DaCe - Data-Centric Parallel Programming Framework

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). The latest version of the code can be found at https://github.com/spcl/daceThis code relies on external libraries to run. The external git sub-modules must be checked out, as defined in the .gitmodules file. Either re-clone branch `v0.8.1` from https://github.com/spcl/dace recursively, or use a script to read the modules from the file directly (e.g., https://stackoverflow.com/a/53269641/6489142) after running `git init` on the top-level folder