ASM-SpMM: Unleashing the Potential of Arm SME for Sparse Matrix Multiplication Acceleration
Jiazhi Jiang, Xijia Yao, Jiayu Chen, Jinhui Wei, Dan Huang, Yutong Lu
2026年份
摘要
Sparse Matrix–Matrix Multiplication (SpMM) is a core kernel in scientific computing, data analytics, and artificial intelligence, supporting applications such as linear solvers and Graph Neural Networks (GNNs). The Scalable Matrix Extension (SME) in Armv9 introduces dedicated matrix acceleration for ARM CPUs, but exploiting its full potential for SpMM requires architecture-aware optimizations to address irregular sparsity and hardware constraints.
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