A Feature-Major Codebook for Memory-Efficient Sparse-Binary Self-Organizing Maps: Scaling a MEDLINE Atlas to 1.05 Million Neurons on a Single Consumer GPU

Abstract

Building a self-organising map at MEDLINE scale has been impractical: the best-matching-unit (BMU) search that dominates training is bound by the bandwidth needed to read the codebook every epoch. I show that this bottleneck is largely an artefact of codebook layout. Storing it feature-major with each feature's weights contiguous, W[v.M+i], recasts the search as a tiled sparse-dense product in which every loaded weight column is reused across a tile of samples. Varying only the layout, with implementation, precision and update rule held fixed, accelerates the BMU search by 4.5-8.5x, and because an exact-argmin BMU is invariant to codebook layout, this costs nothing: held-out quantisation error agrees with a cuSPARSE baseline to within 0.5% at every map size. The advantage is a crossover: cuSPARSE$.$SOM is faster at small maps, SparseBin$.$SOM is 1.5x faster at 128x128 and 2.6x at 256x256, and at 512x512 it is the only one that runs on 24 GB without re-engineering its memory path. Paired with a radius-independent box-blur update and a convergence-based stopping rule, it trains a converged map over 29.9 million MEDLINE articles in about 72 s at 64x64 on one 24 GB GPU, and fits 262,144 neurons (512x512) where every alternative I tested exceeds memory; on a 141 GB H200 it reaches 1,048,576 neurons (1024x1024), to my knowledge the largest self-organising map yet reported. Held-out error follows a smooth power law with no elbow across three decades of map size. At matched work, in the configuration benchmarked here, the design is ~82x faster than MedSOM and, at 128x128, 621x faster than the best multicore-CPU library. A post-submission addendum, tuning both implementations symmetrically, accelerates the search a further 5.6-10.1x, brings that 64x64 run to about 13 s, removes the crossover, raises those margins to ~385x and ~3,000x, and narrows two mechanism claims.