1. AutoTSMM: An Auto-tuning Framework for Building High-Performance Tall-and-Skinny Matrix-Matrix Multiplication on CPUs
- Author
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Li, Chendi, Jia, Haipeng, Cao, Hang, Yao, Jianyu, Shi, Boqian, Xiang, Chunyang, Sun, Jinbo, Lu, Pengqi, and Zhang, Yunquan
- Subjects
Computer Science - Distributed, Parallel, and Cluster Computing ,D.1.3 - 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., Comment: 8 pages, 12 figures, published in IEEE ISPA 2021
- Published
- 2022
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