Reproducibility of the DaCe Framework on NPBench Benchmarks

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 a...

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Bibliographic Details
Published in:IEEE transactions on parallel and distributed systems 2024-07, p.1-5
Main Authors: Govind, Anish, Jing, Yuchen, Dao, Stefanie, Granado, Michael, Handran, Rachel, Margarian, Davit, Mikhailov, Matthew, Vo, Danny, Gardus, Matei-Alexandru, Vu, Khai, Bouius, Derek, Chin, Bryan, Tatineni, Mahidhar, Thomas, Mary
Format: Article
Language:English
Subjects:
Benchmark testing
Graphics processing units
Hardware
Optimization
Python
Random access memory
Software
Online Access:Get full text
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Summary: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.
ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2024.3427130