aCe - 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 generates high-performance programs for:

• Multi-core CPUs (tested on Intel and IBM POWER9)
• NVIDIA GPUs
• AMD GPUs (with HIP)
• Xilinx FPGAs
• Intel FPGAs

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

For more information, see our paper.

See an example SDFG in the standalone viewer (SDFV).

Tutorials

• Getting Started
• Explicit Dataflow in Python
• NumPy API Reference
• SDFG API
• Using and Creating Transformations
• Extending the Code Generator

Installation and Dependencies

To install: pip install dace

Runtime dependencies:

• A C++14-capable compiler (e.g., gcc 5.3+)
• Python 3.6 or newer
• CMake 3.15 or newer

Running

Python scripts: Run DaCe programs (in implicit, explicit, or TensorFlow syntax) using Python directly.

SDFV (standalone SDFG viewer): To view SDFGs separately, run the sdfv installed script with the .sdfg file as an argument. Alternatively, you can use the link or open diode/sdfv.html directly and choose a file in the browser.

Visual Studio Code plugin: Install from the VSCode marketplace or open an .sdfg file for interactive SDFG viewing and transformation.

DIODE interactive development (experimental):: Either run the installed script diode, or call python3 -m diode from the shell. Then, follow the printed instructions to enter the web interface.

The sdfgcc tool: Compile .sdfg files with sdfgcc program.sdfg. Interactive command-line optimization is possible with the --optimize flag.

Jupyter Notebooks: DaCe is Jupyter-compatible. If a result is an SDFG or a state, it will show up directly in the notebook. See the tutorials for examples.

Octave scripts (experimental): .m files can be run using the installed script dacelab, which will create the appropriate SDFG file.

Note for Windows/Visual C++ users: If compilation fails in the linkage phase, try setting the following environment variable to force Visual C++ to use Multi-Threaded linkage:

X:\path\to\dace> set _CL_=/MT

Publication

If you use DaCe, cite us:

@inproceedings{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 Performance Portability on Heterogeneous Architectures},
  year      = {2019},
  booktitle = {Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis},
  series = {SC '19}
}

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.

General configuration:

• DACE_debugprint (default: False): Print debugging information.
• DACE_compiler_use_cache (default: False): Uses DaCe program cache instead of re-optimizing and compiling programs.
• DACE_compiler_default_data_types (default: Python): Chooses default types for integer and floating-point values. If Python is chosen, int and float are both 64-bit wide. If C is chosen, int and float are 32-bit wide.

GPU programming and debugging:

• DACE_compiler_cuda_backend (default: cuda): Chooses the GPU backend to use (can be cuda for NVIDIA GPUs or hip for AMD GPUs).
• DACE_compiler_cuda_syncdebug (default: False): If True, calls device-synchronization after every GPU kernel and checks for errors. Good for checking crashes or invalid memory accesses.

FPGA programming:

• DACE_compiler_fpga_vendor: (default: xilinx): Can be xilinx for Xilinx FPGAs, or intel_fpga for Intel FPGAs.

SDFG interactive transformation:

• DACE_optimizer_transform_on_call (default: False): Uses the transformation command line interface every time a @dace function is called.
• DACE_optimizer_interface (default: dace.transformation.optimizer.SDFGOptimizer): Controls the SDFG optimization process if transform_on_call is enabled. By default, uses the transformation command line interface.
• DACE_optimizer_automatic_strict_transformations (default: True): If False, skips automatic strict transformations in the Python frontend (see transformations tutorial for more information).

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.