vix.ing · top · new · best · stats · spec

Demystifying ARM SME to Optimize General Matrix Multiplications

2025/12/25 by Chencheng Deng, Deng, Chencheng, Weiling Yang +5 · 11 voices
Computer Science · Engineering · #Low-power high-performance VLSI design #Numerical Methods and Algorithms #Parallel Computing and Optimization Techniques #cs.DC

paper · pdf · doi:10.48550/arxiv.2512.21473

openalex publication_date 2025/12/25 · openalex created_date 2025/12/30 · openalex updated_date 2026/07/28

Abstract

General Matrix Multiplication (GEMM) is a critical kernel in high-performance computing and deep learning. While modern architectures like ARM's Scalable Matrix Extension (SME) introduce dedicated hardware for matrix operations, existing linear algebra libraries fail to fully exploit its potential, particularly for large matrices. This paper presents MpGEMM, an open-source library that leverages key architectural features of SME to optimize GEMM across multiple precisions. Through a systematic characterization of SME, we derive optimization guidelines that inform our design. MpGEMM employs cache-aware partitioning, efficient data packing with on-the-fly transposition, and specialized micro-kernels that utilize multi-vector loads and all available tile registers. Evaluated on an Apple M4 Pro with real-world workloads from DeepSeek and LLaMA, MpGEMM achieves an average speedup of 1.23x over the vendor-optimized Apple Accelerate library and significantly outperforms other open-source alternatives.

Discussions

Related