GWAS on GPUs: Streaming Data from HDD for Sustained Performance
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.
KeywordsGWAS generalized least-squares computational biology out-of-core computation high-performance multiple GPUs data transfer multibuffering streaming big data
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