research-article

NPBench: a benchmarking suite for high-performance NumPy

Authors:

Alexandros Nikolaos Ziogas,

Timo Schneider,

Torsten HoeflerAuthors Info & Claims

ICS '21: Proceedings of the 35th ACM International Conference on Supercomputing

Pages 63 - 74

https://doi.org/10.1145/3447818.3460360

Published: 04 June 2021 Publication History

Abstract

Python, already one of the most popular languages for scientific computing, has made significant inroads in High Performance Computing (HPC). At the center of Python's ecosystem is NumPy, an efficient implementation of the multi-dimensional array (tensor) structure, together with basic arithmetic and linear algebra. Compared to traditional HPC languages, the relatively low performance of Python and NumPy has spawned significant research in compilers and frameworks that decouple Python's compact representation from the underlying implementation. However, it is challenging to compare language compatibility and performance among different frameworks and architectures without a standard set of benchmarks and metrics. To that end, we introduce NPBench, a set of NumPy code samples representing a large variety of HPC applications. We use NPBench to test popular NumPy-accelerating compilers and frameworks on a variety of metrics. NPBench will guide both end-users and framework developers focusing on performance and will drive further use of Python in the high-performance scientific domains.

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Index Terms

NPBench: a benchmarking suite for high-performance NumPy
1. General and reference
  1. Cross-computing tools and techniques

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cover image ACM Conferences

ICS '21: Proceedings of the 35th ACM International Conference on Supercomputing

June 2021

506 pages

ISBN:9781450383356

DOI:10.1145/3447818

General Chairs:
Huiyang Zhou
North Carolina State University
,
Jose Moreira
IBM Research
,
Program Chairs:
Frank Mueller
North Carolina State University
,
Yoav Etsion
Technion

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Published: 04 June 2021

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ICS '21: 2021 International Conference on Supercomputing

June 14 - 17, 2021

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ICS '21 Paper Acceptance Rate 39 of 157 submissions, 25%;

Overall Acceptance Rate 629 of 2,180 submissions, 29%

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https://dl.acm.org/doi/10.1145/3640537.3641575
López-Fernández AGómez-Vela Fdel Saz-Navarro MDelgado-Chaves FRodríguez-Baena D(2024)Optimized Python library for reconstruction of ensemble-based gene co-expression networks using multi-GPUThe Journal of Supercomputing10.1007/s11227-024-06127-480:12(18142-18176)Online publication date: 1-Aug-2024
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https://doi.org/10.1007/978-3-031-52186-7_1
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