| Exploiting graphics processing units for computational biology and bioinformatics. | |
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MedLine Citation:
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PMID: 20658333 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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Advances in the video gaming industry have led to the production of low-cost, high-performance graphics processing units (GPUs) that possess more memory bandwidth and computational capability than central processing units (CPUs), the standard workhorses of scientific computing. With the recent release of generalpurpose GPUs and NVIDIA's GPU programming language, CUDA, graphics engines are being adopted widely in scientific computing applications, particularly in the fields of computational biology and bioinformatics. The goal of this article is to concisely present an introduction to GPU hardware and programming, aimed at the computational biologist or bioinformaticist. To this end, we discuss the primary differences between GPU and CPU architecture, introduce the basics of the CUDA programming language, and discuss important CUDA programming practices, such as the proper use of coalesced reads, data types, and memory hierarchies. We highlight each of these topics in the context of computing the all-pairs distance between instances in a dataset, a common procedure in numerous disciplines of scientific computing. We conclude with a runtime analysis of the GPU and CPU implementations of the all-pairs distance calculation. We show our final GPU implementation to outperform the CPU implementation by a factor of 1700. |
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Authors:
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Joshua L Payne; Nicholas A Sinnott-Armstrong; Jason H Moore |
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Publication Detail:
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Type: Journal Article Date: 2010-07-25 |
Journal Detail:
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Title: Interdisciplinary sciences, computational life sciences Volume: 2 ISSN: 1913-2751 ISO Abbreviation: Interdiscip Sci Publication Date: 2010 Sep |
Date Detail:
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Created Date: 2010-07-26 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101515919 Medline TA: Interdiscip Sci Country: Germany |
Other Details:
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Languages: eng Pagination: 213-20 Citation Subset: IM |
Affiliation:
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Computational Genetics Laboratory, Department of Genetics, Dartmouth Medical School, Lebanon, NH, 03756, USA, Joshua.Payne@Dartmouth.edu. |
Export Citation:
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Descriptor/Qualifier:
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| Grant Support | |
ID/Acronym/Agency:
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R01 AI059694-06/AI/NIAID NIH HHS; R01 LM009012-04/LM/NLM NIH HHS; R01 LM010098-01/LM/NLM NIH HHS |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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