Large-Scale Genome-Wide Association Studies on a GPU Cluster Using a CUDA-Accelerated PGAS Programming Model

Abstract Detecting epistasis, such as 2-SNP interac-tions, in Genome-Wide Association Studies (GWAS) is an important but time consuming operation. Consequently, GPUs have already been used to accelerate these studies, reducing the runtime for moderately-sized datasets to less than one hour. However, single-GPU approaches cannot perform large-scale GWAS in reasonable time. In this work we present multiEpistSearch, a tool to detect epistasis that works on GPU clusters. While CUDA is used for paralleliza-tion within each GPU, the workload distribution among GPUs is performed with Unified Parallel C++ (UPC++), a novel extension of C++ that follows the Partitioned Global Address Space (PGAS) model. multiEpistSearch is able to analyze large-scale datasets with 5M SNPs from 10K indi-viduals in less than 3 hours using 24 NVIDIA GTX Titans