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arXiv · 2208.05872

Optimizing Irregular-Shaped Matrix-Matrix Multiplication on Multi-Core DSPs

Shangfei Yin·Qinglin Wang·Ruochen Hao·Tianyang Zhou·Songzhu Mei·Jie Liu
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Abstract

General Matrix Multiplication (GEMM) has a wide range of applications in scientific simulation and artificial intelligence. Although traditional libraries can achieve high performance on large regular-shaped GEMMs, they often behave not well on irregular-shaped GEMMs, which are often found in new algorithms and applications of high-performance computing (HPC). Due to energy efficiency constraints, low-power multi-core digital signal processors (DSPs) have become an alternative architecture in HPC systems. Targeting multi-core DSPs in FT-m7032, a prototype CPU-DSPs heterogeneous processor for HPC, an efficient implementation - ftIMM - for three types of irregular-shaped GEMMs is proposed. FtIMM supports automatic generation of assembly micro-kernels, two parallelization strategies, and auto-tuning of block sizes and parallelization strategies. The experiments show that ftIMM can get better performance than the traditional GEMM implementations on multi-core DSPs in FT-m7032, yielding on up to 7.2x performance improvement, when performing on irregular-shaped GEMMs. And ftIMM on multi-core DSPs can also far outperform the open source library on multi-core CPUs in FT-m7032, delivering up to 3.1x higher efficiency.

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Shangfei Yin, Qinglin Wang, Ruochen Hao, Tianyang Zhou, Songzhu Mei, Jie Liu. 2022-08-11. Optimizing Irregular-Shaped Matrix-Matrix Multiplication on Multi-Core DSPs. https://arxiv.org/abs/2208.05872

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