GEMM the New Gem: The Inevitable Kernel and its Sensitivity to Compiler Optimizations and Libraries

IF 2 3区计算机科学 Q4 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

IEEE Computer Architecture Letters Pub Date : 2026-01-21 DOI:10.1109/LCA.2026.3656423

Siyuan Ma;Bagus Hanindhito;Anushka Subramanian;Lizy K. John

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引用次数: 0

Abstract

When the SPEC benchmark suite was first assembled in 1989, matrix multiplication code matrix300 was one of the 10 programs in the suite, but it was discarded within 2-3 years due to the high sensitivity of matrix multiplication to compiler optimizations. However, with the advent of machine learning (ML), neural networks and generative AI (GenAI), matrix multiplication is an integral part of the modern computing workload. While sensitive, general matrix multiplication (GEMM) cannot be ignored anymore, especially if hardware that runs ML workloads is being evaluated. In this paper the sensitivity of GEMM workloads to libraries and compiler optimizations is studied. While it may be inevitable to use matmul kernels as a benchmark to understand the performance of accelerators for machine learning, understanding the sensitivity to compiler optimizations and software libraries can help to optimize and interpret the results appropriately. We observe more than 9000× variation in CPU runtimes and around 84× variation in GPU run times depending on the optimizations used.

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新的Gem：不可避免的内核及其对编译器优化和库的敏感性

当SPEC基准套件于1989年首次组装时，矩阵乘法代码matrix300是套件中的10个程序之一，但由于矩阵乘法对编译器优化的高度敏感性，它在2-3年内被丢弃。然而，随着机器学习（ML），神经网络和生成式人工智能（GenAI）的出现，矩阵乘法是现代计算工作量的一个组成部分。虽然很敏感，但通用矩阵乘法（GEMM）不能再被忽视了，特别是在评估运行ML工作负载的硬件时。本文研究了GEMM工作负载对库和编译器优化的敏感性。虽然使用多内核作为基准来理解机器学习加速器的性能可能是不可避免的，但理解对编译器优化和软件库的敏感性可以帮助适当地优化和解释结果。根据所使用的优化，我们观察到CPU运行时间的变化超过9000倍，GPU运行时间的变化约为84倍。

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

IEEE Computer Architecture Letters COMPUTER SCIENCE, HARDWARE & ARCHITECTURE-

CiteScore

4.60

自引率

4.30%

发文量

期刊介绍： IEEE Computer Architecture Letters is a rigorously peer-reviewed forum for publishing early, high-impact results in the areas of uni- and multiprocessor computer systems, computer architecture, microarchitecture, workload characterization, performance evaluation and simulation techniques, and power-aware computing. Submissions are welcomed on any topic in computer architecture, especially but not limited to: microprocessor and multiprocessor systems, microarchitecture and ILP processors, workload characterization, performance evaluation and simulation techniques, compiler-hardware and operating system-hardware interactions, interconnect architectures, memory and cache systems, power and thermal issues at the architecture level, I/O architectures and techniques, independent validation of previously published results, analysis of unsuccessful techniques, domain-specific processor architectures (e.g., embedded, graphics, network, etc.), real-time and high-availability architectures, reconfigurable systems.