Scaling computational genomics to millions of individuals with GPUs.

Genome Biol
Authors
Abstract

Current genomics methods are designed to handle tens to thousands of samples but will need to scale to millions to match the pace of data and hypothesis generation in biomedical science. Here, we show that high efficiency at low cost can be achieved by leveraging general-purpose libraries for computing using graphics processing units (GPUs), such as PyTorch and TensorFlow. We demonstrate > 200-fold decreases in runtime and ~ 5-10-fold reductions in cost relative to CPUs. We anticipate that the accessibility of these libraries will lead to a widespread adoption of GPUs in computational genomics.

Year of Publication
2019
Journal
Genome Biol
Volume
20
Issue
1
Pages
228
Date Published
2019 Nov 01
ISSN
1474-760X
DOI
10.1186/s13059-019-1836-7
PubMed ID
31675989
PubMed Central ID
PMC6823959
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