Researchblasperformance tuningmulticoreml optimization
Machine Learning Selects Optimal Threads for GEMM
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7.9
Yufan Xia (arXiv preprint submitted Jan 14, 2026) presents a proof-of-concept ADSALA library that uses an on-the-fly machine learning model to select optimal thread counts for GEMM. Tests on two-socket Intel Cascade Lake and two-socket AMD Zen 3 nodes report 25–40% speedups versus traditional BLAS GEMM for workloads with up to 100 MB memory usage. The approach targets multi-core shared-memory tuning complexity.
Scoring Rationale
Strong cross-architecture ML optimization and actionable speedups, limited by single arXiv preprint validation and 100 MB workload scope.
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