期刊
PARALLEL COMPUTING
卷 35, 期 8-9, 页码 429-440出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.parco.2009.05.002
关键词
Short read mapping; GPGPU; Suffix trees; CUDA
资金
- National Institutes of Health [R01-LM006845, R01-GM083873]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [0844494] Funding Source: National Science Foundation
MUMmerGPU uses highly-parallel commodity graphics processing units (GPU) to accelerate the data-intensive computation of aligning next generation DNA sequence data to a reference sequence for use in diverse applications such as disease genotyping and personal genomics. MUMmerGPU 2.0 features a new stackless depth-first-search print kernel and is 13 x faster than the serial CPU version of the alignment code and nearly 4x faster in total computation time than MUMmerGPU 1.0. We exhaustively examined 128 GPU data layout configurations to improve register footprint and running time and conclude higher occupancy has greater impact than reduced latency. MUMmerGPU is available open-source at http://www.mummergpu.sourceforge.net. (C) 2009 Elsevier B.V. All rights reserved.
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