A Machine Learning Approach Towards Runtime Optimisation of Matrix Multiplication

• URL: http://arxiv.org/abs/2601.09114v1
• Date: Wed, 14 Jan 2026 03:28:54 GMT
• Title: A Machine Learning Approach Towards Runtime Optimisation of Matrix Multiplication
• Authors: Yufan Xia, Marco De La Pierre, Amanda S. Barnard, Giuseppe Maria Junior Barca,
• Abstract summary: We present a proof-of-concept approach to building an Architecture and Data- AwareStructure Linear Algebra (ADSALA) software library.<n>Our method uses a machine learning model on-the-fly to automatically select the optimal number of threads for a given GEMM task.<n>Test results on two different HPC node architectures, one based on a two-socket Intel Cascade Lake and the other on a two-socket AMD Zen 3, revealed a 25 to 40 per cent speedup.
• Score: 1.5223740593989443
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The GEneral Matrix Multiplication (GEMM) is one of the essential algorithms in scientific computing. Single-thread GEMM implementations are well-optimised with techniques like blocking and autotuning. However, due to the complexity of modern multi-core shared memory systems, it is challenging to determine the number of threads that minimises the multi-thread GEMM runtime. We present a proof-of-concept approach to building an Architecture and Data-Structure Aware Linear Algebra (ADSALA) software library that uses machine learning to optimise the runtime performance of BLAS routines. More specifically, our method uses a machine learning model on-the-fly to automatically select the optimal number of threads for a given GEMM task based on the collected training data. Test results on two different HPC node architectures, one based on a two-socket Intel Cascade Lake and the other on a two-socket AMD Zen 3, revealed a 25 to 40 per cent speedup compared to traditional GEMM implementations in BLAS when using GEMM of memory usage within 100 MB.

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