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DaCe - Data-Centric Parallel Programming

Decoupling domain science from performance optimization.

DaCe compiles code in various programming languages and paradigms (Python/Numpy, MATLAB, TensorFlow) and maps it efficiently to CPUs, GPUs, and FPGAs with high utilization, on par with the state-of-the-art. The key feature driving DaCe is its Stateful DataFlow multiGraph (SDFG) data-centric intermediate representation: A transformable, interactive representation of code based on data movement. With data-centric parallel programming, we enable direct knowledge transfer of performance optimization, regardless of the scientific application or the target processor.

DaCe can be written inline in Python and transformed in the command-line, or SDFGs can be interactively modified using the Data-centric Interactive Optimization Development Environment (DIODE).

For more information, see our paper.

Tutorials

Installation and Dependencies

To install: pip install dace

Runtime dependencies:

  • A C++14-capable compiler (e.g., gcc 5.3+)
  • Python 3.5 or newer

To run DIODE, either run the installed script diode, or call python3 -m diode.diode_rest from the shell. Then, follow the printed instructions.

Publication

If you use DaCe, cite us:

@article{dace,
  author = {Ben-Nun, Tal and de Fine Licht, Johannes and Ziogas, Alexandros Nikolaos and Schneider, Timo and Hoefler, Torsten},
        title = {Stateful Dataflow Multigraphs: A Data-Centric Model for High-Performance Parallel Programs},
  journal   = {CoRR},
  volume    = {abs/1902.10345},
  year      = {2019},
  url       = {http://arxiv.org/abs/1902.10345},
  archivePrefix = {arXiv},
  eprint    = {1902.10345}
}

Configuration

DaCe creates a file called .dace.conf in the user's home directory. It provides useful settings that can be modified either directly in the file (YAML), within DIODE, or overriden on a case-by-case basis using environment variables that begin with DACE_ and specify the setting (where categories are separated by underscores). The full configuration schema is located here.

Useful environment variable configurations include:

  • DACE_CONFIG (default: ~/.dace.conf): Override DaCe configuration file choice.

Context configuration:

  • DACE_use_cache (default: False): Uses DaCe program cache instead of re-optimizing and compiling programs.
  • DACE_debugprint (default: True): Print debugging information.

CPU target configuration:

  • DACE_compiler_cpu_executable (default: g++): Chooses the default C++ compiler for CPU code.
  • DACE_compiler_cpu_additional_args (default: None): Additional compiler flags (separated by spaces).

SDFG processing:

  • DACE_optimizer_interface (default: dace.transformation.optimizer.SDFGOptimizer): Controls the SDFG optimization process. If empty or class name is invalid, skips process. By default, uses the transformation command line interface.
  • DACE_optimizer_visualize (default: False): Visualizes optimization process by saving .dot (GraphViz) files after each pattern replacement.

Profiling:

  • DACE_profiling (default: False): Enables profiling measurement of the DaCe program runtime in milliseconds. Produces a log file and prints out median runtime.
  • DACE_treps (default: 100): Number of repetitions to run a DaCe program when profiling is enabled.

Contributing

DaCe is an open-source project. We are happy to accept Pull Requests with your contributions!

License

DaCe is published under the New BSD license, see LICENSE.