GPU-Accelerated Financial Clustering

This feature thread tracks the evolution of GPU-accelerated clustering techniques for financial instruments at scale. Initial efforts focused on developing AdaptGrow, a GPU-accelerated symmetric non-negative matrix factorization (SymNMF) solver capable of producing hard cluster labels, soft factor loadings, and structural-break signals from rolling correlation and tail pairwise dependence matrices. AdaptGrow features a memory-efficient formulation reducing peak storage and a distributed implementation row-sharding the dependence matrix and communicating O(nk) data via NCCL, scaling to one million instruments across 64 NVIDIA GB200 GPUs on 16 nodes. AdaptGrow auto-configures from the matrix eigenspectrum, choosing full-batch AdaGrad for clean spectral gaps and block-stochastic SVRG updates for flatter spectra, using the same solver interface from one GPU to many. Across 250 rolling windows approximating one trading year, adjusted Rand index (ARI) stability tracking with a self-calibrated 3σ control limit detects planted membership changes, while TPDM cross-sector dependence exposes co-crash events that correlation misses. Spherical k-means provides a lower-cost hard-clustering baseline for well-separated sectors, while SymNMF retains graded factor loadings when boundaries are ambiguous or the spectrum collapses toward a dominant factor.