Scaling computational genomics to millions of individuals with GPUs.
Genome Biol
; 20(1): 228, 2019 11 01.
Article
em En
| MEDLINE
| ID: mdl-31675989
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Genômica
Tipo de estudo:
Evaluation_studies
Idioma:
En
Revista:
Genome Biol
Ano de publicação:
2019
Tipo de documento:
Article