Stencil Computations on AMD and Nvidia Graphics Processors: Performance and Tuning Strategies

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date

2025-06-25

Department

Department of Computer Science

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Mcode

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Language

en

Pages

23

Series

Concurrency and Computation: Practice and Experience, Volume 37, issue 12-14, pp. 1-23

Abstract

Over the last ten years, graphics processors have become the de facto accelerator for data-parallel tasks in various branches of high-performance computing, including machine learning and computational sciences. However, with the recent introduction of AMD-manufactured graphics processors to the world's fastest supercomputers, tuning strategies established for previous hardware generations must be re-evaluated. In this study, we evaluate the performance and energy efficiency of stencil computations on modern datacenter graphics processors and propose a tuning strategy for fusing cache-heavy stencil kernels. The studied cases comprise both synthetic and practical applications, which involve the evaluation of linear and nonlinear stencil functions in one to three dimensions. Our experiments reveal that AMD and Nvidia graphics processors exhibit key differences in both hardware and software, necessitating platform-specific tuning to reach their full computational potential.

Description

| openaire: EC/H2020/818665/EU//UniSDyn

Keywords

discrete convolution, stencil computations, energy efficiency, partial differential equations, performance optimization, graphics processing units, high-performance computing

Other note

DOI

10.1002/cpe.70129

Citation

Pekkilä, J, Lappi, O, Robertsen, F & Korpi-Lagg, M 2025, 'Stencil Computations on AMD and Nvidia Graphics Processors: Performance and Tuning Strategies', Concurrency and Computation: Practice and Experience, vol. 37, no. 12-14, e70129, pp. 1-23. https://doi.org/10.1002/cpe.70129

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https://urn.fi/URN:NBN:fi:aalto-202506265305

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[article-cris] Perustieteiden korkeakoulu / SCI