the predictive\n",
    "model, but to show the performance improvements associated with training on\n",
    "synthetic programs. To this extent, we are successful, achieving average\n",
    "speedups of $3.56\\times$ on AMD and $5.04\\times$ on NVIDIA across a very large\n",
    "test set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Repeat this experiment using the small subset of experimental data we produced earlier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "analyzing 1 amd-app-sdk-3.0-BinomialOption\r",
      "analyzing 2 amd-app-sdk-3.0-BitonicSort\r",
      "analyzing 3 amd-app-sdk-3.0-BlackScholes\r",
      "analyzing 4 amd-app-sdk-3.0-FastWalshTransform\r",
      "analyzing 5 amd-app-sdk-3.0-FloydWarshall\r",
      "analyzing 6 amd-app-sdk-3.0-MatrixMultiplication\r",
      "analyzing 7 amd-app-sdk-3.0-MatrixTranspose\r",
      "analyzing 8 amd-app-sdk-3.0-MonteCarloAsian\r",
      "analyzing 9 amd-app-sdk-3.0-PrefixSum\r",
      "analyzing 10 amd-app-sdk-3.0-Reduction\r",
      "analyzing 11 amd-app-sdk-3.0-ScanLargeArrays\r",
      "analyzing 12 amd-app-sdk-3.0-SimpleConvolution\r",
      "analyzing 13 amd-app-sdk-3.0-SobelFilter\n",
      "  #. benchmarks:           17 kernels, 17 observations\n",
      "  #. synthetic:            17 kernels, 242 observations\n",
      "\n",
      "  Speedup:                 1.08 x\n"
     ]
    },
    {
     "data": {
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URBesgwYNwpw5c3D37l0AQFxcHLy9vWFnZ6f1cEREREREogvWr7/+GhYWFhg+fDhSU1Mx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\nMNcm2c5ZNA4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd7f03bf5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def compare_clfs(clf1, get_features1, clf2, get_features2, D1, D2, benchmark):\n",
    "    \"\"\"cross-validate across all benchmarks using CGO13 model and our own, with\n",
    "    and without synthetic benchmarks. Report per-platform speedup of our model\n",
    "    over CGO13\"\"\"\n",
    "    test1_mask = D1[\"benchmark\"].str.contains(r\"^\" + benchmark)\n",
    "    test2_mask = D2[\"benchmark\"].str.contains(r\"^\" + benchmark)\n",
    "    assert(len(D1[test1_mask]) == len(D2[test2_mask]))\n",
    "\n",
    "    # create data masks. For training we exclude all results from benchmark\n",
    "    train1_mask = ~test1_mask\n",
    "    train2_mask = ~test2_mask\n",
    "\n",
    "    # create training and testing data\n",
    "    X1_train = get_features1(D1.loc[train1_mask])\n",
    "    X2_train = get_features2(D2.loc[train2_mask])\n",
    "    y1_train = cgo13.getlabels(D1[train1_mask])\n",
    "    y2_train = cgo13.getlabels(D2[train2_mask])\n",
    "\n",
    "    D1_test = D1[test1_mask]\n",
    "    D2_test = D2[test2_mask]\n",
    "    X1_test = get_features1(D1.loc[test1_mask])\n",
    "    X2_test = get_features2(D2.loc[test2_mask])\n",
    "    y1_test = cgo13.getlabels(D1_test)\n",
    "    y2_test = cgo13.getlabels(D2_test)\n",
    "\n",
    "    clf1.fit(X1_train, y1_train)  # train classifiers\n",
    "    clf2.fit(X2_train, y2_train)\n",
    "\n",
    "    predicted1 = clf1.predict(X1_test)  # make predictions\n",
    "    predicted2 = clf2.predict(X2_test)\n",
    "\n",
    "    D_out = []\n",
    "    for d, y, p1, p2 in zip(D1_test.to_dict('records'), y1_test,\n",
    "                            predicted1, predicted2):\n",
    "        d[\"p1\"], d[\"p2\"] = p1, p2\n",
    "        D_out.append(d)\n",
    "\n",
    "    return D_out  # return a list of dicts\n",
    "\n",
    "if can_reproduce_experiments():\n",
    "    B = pd.read_csv(\"../data/benchmarks/training.csv\")\n",
    "    B[\"synthetic\"] = np.zeros(len(B))\n",
    "\n",
    "    S = pd.read_csv(\"../data/clgen-10/training.csv\")\n",
    "    S[\"synthetic\"] = np.ones(len(S))\n",
    "\n",
    "    BS = pd.concat((B, S))\n",
    "\n",
    "    assert(len(BS) == len(B) + len(S))\n",
    "\n",
    "    # get benchmark names: <suite>-<benchmark>\n",
    "    benchmark_names = sorted(set([\n",
    "        re.match(r\"^([^0-9]+-[0-9\\.]+-[^-]+)\", b).group(1)\n",
    "        for b in B[\"benchmark\"]\n",
    "    ]))\n",
    "\n",
    "    # perform cross-validation\n",
    "    B_out = []\n",
    "    for i, benchmark in enumerate(benchmark_names):\n",
    "        print(\"\\ranalyzing\", i + 1, benchmark, end=\"\")\n",
    "        cgo13_clf, our_clf = cgo13.model(), get_our_model()\n",
    "        cgo13_features, our_features = get_cgo13_features, get_our_features\n",
    "\n",
    "        # cross validate on Grewe et al. and our model\n",
    "        tmp = compare_clfs(cgo13_clf, cgo13_features, our_clf, our_features,\n",
    "                           BS, BS, benchmark)\n",
    "        B_out += tmp\n",
    "    print()\n",
    "\n",
    "    # create results frame\n",
    "    R_out = []\n",
    "    # get runtimes of device using predicted device\n",
    "    for b in B_out:\n",
    "        p1_runtime = b[\"runtime_\" + b[\"p1\"].lower()]\n",
    "        p2_runtime = b[\"runtime_\" + b[\"p2\"].lower()]\n",
    "\n",
    "        # speedup is the ratio of runtime using our predicted device\n",
    "        # over runtime using CGO13 predicted device.\n",
    "        b[\"p_speedup\"] = p2_runtime / p1_runtime\n",
    "\n",
    "        # get the benchmark name\n",
    "        b[\"group\"] = escape_benchmark_name(b[\"benchmark\"])\n",
    "\n",
    "        R_out.append(b)\n",
    "    R = pd.DataFrame(R_out)\n",
    "\n",
    "    improved = R[R[\"p_speedup\"] > 1]\n",
    "\n",
    "    speedups = R.groupby([\"group\"])[\"p_speedup\"].mean()\n",
    "    speedup = labmath.mean(speedups)\n",
    "\n",
    "    print(\"  #. benchmarks:          \",\n",
    "          len(set(B[\"benchmark\"])), \"kernels,\", len(B), \"observations\")\n",
    "    print(\"  #. synthetic:           \",\n",
    "          len(set(S[\"benchmark\"])), \"kernels,\", len(S), \"observations\")\n",
    "    print()\n",
    "    print(\"  Speedup:                 {:.2f} x\".format(speedup))\n",
    "\n",
    "    palette = sns.cubehelix_palette(1, start=4, rot=.8, light=.8, dark=.3)\n",
    "\n",
    "    R = R.append({  # average bar\n",
    "        \"group\": \"Average\",\n",
    "        \"p_speedup\": speedup\n",
    "    }, ignore_index=True)\n",
    "\n",
    "    R[\"p_speedup\"] -= 1  # negative offset so that bars start at 1\n",
    "\n",
    "    ax = sns.barplot(x=\"group\", y=\"p_speedup\", data=R,\n",
    "                     palette=palette, ci=None)\n",
    "\n",
    "    plt.ylabel(\"Speedup over Grewe et al.\"); plt.xlabel(\"\")\n",
    "\n",
    "    plt.axhline(y=0, color=\"k\", lw=1)\n",
    "    plt.axvline(x=plt.xlim()[1] - 1, color=\"k\", lw=1, linestyle=\"--\")\n",
    "    plt.setp(ax.get_xticklabels(), rotation=90)  # rotate x ticks\n",
    "\n",
    "    # counter negative offset\n",
    "    ax.set_yticklabels([int(i) + 1 for i in ax.get_yticks()])\n",
    "\n",
    "    viz.finalise(figsize=(7, 3.7), tight=True)\n",
    "else:\n",
    "    print(\"no new data, skipped\", file=sys.stderr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.3. Comparison of Source Features\n",
    "\n",
    "As demonstrated in Section 2, the predictive quality of a model for a given\n",
    "point in the feature space is improved with the addition of observations from\n",
    "neighboring points. By producing thousands of artificial programs modeled on the\n",
    "structure real OpenCL programs, CLgen is able to consistently and automatically\n",
    "generate programs which are close in the feature space to the benchmarks which\n",
    "we are testing on.\n",
    "\n",
    "To quantify this effect we use the static code features, plus the branching\n",
    "feature discussed in the previous subsection, to measure the number of CLgen\n",
    "kernels generated with the same feature values as those of the benchmarks we\n",
    "examined in the previous subsections. We examine only static code features to\n",
    "allow comparison with the GitHub kernels for which we have no automated method\n",
    "to execute them and extract runtime features, and CLSmith generated programs.\n",
    "\n",
    "Plotting the number of matches as a function of the number of kernels:\n",
    "\n",
    "**Figure 9:** The number of kernels from GitHub, CLSmith, and CLgen with static code features matching the benchmarks. CLgen generates kernels that are closer in the feature space than CLSmith, and can continue to do so long after we have exhausted the extent of the GitHub dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#. GitHub kernels  6500\n",
      "#. CLSmith kernels 10000\n",
      "#. CLgen kernels   10000\n",
      " CLgen 21 of 21"
     ]
    },
    {
     "data": {
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Mpk2bpqaq0mg0LF26lFmzZgF19yCtXLmSoqIi3Nzc2LhxI0OGDGm27S9/+YtF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\nVk0CmRBCCKsmgUwIIYRV+/+9eEgjvh7XLgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd8680ecb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def uniq_benchmarks(g):\n",
    "    \"\"\"return set of unique benchmarks, removes duplicates\"\"\"\n",
    "    uniq, out = set(), []\n",
    "    for row in g.to_dict('records'):\n",
    "        key = (row['benchmark'])\n",
    "        if key not in uniq:\n",
    "            out.append(row)\n",
    "            uniq.add(key)\n",
    "    return pd.DataFrame(out)\n",
    "\n",
    "np.random.seed(204)\n",
    "\n",
    "# load datasets\n",
    "CLgen = pd.read_csv(\"../data/clgen-features.csv\")\n",
    "GitHub = pd.read_csv(\"../data/github-features.csv\")\n",
    "CLSmith = pd.read_csv(\"../data/clsmith-features.csv\")\n",
    "\n",
    "# extract features\n",
    "Benchmarks_features = cgo13.get_static_features(uniq_benchmarks(amd_benchmarks))\n",
    "CLgen_features = cgo13.get_static_features(CLgen)\n",
    "GitHub_features = cgo13.get_static_features(GitHub)\n",
    "CLSmith_features = cgo13.get_static_features(CLSmith)\n",
    "\n",
    "stepMin, stepMax = 0, 10000\n",
    "linStepSize = int((stepMax - stepMin) / 20)\n",
    "closeness_threshold = 0\n",
    "\n",
    "steps = np.array(list(range(stepMin, stepMax + linStepSize, linStepSize)),\n",
    "                 dtype=np.int32)\n",
    "steps[0] = max(steps[0], 1)\n",
    "\n",
    "GitHubSteps = [n for n in steps if n <= len(GitHub_features)]\n",
    "CLSmithSteps = [n for n in steps if n <= len(CLSmith_features)]\n",
    "CLgenSteps = [n for n in steps if n <= len(CLgen_features)]\n",
    "\n",
    "numGitHubSteps = len(GitHubSteps)\n",
    "numCLSmithSteps = len(CLSmithSteps)\n",
    "numCLgenSteps = len(CLgenSteps)\n",
    "\n",
    "print(\"#. GitHub kernels \", GitHubSteps[-1])\n",
    "print(\"#. CLSmith kernels\", CLSmithSteps[-1])\n",
    "print(\"#. CLgen kernels  \", CLgenSteps[-1])\n",
    "\n",
    "datasets = [\n",
    "    {\n",
    "        \"name\": \"GitHub\",\n",
    "        \"color\": \"b\",\n",
    "        \"linestyle\": \":\",\n",
    "        \"steps\": GitHubSteps,\n",
    "        \"static_features\": GitHub_features,\n",
    "        \"distances\": np.zeros(numGitHubSteps, dtype=float),\n",
    "        \"errors\": np.zeros(numGitHubSteps, dtype=float)\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"CLSmith\",\n",
    "        \"color\": \"r\",\n",
    "        \"linestyle\": \"--\",\n",
    "        \"steps\": CLSmithSteps,\n",
    "        \"static_features\": CLSmith_features,\n",
    "        \"distances\": np.zeros(numCLSmithSteps, dtype=float),\n",
    "        \"errors\": np.zeros(numCLSmithSteps, dtype=float)\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"CLgen\",\n",
    "        \"color\": \"g\",\n",
    "        \"linestyle\": \"-\",\n",
    "        \"steps\": CLgenSteps,\n",
    "        \"static_features\": CLgen_features,\n",
    "        \"distances\": np.zeros(numCLgenSteps, dtype=float),\n",
    "        \"errors\": np.zeros(numCLgenSteps, dtype=float)\n",
    "    },\n",
    "]\n",
    "\n",
    "numIterations = 10  # repeat experiment N times\n",
    "for dataset in datasets:\n",
    "    for i, n in enumerate(dataset[\"steps\"]):\n",
    "        numSteps = len(dataset[\"steps\"])\n",
    "        print(\"\\r\", dataset[\"name\"], i + 1, \"of\", numSteps, end=\"\")\n",
    "        r = np.zeros(numIterations, dtype=float)\n",
    "        for j in range(numIterations):\n",
    "            np.random.shuffle(dataset[\"static_features\"])\n",
    "            distances = get_nearest_neighbour_distance(\n",
    "                dataset[\"static_features\"][:n], Benchmarks_features)\n",
    "            r[j] = sum([1 if d <= closeness_threshold else 0 for d in distances])\n",
    "        dataset[\"distances\"][i] = np.mean(r)\n",
    "        dataset[\"errors\"][i] = np.std(r)\n",
    "    plt.errorbar(dataset[\"steps\"], dataset[\"distances\"],\n",
    "                 yerr=dataset[\"errors\"], c=dataset[\"color\"],\n",
    "                 linestyle=dataset[\"linestyle\"],\n",
    "                 label=dataset[\"name\"], ecolor=\"k\", capthick=1)\n",
    "ax = plt.gca()\n",
    "\n",
    "plt.legend(loc='upper left')\n",
    "ax.get_legend().set_title(\"\")\n",
    "ax.get_legend().draw_frame(True)\n",
    "\n",
    "plt.xlim(stepMin, stepMax)\n",
    "plt.ylim(0,)\n",
    "plt.xlabel(\"#. kernels\"); plt.ylabel(\"#. matches\")\n",
    "\n",
    "viz.finalise(figsize=(4.5, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Out of 10,000 unique CLgen kernels, more than a third have static feature values\n",
    "matching those of the benchmarks, providing on average 14 CLgen kernels for each\n",
    "benchmark. This confirms our original intuition: CLgen kernels, by emulating the\n",
    "way real humans write OpenCL programs, are concentrated in the same area of the\n",
    "feature space as real programs. Moreover, the number of CLgen kernels we\n",
    "generate is unbounded, allowing us to continually refine the exploration of the\n",
    "feature space, while the number of kernels available on GitHub is finite.\n",
    "CLSmith rarely produces code similar to real-world OpenCL programs, with only\n",
    "0.53% of the generated kernels have matching feature values with benchmark\n",
    "kernels. We conclude that the unique contribution of CLgen is its ability to\n",
    "generate many thousands of programs *that are appropriate for predictive\n",
    "modeling*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Related Work\n",
    "\n",
    "See the PDF version of this paper.\n",
    "\n",
    "# 10. Conclusion\n",
    "\n",
    "The quality of predictive models is bound by the quantity and quality of\n",
    "programs used for training, yet there is typically only a few dozen common\n",
    "benchmarks available for experiments. We present a novel tool which is the first\n",
    "of it’s kind — an entirely probabilistic program generator capable of generating\n",
    "an unbounded number of human like programs. Our approach applies deep learning\n",
    "over a huge corpus of publicly available code from GitHub to automatically infer\n",
    "the semantics and practical usage of a programming language. Our tool generates\n",
    "programs which to trained eyes are indistinguishable from hand-written code. We\n",
    "tested our approach using a state of the art predictive model, improving its\n",
    "performance by a factor of 1.27x. We found that synthetic benchmarks exposed\n",
    "weaknesses in the feature set which, when corrected, further improved the\n",
    "performance by 2.66x. In future work we will extend CLgen to synthesize\n",
    "benchmarks in multiple programming languages, and investigate methods for\n",
    "performing an automatic directed search of the program space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation completed 2016-11-21 00:25:17\n"
     ]
    }
   ],
   "source": [
    "print(\"Evaluation completed\", time.nowstr())"
   ]
  }
 ],
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   "display_name": "CLgen",
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