4.3 Article

Optimizing data intensive GPGPU computations for DNA sequence alignment

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

  1. National Institutes of Health [R01-LM006845, R01-GM083873]
  2. Direct For Computer & Info Scie & Enginr
  3. Div Of Information & Intelligent Systems [0844494] Funding Source: National Science Foundation

向作者/读者索取更多资源

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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