GWAS on GPUs: Streaming data from HDD for sustained performance

Abstract

In the context of genome-wide association studies (GWAS), one has to solve long sequences of generalized least-squares problems; such a task has two limiting factors: execution time -often in the range of days or weeks- and data management -data sets in the order of Terabytes. We present an algorithm that obviates both issues. By pipelining the computation, and thanks to a sophisticated transfer mechanism, we stream data from hard disk to main memory to GPUs and achieve sustained performance; with respect to a highly-optimized CPU implementation, our algorithm shows a speedup of 2.6x. Moreover, the approach lends itself to multiple GPUs and attains almost perfect scalability. When using 4 GPUs, we observe speedups of 9x over the aforementioned CPU implementation, and 488x over ProbABEL, a widespread biology library. © 2013 Springer-Verlag.