Reproducibility of the DaCe Framework on NPBench Benchmarks
Anish Govind, Yuchen Jing, Stefanie Dao, Michael Granado, Rachel Handran, Davit Margarian, Matthew Mikhailov, Danny Vo, Matei-Alexandru Gardus, Khai Vu, Derek Bouius, Bryan Allen Chin, Mahidhar Tatineni, Mary Thomas · IEEE Transactions on Parallel and Distributed Systems · 2024
DaCe is a framework for Python that claims to provide massive speedups with C-like speeds compared to already existing high-performance Python frameworks (e.g. Numba or Pythran). In this work, we take a closer look at reproducing the NPBench work. We use performance results to confirm that NPBench achieves higher performance than NumPy in a variety of benchmarks and provide reasons as to why DaCe is not truly as portable as it claims to be, but with a small adjustment it can run anywhere.