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In the context of the genome-wide association studies (GWAS), one has tosolve long sequences of generalized least-squares problems; such a task has twolimiting factors: execution time --often in the range of days or weeks-- anddata management --data sets in the order of Terabytes. We present an algorithmthat obviates both issues. By pipelining the computation, and thanks to asophisticated transfer strategy, we stream data from hard disk to main memoryto GPUs and achieve sustained peak performance; with respect to ahighly-optimized CPU implementation, our algorithm shows a speedup of 2.6x.Moreover, the approach lends itself to multiple GPUs and attains almost perfectscalability. When using 4 GPUs, we observe speedups of 9x over theaforementioned implementation, and 488x over a widespread biology library.

Streaming Data from HDD to GPUs for Sustained Peak Performance