AutoTSMM：一种在CPU上构建高性能高瘦矩阵乘法的自动调优框架
AutoTSMM: An Auto-tuning Framework for Building High-Performance Tall-and-Skinny Matrix-Matrix Multiplication on CPUs
AI总结：
本文提出自动调优框架AutoTSMM，在CPU上通过安装时选择最优内核和运行时生成执行计划，构建高性能高瘦矩阵乘法，性能优于传统实现。
摘要：
近年来，输入矩阵形状不规则的通用矩阵乘法在深度学习等众多应用中得到广泛使用，并受到越来越多的关注。然而，传统实现并不适用于形状不规则的矩阵乘法，且很少有工作专注于在CPU上优化高瘦矩阵乘法。本文提出了一种自动调优框架AutoTSMM，用于构建高性能的高瘦矩阵乘法。AutoTSMM在安装时阶段选择最优内部内核，并在运行时阶段为预打包的高瘦矩阵乘法生成执行计划。实验表明，AutoTSMM与最先进的高瘦矩阵乘法相比达到了具有竞争力的性能，并且优于所有传统矩阵乘法实现。
英文摘要：
In recent years, general matrix-matrix multiplication with non-regular-shaped input matrices has been widely used in many applications like deep learning and has drawn more and more attention. However, conventional implementations are not suited for non-regular-shaped matrix-matrix multiplications, and few works focus on optimizing tall-and-skinny matrix-matrix multiplication on CPUs. This paper proposes an auto-tuning framework, AutoTSMM, to build high-performance tall-and-skinny matrix-matrix multiplication. AutoTSMM selects the optimal inner kernels in the install-time stage and generates an execution plan for the pre-pack tall-and-skinny matrix-matrix multiplication in the runtime stage. Experiments demonstrate that AutoTSMM achieves competitive performance comparing to state-of-the-art tall-and-skinny matrix-matrix multiplication. And, it outperforms all conventional matrix-matrix multiplication implementations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
