aiqbench analysis
This study demonstrates a symmetrically tuned GPU implementation of Self-Organizing Map training that achieves near-L2 bandwidth saturation, significantly narrowing the performance gap between novel algorithms and established CUDA libraries. By optimizing four specific levers, the researchers pushed the algorithm to its hardware limits, proving that further gains are currently bounded by memory bandwidth rather than computational logic. This work is critical for practitioners deploying large-scale unsupervised learning models where training speed and resource efficiency directly impact scalability.
From 80x to 385x: A Best-Matching-Unit Search at the L2 Roof, Measured Against a Symmetrically Tuned Baseline
arXiv:2609.05138v1 Announce Type: new Abstract: Comparisons between GPU implementations are usually asymmetric: one side is tuned by its author, the other is run as found. I report a programme that tuned both a novel SOM algorithm (SparseBin) and the baseline algorithm it was being compared to (cuSPARSE). The best-matching-unit search that dominates self-organizing map training was tuned through four levers - tile size, tile-membership clustering, neuron-axis chunking and vectorised loads - reaching 5.6-10.1x per epoch over the previously published configuration at map sizes from 32x32 to 512x512, and lifting the margin over the CUDA implementation behind our earlier MEDLINE atlases from ~80x to ~385x. cuSPARSE, the implementation SparseBin is compared against, received every lever with an analogue on its side, and became 2-3x faster in the process. The tuned kernel pressed the L2 bandwidth roof at 77% of peak with every other unit at 40-65%, bounding any further lever at ~1.3x - a terminal result rather than a waypoint, and every untested lever was either capped by that bound by construction or measured null.
Abstract:Comparisons between GPU implementations are usually asymmetric: one side is tuned by its author, the other is run as found. I report a programme that tuned both a novel SOM algorithm (SparseBin) and the baseline algorithm it was being compared to (cuSPARSE). The best-matching-unit search that dominates self-organizing map training was tuned through four levers - tile size, tile-membership clustering, neuron-axis chunking and vectorised loads - reaching 5.6-10.1x per epoch over the previously published configuration at map sizes from 32x32 to 512x512, and lifting the margin over the CUDA implementation behind our earlier MEDLINE atlases from ~80x to ~385x. cuSPARSE, the implementation SparseBin is compared against, received every lever with an analogue on its side, and became 2-3x faster in the process. The tuned kernel pressed the L2 bandwidth roof at 77% of peak with every other unit at 40-65%, bounding any further lever at ~1.3x - a terminal result rather than a waypoint, and every untested lever was either capped by that bound by construction or measured null.
15 pages, 8 tables, 3 figures. Companion to arXiv:2608.24067. Both implementations were tuned symmetrically; cuSPARSE became 2-3x faster in the process. Code, data and frozen results: doi:https://doi.org/10.5281/zenodo.22245712 (tag v2.0)
Machine Learning (cs.LG)
arXiv:2609.05138 [cs.LG]
(or arXiv:2609.05138v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.05138 arXiv-issued DOI via DataCite (pending registration)
Full text under the publisher's open licence · 216 words
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