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Omnitrace docs refactoring (ROCm#353)
* Add Sphinx and Read the Docs configs * Add documentation workflow configurations * Changed macros verbprintf and verbprintf_bare so they write to stdout… (ROCm#346) Flush stdout when listing keys + bump verbose level for GPU count * Removing static version asserts. (ROCm#347) It is causing failures on our internal builds Signed-off-by: David Galiffi <[email protected]> * Check for an empty vector before popping (ROCm#350) Protect from possible seg. fault Signed-off-by: David Galiffi <[email protected]> * Add release links to installation.md (ROCm#351) * Initial infrastructure rework for Omnitrace refactoring and a rewrite of the What is file * Add files in conceptual section, along with images and infrastructure changes. * Formatting and style fixes for files in conceptual directory * Add quick start install guide and fix spelling errors in other files * Add install document and fix code tags. Infrastructure changes * Add two how-to guides along with infra changes and spelling fixes * Add two new how to files and fix errors in the last commit * Fix spelling mistakes * Add new how to file on causal profiling and infra changes. * Add how to file on interpreting Omnitrace output, fixes, and images * Add remaining how-to guides and reference materials along with fixes and infrastructure * Add YouTube file and fix spelling and formatting * Fix a few loose ends and add link to license page * Add Sphinx and Doxygen infrastructure and some additional corrections * Update rocm-docs-core * Fix Doxyfile * Fix path to API header files * Run doxysphinx in conf.py * Add back custom css for doxygen * Remove doxygenlayout * Add api to toc * Update Doxyfile Generate from source .in * Proofreading edits and other changes * Add .gitignore for Doxygen and remove deprecated words and typos * Fix one additional typo * Turn off dot * Update doxyfile strip from path * Workflow, submodules, and thread info Updates (ROCm#352) * Update CI workflows - use node20 workflow packages * Update tests/source/CMakeLists.txt - Use OMNITRACE_TRACE and OMNTRACE_PROFILE instead of perfetto/timemory * Update timemory submodule - argparse: requires -> required - parse callbacks * Update thread_info.cpp - fix causal::delay::get_local usage * Update timemory submodule * Update kokkos submodule - release 3.7.02 * Revert opensuse.yml and ubuntu-bionic.yml to use node16 workflows * Update docs.yml * ROCm 6.1 Installers (ROCm#349) * Add ROCm 6.1 to packages * Bump version to 1.11.3 * Add 6.1 support to the docker build support. Simplified this by adding 6.* to case statements, now that repo links have been standardized. * Update timemory submodule (ROCm#354) - fix argparse::argument::required template deduction * Build omnitrace-rt library (ROCm#355) * Build omnitrace-rt library - Explicitly build dyninstAPI_RT as omnitrace-rt so that the SONAME in the ELF is omnitrace-rt instead of dyninstAPI_RT - Create symbolic link lib/omnitrace/libdyninstAPI_RT.so which points to lib/libomnitrace-rt.so - Simplify build tree location of libomnitrace-rt.so since it is ../lib from the bin directory even in the build tree - Update dyninst submodule with minor tweaks to dyninstAPI_RT/CMakeLists.txt * Update source/lib/omnitrace-rt/cmake/platform.cmake * Use ftpmirror.gnu.org instead of ftp.gnu.org - in timemory and dyninst submodules - minor .clang-tidy tweak * Executables append omnitrace library directory to LD_LIBRARY_PATH (ROCm#356) - omnitrace-run, omnitrace-sample, and omnitrace-causal now automatically append the LD_LIBRARY_PATH with the directory containing the omnitrace libraries - this helps ensure that binary rewritten exes can resolve omnitrace-rt library location * Fix a few typos and formatting issues * Additional fixes and minor formatting changes. * More fixes and minor formatting changes. * Complete second proofreading with fixes and minor formatting changes. * Make changes to table of contents and disable linting * Update links in the README doc to reflect the new structure. * Align intro on the Omnitrace index page with the first paragraph of the what-is page * Changes and edits based on review comments * Additional changes and edits based on external review * Additional updates and changes from the external review of Omnitrace * Additional changes based on the external review * New round of edits based on the external review * Additional edits based on the external review * Changes to address comments from the internal review * Correct to the RHEL SELinux note in the troubleshooting guide * One additional change to the development guide code example * Move troubleshooting to post-install of install.rst and other minor edits. * Remove troubleshooting page and modify new post-install troubleshooting section on install.rst * Refactor the how Omnitrace works page into seperate topics and redo infrastructure * API ToC changes * Additional API and ToC changes * Back out API and ToC changes and update requirements.txt * Additional API and ToC changes * Add commit for signing purposes * Add ElfUtils and BinUtils Download URL Overrides (ROCm#358) * Add CMake CACHE Variable ElfUtils_DOWNLOAD_URL Used to override the default URL to download ElfUtils from. Useful for internal builds Also, include a mirror to fallback to if the override URL fails. * Update timemory submodule Updating to include the BINUTIL_DOWNLOAD_URL override cmake variable. --------- Signed-off-by: David Galiffi <[email protected]> * Remove Ubuntu 18.04 and SUSE 15.2 * Update checkout action to v4 * Add `docs/**` to `paths-ignore` Document location is being refactored. * Modified submodules dyninst and timemory. (ROCm#361) --------- Signed-off-by: David Galiffi <[email protected]> Co-authored-by: Peter Jun Park <[email protected]> Co-authored-by: ajanicijamd <[email protected]> Co-authored-by: David Galiffi <[email protected]> Co-authored-by: Jonathan R. Madsen <[email protected]> Co-authored-by: Sam Wu <[email protected]>
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# Python cache files | ||
*.pyc | ||
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# Documentation artifacts | ||
/_build | ||
_toc.yml | ||
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/build* | ||
/.vscode | ||
/.cache | ||
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# Read the Docs configuration file | ||
# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details | ||
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version: 2 | ||
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build: | ||
os: ubuntu-22.04 | ||
tools: | ||
python: "3.10" | ||
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python: | ||
install: | ||
- requirements: docs/sphinx/requirements.txt | ||
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sphinx: | ||
configuration: docs/conf.py | ||
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formats: [] |
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_build/ | ||
_doxygen/ |
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.. meta:: | ||
:description: Omnitrace documentation and reference | ||
:keywords: Omnitrace, ROCm, profiler, tracking, visualization, tool, Instinct, accelerator, AMD | ||
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********************** | ||
Data collection modes | ||
********************** | ||
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Omnitrace supports several modes of recording trace and profiling data for your application. | ||
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.. note:: | ||
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For an explanation of the terms used in this topic, see | ||
the :doc:`Omnitrace glossary <../reference/omnitrace-glossary>`. | ||
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+-----------------------------+---------------------------------------------------------+ | ||
| Mode | Description | | ||
+=============================+=========================================================+ | ||
| Binary Instrumentation | Locates functions (and loops, if desired) in the binary | | ||
| | and inserts snippets at the entry and exit | | ||
+-----------------------------+---------------------------------------------------------+ | ||
| Statistical Sampling | Periodically pauses application at specified intervals | | ||
| | and records various metrics for the given call stack | | ||
+-----------------------------+---------------------------------------------------------+ | ||
| Callback APIs | Parallelism frameworks such as ROCm, OpenMP, and Kokkos | | ||
| | make callbacks into Omnitrace to provide information | | ||
| | about the work the API is performing | | ||
+-----------------------------+---------------------------------------------------------+ | ||
| Dynamic Symbol Interception | Wrap function symbols defined in a position independent | | ||
| | dynamic library/executable, like ``pthread_mutex_lock`` | | ||
| | in ``libpthread.so`` or ``MPI_Init`` in the MPI library | | ||
+-----------------------------+---------------------------------------------------------+ | ||
| User API | User-defined regions and controls for Omnitrace | | ||
+-----------------------------+---------------------------------------------------------+ | ||
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The two most generic and important modes are binary instrumentation and statistical sampling. | ||
It is important to understand their advantages and disadvantages. | ||
Binary instrumentation and statistical sampling can be performed with the ``omnitrace-instrument`` | ||
executable. For statistical sampling, it's highly recommended to use the | ||
``omnitrace-sample`` executable instead if binary instrumentation isn't required or needed. | ||
Callback APIs and dynamic symbol interception can be utilized with either tool. | ||
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Binary instrumentation | ||
----------------------------------- | ||
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Binary instrumentation lets you record deterministic measurements for | ||
every single invocation of a given function. | ||
Binary instrumentation effectively adds instructions to the target application to | ||
collect the required information. It therefore has the potential to cause performance | ||
changes which might, in some cases, lead to inaccurate results. The effect depends on | ||
the information being collected and which features are activated in Omnitrace. | ||
For example, collecting only the wall-clock timing data | ||
has less of an effect than collecting the wall-clock timing, CPU-clock timing, | ||
memory usage, cache-misses, and number of instructions that were run. Similarly, | ||
collecting a flat profile has less overhead than a hierarchical profile | ||
and collecting a trace OR a profile has less overhead than collecting a | ||
trace AND a profile. | ||
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In Omnitrace, the primary heuristic for controlling the overhead with binary | ||
instrumentation is the minimum number of instructions for selecting functions | ||
for instrumentation. | ||
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Statistical sampling | ||
----------------------------------- | ||
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Statistical call-stack sampling periodically interrupts the application at | ||
regular intervals using operating system interrupts. | ||
Sampling is typically less numerically accurate and specific, but the | ||
target program runs at nearly full speed. | ||
In contrast to the data derived from binary instrumentation, the resulting | ||
data is not exact but is instead a statistical approximation. | ||
However, sampling often provides a more accurate picture of the application | ||
execution because it is less intrusive to the target application and has fewer | ||
side effects on memory caches or instruction decoding pipelines. Furthermore, | ||
because sampling does not affect the execution speed as much, is it | ||
relatively immune to over-evaluating the cost of small, frequently called | ||
functions or "tight" loops. | ||
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In Omnitrace, the overhead for statistical sampling depends on the | ||
sampling rate and whether the samples are taken with respect to the CPU time | ||
and/or real time. | ||
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Binary instrumentation vs. statistical sampling example | ||
------------------------------------------------------- | ||
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Consider the following code: | ||
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.. code-block:: c++ | ||
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long fib(long n) | ||
{ | ||
if(n < 2) return n; | ||
return fib(n - 1) + fib(n - 2); | ||
} | ||
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void run(long n) | ||
{ | ||
long result = fib(n); | ||
printf("[%li] fibonacci(%li) = %li\n", i, n, result); | ||
} | ||
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int main(int argc, char** argv) | ||
{ | ||
long nfib = 30; | ||
long nitr = 10; | ||
if(argc > 1) nfib = atol(argv[1]); | ||
if(argc > 2) nitr = atol(argv[2]); | ||
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for(long i = 0; i < nitr; ++i) | ||
run(nfib); | ||
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return 0; | ||
} | ||
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Binary instrumentation of the ``fib`` function will record **every single invocation** | ||
of the function. For a very small function | ||
such as ``fib``, this results in **significant** overhead since this simple function | ||
takes about 20 instructions, whereas the entry and | ||
exit snippets are ~1024 instructions. Therefore, you generally want to avoid | ||
instrumenting functions where the instrumented function has significantly fewer | ||
instructions than entry and exit instrumentation. (Note that many of the | ||
instructions in entry and exit functions are either logging functions or | ||
depend on the runtime settings and thus might never run). However, | ||
due to the number of potential instructions in the entry and exit snippets, | ||
the default behavior of ``omnitrace-instrument`` is to only instrument functions | ||
which contain fewer than 1024 instructions. | ||
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However, recording every single invocation of the function can be extremely | ||
useful for detecting anomalies, such as profiles that show minimum or maximum values much smaller or larger | ||
than the average or a high standard deviation. In this case, the traces help you | ||
identify exactly when and where those instances deviated from the norm. | ||
Compare the level of detail in the following traces. In the top image, | ||
every instance of the ``fib`` function is instrumented, while in the bottom image, | ||
the ``fib`` call-stack is derived via sampling. | ||
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Binary instrumentation of the Fibonacci function | ||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ | ||
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.. image:: ../data/fibonacci-instrumented.png | ||
:alt: Visualization of the output of a binary instrumentation of the Fibonacci function | ||
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Statistical sampling of the Fibonacci function | ||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ | ||
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.. image:: ../data/fibonacci-sampling.png | ||
:alt: Visualization of the output of a statistical sample of the Fibonacci function |
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.. meta:: | ||
:description: Omnitrace documentation and reference | ||
:keywords: Omnitrace, ROCm, profiler, tracking, visualization, tool, Instinct, accelerator, AMD | ||
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*************************************** | ||
The Omnitrace feature set and use cases | ||
*************************************** | ||
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`Omnitrace <https://github.com/ROCm/omnitrace>`_ is designed to be highly extensible. | ||
Internally, it leverages the `Timemory performance analysis toolkit <https://github.com/NERSC/timemory>`_ | ||
to manage extensions, resources, data, and other items. It supports the following features, | ||
modes, metrics, and APIs. | ||
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Data collection modes | ||
======================================== | ||
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* Dynamic instrumentation | ||
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* Runtime instrumentation: Instrument executables and shared libraries at runtime | ||
* Binary rewriting: Generate a new executable and/or library with instrumentation built-in | ||
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* Statistical sampling: Periodic software interrupts per-thread | ||
* Process-level sampling: A background thread records process-, system- and device-level metrics while the application runs | ||
* Causal profiling: Quantifies the potential impact of optimizations in parallel code | ||
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.. note:: | ||
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Critical trace support was removed in Omnitrace v1.11.0. | ||
It was replaced by the causal profiling feature. | ||
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Data analysis | ||
======================================== | ||
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* High-level summary profiles with mean, min, max, and standard deviation statistics | ||
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* Low overhead and memory efficient | ||
* Ideal for running at scale | ||
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* Comprehensive traces for every individual event and measurement | ||
* Application speed-up predictions resulting from potential optimizations in functions and lines of code based on causal profiling | ||
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Parallelism API support | ||
======================================== | ||
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* HIP | ||
* HSA | ||
* Pthreads | ||
* MPI | ||
* Kokkos-Tools (KokkosP) | ||
* OpenMP-Tools (OMPT) | ||
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GPU metrics | ||
======================================== | ||
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* GPU hardware counters | ||
* HIP API tracing | ||
* HIP kernel tracing | ||
* HSA API tracing | ||
* HSA operation tracing | ||
* System-level sampling (via rocm-smi) | ||
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* Memory usage | ||
* Power usage | ||
* Temperature | ||
* Utilization | ||
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CPU metrics | ||
======================================== | ||
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* CPU hardware counters sampling and profiles | ||
* CPU frequency sampling | ||
* Various timing metrics | ||
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* Wall time | ||
* CPU time (process and thread) | ||
* CPU utilization (process and thread) | ||
* User CPU time | ||
* Kernel CPU time | ||
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* Various memory metrics | ||
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* High-water mark (sampling and profiles) | ||
* Memory page allocation | ||
* Virtual memory usage | ||
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* Network statistics | ||
* I/O metrics | ||
* Many others | ||
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Third-party API support | ||
======================================== | ||
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* TAU | ||
* LIKWID | ||
* Caliper | ||
* CrayPAT | ||
* VTune | ||
* NVTX | ||
* ROCTX | ||
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Omnitrace use cases | ||
======================================== | ||
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When analyzing the performance of an application, do NOT | ||
assume you know where the performance bottlenecks are | ||
and why they are happening. Omnitrace is a tool for analyzing the entire | ||
application and its performance. It is | ||
ideal for characterizing where optimization would have the greatest impact | ||
on an end-to-end run of the application and for | ||
viewing what else is happening on the system during a performance bottleneck. | ||
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When GPUs are involved, there is a tendency to assume that | ||
the quickest path to performance improvement is minimizing | ||
the runtime of the GPU kernels. This is a highly flawed assumption. | ||
If you optimize the runtime of a kernel from one millisecond | ||
to 1 microsecond (1000x speed-up) but the original application never | ||
spent time waiting for kernels to complete, | ||
there would be no statistically significant reduction in the end-to-end | ||
runtime of your application. In other words, it does not matter | ||
how fast or slow the code on GPU is if the application has a | ||
bottleneck on waiting on the GPU. | ||
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Use Omnitrace to obtain a high-level view of the entire application. Use it | ||
to determine where the performance bottlenecks are and | ||
obtain clues to why these bottlenecks are happening. Rather than worrying about kernel | ||
performance, start your investigation with Omnitrace, which characterizes the | ||
broad picture. | ||
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.. note:: | ||
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For insight into the execution of individual kernels on the GPU, | ||
use `Omniperf <https://github.com/rocm/omniperf>`_. | ||
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In terms of CPU analysis, Omnitrace does not target any specific vendor. | ||
It works just as well on AMD and non-AMD CPUs. | ||
With regard to the GPU, Omnitrace is currently restricted to HIP and HSA APIs | ||
and kernels running on AMD GPUs. |
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