FloatSOM实现大规模自组织映射，支持多卡加速与内存超限数据流处理。
FloatSOM: GPU-Accelerated, Distributed, Topology-Flexible Self-Organizing Maps
- 采用多GPU并行与磁盘流式存储，突破显存限制
- 10亿样本、50维数据在8卡上6.16分钟完成训练
- 支持非规则拓扑结构，降低量化误差，适合大模型部署
GPU加速的自组织映射（SOM）在大规模分析中表现优异，但数据量增长导致显存不足。本文提出FloatSOM框架，支持多GPU执行、磁盘回溯的流式处理以及新型非规则拓扑结构。在14个合成与真实基准数据集上评估，结合拓扑感知超参数调优，其量化误差优于当前最优基线。在最大规模实验中，8张GPU跨两台高性能计算节点，在6.16分钟内训练了包含1024个节点的SOM网络，处理10亿条、每条50维的样本数据。
原文摘要 · Abstract (English)
GPU-accelerated Self-Organizing Map (SOM) implementations are among the most competitive options for large-scale SOM analysis, but growing dataset sizes increasingly challenge their practical use because workloads no longer fit cleanly within device-memory limits. We introduce FloatSOM, a SOM framework for scalable training and deployment that supports multi-GPU execution, out-of-memory disk-backed streaming, and novel topologies beyond regular lattices. We evaluate FloatSOM on 14 synthetic and real benchmark datasets together with controlled speed scaling benchmarks, and show that these improved topologies, combined with topology-aware hyperparameter fine-tuning, yield lower quantization error than current state-of-the-art SOM baselines. FloatSOM also sustains this performance at large scale with high-throughput distributed execution; in the largest benchmark, it trains a 1024-node SOM network on 1,000,000,000 samples with 50 features in 6.16 minutes on 8 GPUs across two separate high-performance-computing nodes.
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