PolyBench/Python: Benchmarking Python Environments With Polyhedral Optimizations
Title
PolyBench/Python: Benchmarking Python Environments With Polyhedral OptimizationsAuthor(s)
Date
2021-03Citation
Abella-González, M. Á., Carollo-Fernández, P., Pouchet, L. N., Rastello, F., & Rodríguez, G. (2021, March). PolyBench/Python: benchmarking Python environments with polyhedral optimizations. In Proceedings of the 30th ACM SIGPLAN International Conference on Compiler Construction (pp. 59-70).
Abstract
[Abstract]
Python has become one of the most used and taught languages nowadays. Its expressiveness, cross-compatibility and ease of use have made it popular in areas as diverse as finance, bioinformatics or machine learning. However, Python programs are often significantly slower to execute than an equivalent native C implementation, especially for computation-intensive numerical kernels.
This work presents PolyBench/Python, implementing the 30 kernels in PolyBench/C, one of the standard benchmark suites for polyhedral optimization, in Python. In addition to the benchmark kernels, a functional wrapper including mechanisms for performance measurement, testing, and execution configuration has been developed. The framework includes support for different ways to translate C-array codes into Python, offering insight into the tradeoffs of Python lists and NumPy arrays. The benchmark performance is thoroughly evaluated on different Python interpreters, and compared against its PolyBench/C counterpart to highlight the profitability (or lack thereof) of using Python for regular numerical codes.
Keywords
Python
Benchmarking
JIT Optimization
Polyhedral Compilation
Benchmarking
JIT Optimization
Polyhedral Compilation
Editor version
Rights
© 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM
ISSN
978-1-4503-8325-7