期刊
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
卷 79-80, 期 -, 页码 132-142出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jpdc.2014.08.009
关键词
Homology detection; Pairwise sequence alignment; Protein family identification; Dynamic load balancing; Work stealing; Distributed task counters; Parallel suffix tree construction
资金
- DOE Office of Science, Advanced Scientific Computing Research program [DE-SC-0006516]
- Laboratory Directed Research and Development program through the eXtreme Scale Computing Initiative at Pacific Northwest National Laboratory (PNNL)
- United States Department of Energy [DE-AC05-76RL01830]
- Office of Science of the US Department of Energy [DE-AC02-05CH11231]
Sequence homology detection is central to a number of bioinformatics applications including genome sequencing and protein family characterization. Given millions of sequences, the goal is to identify all pairs of sequences that are highly similar (or homologous) on the basis of alignment criteria. While there are optimal alignment algorithms to compute pairwise homology, their deployment for large-scale is currently not feasible; instead, heuristic methods are used at the expense of quality. Here, we present the design and evaluation of a parallel implementation for conducting optimal homology detection on distributed memory supercomputers. Our approach uses a combination of techniques from asynchronous load balancing (viz, work stealing, dynamic task counters), data replication, and exact-matching filters to achieve homology detection at scale. Results for 2.56 M sequences on up to 8K cores show parallel efficiencies of similar to 75%-100%, a time-to-solution of 33 s, and a rate of similar to 2.0 M alignments per second. (C) 2014 Elsevier Inc.
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