Performance on HPC Platforms Is Possible Without C++

IF 1.8 4区计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computing in Science & Engineering Pub Date : 2024-04-18 DOI:10.1109/mcse.2023.3329330

Anshu Dubey, Tal Ben-Nun, Bradford L. Chamberlain, Bronis R. de Supinski, Damian Rouson

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引用次数: 0

Abstract

Computing at large scales has become extremely challenging due to increasing heterogeneity in both hardware and software. More and more scientific workflows must tackle a range of scales and use machine learning and AI intertwined with more traditional numerical modeling methods, placing more demands on computational platforms. These constraints indicate a need to fundamentally rethink the way computational science is done and the tools that are needed to enable these complex workflows. The current set of C++-based solutions may not suffice, and relying exclusively upon C++ may not be the best option, especially because several newer languages and boutique solutions offer more robust design features to tackle the challenges of heterogeneity. In June 2023, we held a mini symposium that explored the use of newer languages and heterogeneity solutions that are not tied to C++ and that offer options beyond template metaprogramming and Parallel. For for performance and portability. We describe some of the presentations and discussion from the mini symposium in this article.

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没有 C++ 也能在高性能计算平台上实现高性能

由于硬件和软件的异质性不断增加，大规模计算变得极具挑战性。越来越多的科学工作流程必须处理各种规模的问题，并将机器学习和人工智能与更传统的数值建模方法结合起来使用，这对计算平台提出了更高的要求。这些制约因素表明，有必要从根本上重新思考计算科学的工作方式以及实现这些复杂工作流程所需的工具。目前基于 C++ 的一系列解决方案可能无法满足需要，而完全依赖 C++ 可能也不是最佳选择，特别是因为一些更新的语言和精品解决方案提供了更强大的设计功能，可以应对异构性的挑战。2023 年 6 月，我们举办了一次小型研讨会，探讨了如何使用更新的语言和异构性解决方案，这些语言和解决方案与 C++ 无关，而且提供了模板元编程和并行之外的选择。对于性能和可移植性。我们将在本文中介绍小型研讨会的部分演讲和讨论内容。

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

Computing in Science & Engineering 工程技术-计算机：跨学科应用

CiteScore

4.20

自引率

0.00%

发文量

审稿时长

6-12 weeks

期刊介绍： Physics, medicine, astronomy -- these and other hard sciences share a common need for efficient algorithms, system software, and computer architecture to address large computational problems. And yet, useful advances in computational techniques that could benefit many researchers are rarely shared. To meet that need, Computing in Science & Engineering presents scientific and computational contributions in a clear and accessible format. The computational and data-centric problems faced by scientists and engineers transcend disciplines. There is a need to share knowledge of algorithms, software, and architectures, and to transmit lessons-learned to a broad scientific audience. CiSE is a cross-disciplinary, international publication that meets this need by presenting contributions of high interest and educational value from a variety of fields, including—but not limited to—physics, biology, chemistry, and astronomy. CiSE emphasizes innovative applications in advanced computing, simulation, and analytics, among other cutting-edge techniques. CiSE publishes peer-reviewed research articles, and also runs departments spanning news and analyses, topical reviews, tutorials, case studies, and more.