The Daisytuner Optimizing Compiler Collection (docc) implements an intermediate representation as well as frontends, drivers, and code generation for translation and optimization of various programming languages to multiple targets.

The core of the project is stateful dataflow multigraphs (SDFG), implemented in the sdfg module. It contains the definition of the intermediate representation as well as numerous passes and analyses. For instance, docc comes with support for auto-parallelization using data-centric and polyhedral analaysis.

SDFGs can be generated from Python (JIT) and MLIR frontends, which are separate components including Python bindings and an MLIR dialect for conversion. Targets such as Generic, Google Highway, OpenMP, and CUDA are implemented in opt.

Furthermore, the repository contains runtime libraries for code instrumentation (performance counters and data capturing).

Quick Start

Binary releases are published for each new version and can be downloaded via standard package managers. They provide an easy way to get started with docc.

Python

The Python frontend generates C++ code, which is compiled and called from Python. This requires clang-19 to be installed on the system (see LLVM releases).

Afterward, simply install docc via PyPI:

pip install docc-compiler

import numpy as np
import docc

@docc.program(target="openmp")
def matrix_multiply(A, B):
    return A @ B

A = np.random.rand(1000, 1000)
B = np.random.rand(1000, 1000)
C = matrix_multiply(A, B)

MLIR

A quickstart example will be added soon.

Building the Core Components

sudo apt-get install -y libgmp-dev libzstd-dev
sudo apt-get install -y nlohmann-json3-dev
sudo apt-get install -y libboost-graph-dev
sudo apt-get install -y libisl-dev
sudo apt-get install -y libcurl4-gnutls-dev

The core components sdfg, opt, rtl and rpc can be built with cmake.

mkdir build && cd build
cmake \
  -G Ninja \
  -DCMAKE_C_COMPILER=clang-19 \
  -DCMAKE_CXX_COMPILER=clang++-19 \
  -DCMAKE_BUILD_TYPE=Debug \
  -DBUILD_TESTS:BOOL=OFF \
  -DBUILD_BENCHMARKS:BOOL=OFF \
  -DBUILD_BENCHMARKS_GOOGLE:BOOL=OFF  \
  ..
ninja -j$(nproc)

For instructions on how to build and extend frontends, check out the README.md of the components' directories.

Attribution

docc is based on the specification described in this paper and the DaCe reference implementation. The license of the reference implementation is included in the licenses/ folder.

If you use docc, cite the dace paper:

@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}
}

License

docc is published under the new BSD license, see LICENSE.