An Initial Evaluation of Arm’s Scalable Matrix Extension

Finn Wilkinson, Simon McIntosh-Smith
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Abstract

Expanding upon their Scalable Vector Extension (SVE), Arm have introduced the Scalable Matrix Extension (SME) to improve in-core performance for matrix operations such as matrix multiplication. With the lack of hardware and cycle-accurate simulations available which supports SME, it is unclear how effective this new instruction set extension will be, and for what type of applications it will provide the most benefit.By adapting The Simulation Engine (SimEng) from the University of Bristol’s High Performance Computing Group to support SME, we aim to compare the simulated performance of a Fujitsu A64FX core (with native SVE support) to a like-for-like hypothetical core with added SME support. By simulating a wide range of Streaming Vector Lengths for our hypothetical SME core model, we provide and discuss first-of-a-kind results for an SME implementation, before discussing future work that will be carried out to further evaluate the suitability of SME.
Arm可伸缩矩阵扩展的初步评价
在可扩展向量扩展(SVE)的基础上,Arm推出了可扩展矩阵扩展(SME),以提高矩阵运算(如矩阵乘法)的核心性能。由于缺乏支持SME的硬件和周期精确模拟,目前还不清楚这个新的指令集扩展将有多有效,以及它将为哪种类型的应用程序提供最大的好处。通过调整来自布里斯托尔大学高性能计算小组的模拟引擎(SimEng)来支持SME,我们的目标是比较富士通A64FX核心(具有原生SVE支持)的模拟性能与添加SME支持的类似假设核心。通过为我们假设的SME核心模型模拟大范围的流向量长度,我们提供并讨论了SME实现的首个此类结果,然后讨论了将进行的进一步评估SME适用性的未来工作。
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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