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arXiv · 2208.08088

AutoTSMM: An Auto-tuning Framework for Building High-Performance Tall-and-Skinny Matrix-Matrix Multiplication on CPUs

Chendi Li·Haipeng Jia·Hang Cao·Jianyu Yao·Boqian Shi·Chunyang Xiang·Jinbo Sun·Pengqi Lu·Yunquan Zhang
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Abstract

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.

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Chendi Li, Haipeng Jia, Hang Cao, Jianyu Yao, Boqian Shi, Chunyang Xiang, Jinbo Sun, Pengqi Lu, Yunquan Zhang. 2022-08-17. AutoTSMM: An Auto-tuning Framework for Building High-Performance Tall-and-Skinny Matrix-Matrix Multiplication on CPUs. https://doi.org/10.1109/ispa-bdcloud-socialcom-sustaincom52081.2021.00034

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