GPU-Accelerated Clustering for Financial Instruments at Scale | NVIDIA Technical Blog

This post introduces AdaptGrow, a GPU-accelerated symmetric non-negative matrix factorization (SymNMF) solver for financial instrument clustering. It details a memory-efficient formulation reducing storage from ~20n² to ~4n² bytes, enabling factorization of ~100,000 instruments on a single NVIDIA GB200 GPU. A distributed implementation scales to one million instruments across 64 GB200 GPUs by row-sharding the dependence matrix and using NCCL for O(nk) communication. AdaptGrow auto-configures its solver (full-batch AdaGrad or block-stochastic SVRG) based on the matrix eigenspectrum, providing a unified interface for correlation and tail-dependence matrices. The workflow includes stability tracking using Adjusted Rand Index and detection of co-crash events via TPDM.