Exploring code portability solutions for HEP with a particle tracking test code.

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Frontiers in Big Data Pub Date : 2024-10-23 eCollection Date: 2024-01-01 DOI:10.3389/fdata.2024.1485344
Hammad Ather, Sophie Berkman, Giuseppe Cerati, Matti J Kortelainen, Ka Hei Martin Kwok, Steven Lantz, Seyong Lee, Boyana Norris, Michael Reid, Allison Reinsvold Hall, Daniel Riley, Alexei Strelchenko, Cong Wang
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引用次数: 0

Abstract

Traditionally, high energy physics (HEP) experiments have relied on x86 CPUs for the majority of their significant computing needs. As the field looks ahead to the next generation of experiments such as DUNE and the High-Luminosity LHC, the computing demands are expected to increase dramatically. To cope with this increase, it will be necessary to take advantage of all available computing resources, including GPUs from different vendors. A broad landscape of code portability tools-including compiler pragma-based approaches, abstraction libraries, and other tools-allow the same source code to run efficiently on multiple architectures. In this paper, we use a test code taken from a HEP tracking algorithm to compare the performance and experience of implementing different portability solutions. While in several cases portable implementations perform close to the reference code version, we find that the performance varies significantly depending on the details of the implementation. Achieving optimal performance is not easy, even for relatively simple applications such as the test codes considered in this work. Several factors can affect the performance, such as the choice of the memory layout, the memory pinning strategy, and the compiler used. The compilers and tools are being actively developed, so future developments may be critical for their deployment in HEP experiments.

利用粒子跟踪测试代码探索 HEP 代码可移植性解决方案。
传统上,高能物理(HEP)实验的大部分重要计算需求都依赖于 x86 CPU。随着该领域对下一代实验(如 DUNE 和高亮度 LHC)的展望,预计计算需求将急剧增加。为了应对这一增长,有必要利用所有可用的计算资源,包括来自不同供应商的 GPU。代码可移植性工具的广泛应用--包括基于编译器语法的方法、抽象库和其他工具--允许相同的源代码在多种体系结构上高效运行。在本文中,我们使用 HEP 跟踪算法的测试代码来比较不同可移植性解决方案的性能和实施经验。虽然在某些情况下,可移植实现的性能接近参考代码版本,但我们发现,性能因实现细节的不同而有很大差异。实现最佳性能并非易事,即使是相对简单的应用,如本研究中考虑的测试代码。有几个因素会影响性能,如内存布局的选择、内存引脚策略和所使用的编译器。编译器和工具正在积极开发中,因此未来的发展可能对其在 HEP 实验中的部署至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.20
自引率
3.20%
发文量
122
审稿时长
13 weeks
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