用张量处理单元加速物理模拟:一个洪水建模的例子

IF 3.5 3区计算机科学 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

International Journal of High Performance Computing Applications Pub Date : 2022-06-03 DOI:10.1177/10943420221102873

R. Hu, D. Pierce, Yusef Shafi, Anudhyan Boral, V. Anisimov, Sella Nevo, Yi-Fan Chen

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引用次数: 6

摘要

硬件加速器的最新进展，如张量处理单元(tpu)，相对于中央处理单元(cpu)，不仅加快了机器学习的计算时间，而且如本文所示，也加快了科学建模和计算机模拟的计算时间。为了研究用于分布式科学计算的TPU硬件，我们求解流体物理模拟的偏微分方程(PDEs)来模拟河流洪水。我们证明tpu比cpu实现了两个数量级的加速。在tpu上运行物理模拟可以通过谷歌云平台公开访问，我们发布了模拟的Python交互式笔记本版本。

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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Accelerating physics simulations with tensor processing units: An inundation modeling example

Recent advancements in hardware accelerators such as Tensor Processing Units (TPUs) speed up computation time relative to Central Processing Units (CPUs) not only for machine learning but, as demonstrated here, also for scientific modeling and computer simulations. To study TPU hardware for distributed scientific computing, we solve partial differential equations (PDEs) for the physics simulation of fluids to model riverine floods. We demonstrate that TPUs achieve a two orders of magnitude speedup over CPUs. Running physics simulations on TPUs is publicly accessible via the Google Cloud Platform, and we release a Python interactive notebook version of the simulation.

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来源期刊

International Journal of High Performance Computing Applications 工程技术-计算机：跨学科应用

CiteScore

6.10

自引率

6.50%

发文量

审稿时长

>12 weeks

期刊介绍： With ever increasing pressure for health services in all countries to meet rising demands, improve their quality and efficiency, and to be more accountable; the need for rigorous research and policy analysis has never been greater. The Journal of Health Services Research & Policy presents the latest scientific research, insightful overviews and reflections on underlying issues, and innovative, thought provoking contributions from leading academics and policy-makers. It provides ideas and hope for solving dilemmas that confront all countries.