Portable Lattice QCD implementation based on OpenCL

The presence of GPU from different vendors demands the Lattice QCD codes to support multiple architectures. To this end, Open Computing Language (OpenCL) is one of the viable frameworks for writing a portable code. It is of interest to find out how the OpenCL implementation performs as compared to the code based on a dedicated programming interface such as CUDA for Nvidia GPUs. We have developed an OpenCL backend for our already existing code of the Wuppertal-Budapest collaboration. In this contribution, we show benchmarks of the most time consuming part of the numerical simulation, namely, the inversion of the Dirac operator. We present the code performance on the JUWELS and LUMI Supercomputers based on Nvidia and AMD graphics cards, respectively, and compare with the CUDA backend implementation.

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Publication Details

Published
2025-02-13
DOI
https://doi.org/10.22323/1.466.0438
Primary Topic
Distributed and Parallel Computing Systems
Type
article
Field-Weighted Citation Impact
0.00

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article

Portable Lattice QCD implementation based on OpenCL

Jana N. Guenther, Szabolcs Borsányi, Chik Him Wong, Piyush Kumar
Distributed and Parallel Computing Systems
article

Portable Lattice QCD implementation based on OpenCL

Jana N. Guenther, Szabolcs Borsányi, Chik Him Wong, Piyush Kumar
article en

Abstract

The presence of GPU from different vendors demands the Lattice QCD codes to support multiple architectures. To this end, Open Computing Language (OpenCL) is one of the viable frameworks for writing a portable code. It is of interest to find out how the OpenCL implementation performs as compared to the code based on a dedicated programming interface such as CUDA for Nvidia GPUs. We have developed an OpenCL backend for our already existing code of the Wuppertal-Budapest collaboration. In this contribution, we show benchmarks of the most time consuming part of the numerical simulation, namely, the inversion of the Dirac operator. We present the code performance on the JUWELS and LUMI Supercomputers based on Nvidia and AMD graphics cards, respectively, and compare with the CUDA backend implementation.

Deutsche Forschungsgemeinschaft, China Scholarship Council, Ministerium für Kultur und Wissenschaft des Landes Nordrhein-Westfalen, Gauss Centre for Supercomputing
Openalex Percentile: Top 98%
Distributed and Parallel Computing Systems
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