A64FX上流核和稀疏矩阵向量乘法的性能建模

C. Alappat, Jan Laukemann, T. Gruber, G. Hager, G. Wellein, N. Meyer, T. Wettig
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引用次数: 14

摘要

A64FX CPU为目前Top500榜单上排名第一的超级计算机提供动力。虽然它是传统的基于缓存的多核处理器,但其峰值性能和内存带宽可与加速器设备相媲美。为这样的新体系结构生成高效的代码需要对其性能特性有很好的理解。利用这些特性,我们为FX700超级计算机中的A64FX处理器构建了执行-缓存-内存(ECM)性能模型,并使用流循环对其进行了验证。我们还确定了体系结构的特性并得出了优化提示。将ECM模型应用于稀疏矩阵向量乘法(SpMV),我们解释了为什么CRS矩阵存储格式是不合适的,以及SELL-C-σ格式如何通过适当的代码优化来实现SpMV的带宽饱和。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance Modeling of Streaming Kernels and Sparse Matrix-Vector Multiplication on A64FX
The A64FX CPU powers the current #1 supercomputer on the Top500 list. Although it is a traditional cache-based multicore processor, its peak performance and memory bandwidth rival accelerator devices. Generating efficient code for such a new architecture requires a good understanding of its performance features. Using these features, we construct the Execution-Cache-Memory (ECM) performance model for the A64FX processor in the FX700 supercomputer and validate it using streaming loops. We also identify architectural peculiarities and derive optimization hints. Applying the ECM model to sparse matrix-vector multiplication (SpMV), we motivate why the CRS matrix storage format is inappropriate and how the SELL-C-σ format with suitable code optimizations can achieve bandwidth saturation for SpMV.
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