n体问题的实现对GPU供应商之间性能和可移植性的影响

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-03-12 DOI:10.1016/j.future.2025.107802

Rodrigo A.C. Bartolomeu , René Halver , Jan H. Meinke , Godehard Sutmann

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引用次数: 0

摘要

自Aurora于2023年11月进入TOP500榜单以来，前十大系统的GPU供应商比例发生了一些变化。由于每个供应商都为其硬件提供了自己的首选编程模型，因此比较这些模型在其他硬件平台上的可移植性就变得相关了。在本文中，我们使用本机和可移植编程框架实现了不同优化的n体问题。对于其中的每一个，我们在一个目标体系结构上确定了性能最佳的优化版本，并比较了每个平台上实现的性能。

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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Effect of implementations of the N-body problem on the performance and portability across GPU vendors

Since Aurora entered the TOP500 list in November 2023, the top ten systems saw some shifts in the ratio of GPU vendors represented. With each vendor supplying their own preferred programming models for their hardware, it becomes relevant to compare the portability of these models on other hardware platforms. For the present paper we implemented the N-body problem with different optimizations using native and portable programming frameworks. For each of those we determined the best performing optimized version on one target architecture and compared the performance achieved for each platform.

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来源期刊

Future Generation Computer Systems-The International Journal of Escience 工程技术-计算机：理论方法

CiteScore

19.90

自引率

2.70%

发文量

376

审稿时长

10.6 months

期刊介绍： Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.