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Large-scale genome-wide association studies on a GPU cluster using a CUDA-accelerated PGAS programming model
dc.contributor.author | González-Domínguez, Jorge | |
dc.contributor.author | Kässens, Jan Christian | |
dc.contributor.author | Wienbrandt, Lars | |
dc.contributor.author | Schmidt, Bertil | |
dc.date.accessioned | 2018-08-16T11:53:41Z | |
dc.date.available | 2018-08-16T11:53:41Z | |
dc.date.issued | 2015 | |
dc.identifier.citation | 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. | es_ES |
dc.identifier.issn | 1094-3420 | |
dc.identifier.issn | 1741-2846 | |
dc.identifier.uri | http://hdl.handle.net/2183/20970 | |
dc.description.abstract | [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. | es_ES |
dc.description.sponsorship | London. Wellcome Trust; 076113 | es_ES |
dc.description.sponsorship | London. Wellcome Trust; 085475 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Sage Publications Ltd. | es_ES |
dc.relation.uri | https://doi.org/10.1177/1094342015585846 | es_ES |
dc.subject | PGAS | es_ES |
dc.subject | CUDA | es_ES |
dc.subject | GPU | es_ES |
dc.subject | UPC++ | es_ES |
dc.subject | Bioinformatics | es_ES |
dc.title | Large-scale genome-wide association studies on a GPU cluster using a CUDA-accelerated PGAS programming model | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | International Journal of High Performance Computing Applications | es_ES |
UDC.volume | 29 | es_ES |
UDC.issue | 4 | es_ES |
UDC.startPage | 506 | es_ES |
UDC.endPage | 510 | es_ES |
dc.identifier.doi | 10.1177/1094342015585846 |
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