GPU accelerated population genetics statistics using pg_gpu

Population genetics summary statistics– diversity, divergence, linkage disequilibrium, selection scans, and dimensionality reduction– are fundamental across human, agricultural, and ecological genomics. As whole-genome sequencing datasets have grown to hundreds of thousands of individuals, the cost of computing these statistics on conventional CPU implementations has become a major bottleneck: windowed scans of a single chromosome arm can take hours to days, and computation of pairwise linkage-disequilibrium statistics useful for demographic inference scales as O(n^2) in sample size, often exceeding wall-clock budgets entirely. We present pg_gpu, a Python library implementing a comprehensive catalog of population-genetics summary statistics as fused CUDA kernels on NVIDIA GPUs. pg_gpu covers eleven categories spanning diversity and neutrality tests, divergence, admixture, the site-frequency spectrum, linkage disequilibrium, haplotype-based selection scans, dimensionality reduction (PCA, randomized PCA, local PCA / lostruct), distance distributions, relatedness, resampling, and a generalized weighted-SFS framework for custom theta estimators. On the full Ag1000G Phase 3 chromosome 3R arm (2,940 haplotypes, 10.9 million variants) pg_gpu agrees with scikit-allel and PLINK2 to machine precision while delivering a median 139x and maximum 1,096x speedup. For the multi-population LD statistics used by moments for demographic inference, pg_gpu is a drop-in replacement that yields a ~1,750-fold speedup over the native implementation. Whole chromosome arm scans, lostruct screens, and calculation of LD statistics complete on a single NVIDIA A100 in seconds to a few minutes.