A64FX performance: experience on Ookami

A64FX performance: experience on Ookami
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A64FX 性能:Ookami 体验

DOI:
10.1109/cluster48925.2021.00106
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发表时间:
2021
期刊:
2021 IEEE International Conference on Cluster Computing (CLUSTER
影响因子:
--
通讯作者:
Jones, Matthew D.
Jones, Matthew D.
中科院分区:
--
文献类型:
--
作者:
Bari, Md Abdullah;Chapman, Barbara;Curtis, Anthony;Harrison, Robert J.;Siegmann, Eva;Simakov, Nikolay A.;Jones, Matthew D.

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我们研究了富士通A64FX处理器上的科学和工程内核的性能,无论是开箱即用的使用各种工具链和处理器特定的优化。虽然几乎所有的应用程序移植很少或没有修改,但在多个工具链之间观察到显着的性能差异。这种变化在很大程度上取决于应用程序的特性(最明显的是它对数学函数的使用),并且还受到最高性能的工具链的限制,这些工具链对最近的语言标准的支持有限。正如预期的那样,高性能要求内核是向量化的、多线程的,并本地化内存引用。详细的优化,包括使用intrinsic,也被检查,以了解性能差距和什么是必要的,以达到峰值性能。本文采用了由美国国家科学基金会资助的Ookami计算机技术试验台。该系统为全球的研究人员提供了176个富士通A64FX计算节点以及其他最先进的技术。
We examine the performance of scientific and engineering kernels on the Fujitsu A64FX processor, both out-of-the-box using various toolchains and with processor-specific optimizations. While nearly all applications port with little to no modification, significant performance variation is observed between the multiple tool chains. This variation depends heavily upon characteristics of the application (most notably its use of mathematical functions) and is also constrained by the most performant toolchains having limited support for recent language standards. As expected, high performance demands that a kernel is vectorized, multi-threaded, and localizes memory references. Detailed optimizations, including use of intrinsics, are also examined to understand performance gaps and what is necessary to attain peak performance. This article employs the Ookami computer technology testbed funded by the American National Science Foundation. The system provides researchers worldwide with access to 176 Fujitsu A64FX compute nodes as well as other state-of the-art technology.
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