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arXiv Machine Learning By Andrew James Amos

From 80x to 385x: A Best-Matching-Unit Search at the L2 Roof, Measured Against a Symmetrically Tuned Baseline

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The paper reports a comprehensive tuning of both a novel sparse self‑organizing map algorithm (SparseBin) and its baseline cuSPARSE implementation. By optimizing four key levers—tile size, tile‑membership clustering, neuron‑axis chunking, and vectorised loads—the authors achieved a 5.6‑10.1× speed‑up per epoch for map sizes ranging from 32×32 to 512×512, and increased the performance margin over the CUDA baseline from ~80× to ~385×. The tuned kernel saturated the L2 bandwidth at 77% of peak, indicating that further performance gains are unlikely without new hardware or fundamentally different approaches.

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