MpGEMM: Optimizing General Matrix Multiplication for ARM's Scalable Matrix Extension Architecture
By
matt_d
A respectable bake. You'd come back tomorrow for another.
Summary
This research paper presents MpGEMM, an open-source library that optimizes General Matrix Multiplication (GEMM) for ARM's Scalable Matrix Extension (SME) architecture. The paper systematically characterizes SME hardware features to develop optimization guidelines, then implements cache-aware partitioning, efficient data packing with on-the-fly transposition, and specialized micro-kernels using multi-vector loads and tile registers. Evaluated on Apple M4 Pro with real-world workloads from DeepSeek and LLaMA, MpGEMM achieves an average 1.23x speedup over Apple's Accelerate library and outperforms other open-source alternatives.
Key quotes
· 4 pulledGeneral 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.
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.
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