Abstract: In recent years,with the rapid adoption of ARM architecture processors in edge computing devices and cloud servers,as well as the increasingly critical role of sparse matrix multiplication(SPMM)in compute-intensive applications such as deep learning,research on sparse computing optimization for ARM platforms has become an academic hotspot.However,current SPMM solutions for ARM platforms primarily employ the ahead-of-time(AOT)compilation model,where all compilation is completed before program execution.Nevertheless,AOT solutions for SPMM face three major limitations:unnecessary memory accesses,additional branch overhead,and redundant instructions.This paper proposes ASPJIT,a just-in-time(JIT)assembly code generation framework for ARM platforms,designed to accelerate SPMM computation on ARM multi-core CPUs.ASPJIT dynamically optimizes the computation sequence using a sparse judgment-based column-to-row algorithm,leveraging runtime sparse feature analysis to significantly improve instruction-level parallelism(ILP).Additionally,ASPJIT reduces memory access latency by employing a register allocation strategy to cache frequently accessed data and maximizes arithmetic throughput by carefully selecting SIMD instruction sets.A performance evaluation of ASPJIT is conducted and compared it with two AOT baselines.The first involves existing SPMM implementations compiled with automatic vectorization using the ARM gcc compiler.The second utilizes optimized SPMM routines provided by ARM Eigen.The results demonstrate that ASPJIT delivers average speedups of 3.8x and 5.6x,respectively.

Key words: Sparse matrix-matrix multiplication(SPMM), Ahead-of-time(AOT), Just-in-time(JIT), Single instruction multiple data(SIMD), ARM multi-core CPUs