Large-scale genome-wide association studies on a GPU cluster using a CUDA-accelerated PGAS programming model
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Large-scale genome-wide association studies on a GPU cluster using a CUDA-accelerated PGAS programming modelData
2015Cita bibliográfica
González-Domínguez, J., Kässens, J. C., Wienbrandt, L., & Schmidt, B. (2015). Large-scale genome-wide association studies on a GPU cluster using a CUDA-accelerated PGAS programming model. The International Journal of High Performance Computing Applications, 29(4), 506-510.
Resumo
[Abstract] Detecting epistasis, such as 2-SNP interactions, in genome-wide association studies (GWAS) is an important but time consuming operation. Consequently, GPUs have already been used to accelerate these studies, reducing the runtime for moderately-sized datasets to less than 1 hour. However, single-GPU approaches cannot perform large-scale GWAS in reasonable time. In this work we present multiEpistSearch, a tool to detect epistasis that works on GPU clusters. While CUDA is used for parallelization within each GPU, the workload distribution among GPUs is performed with Unified Parallel C++ (UPC++), a novel extension of C++ that follows the Partitioned Global Address Space (PGAS) model. multiEpistSearch is able to analyze large-scale datasets with 5 million SNPs from 10,000 individuals in less than 3 hours using 24 NVIDIA GTX Titans.
Palabras chave
PGAS
CUDA
GPU
UPC++
Bioinformatics
CUDA
GPU
UPC++
Bioinformatics
Versión do editor
ISSN
1094-3420
1741-2846
1741-2846