Streaming Data from HDD to GPUs for Sustained Peak Performance

Lucas Beyer, Paolo Bientinesi
International European Conference on Parallel and Distributed Computing (Euro-Par'13) - Oral

In the context of the 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 strategy, we stream data from hard disk to main memory to GPUs and achieve sustained peak 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 implementation, and 488x over a widespread biology library.

» Show BibTeX

@inproceedings{Beyer2013GWAS,
author = {Lucas Beyer and Paolo Bientinesi},
title = {Streaming Data from HDD to GPUs for Sustained Peak Performance},
booktitle = {Euro-Par},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = {8097},
pages = {788-799},
year = {2013},
isbn = {3642400477},
ee = {http://arxiv.org/abs/1302.4332},
}




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