Name
Abstract | ||
|---|---|---|
Finding regions of local similarity between biological sequences is a fundamental task in computational biology. BLAST is the most widely-used tool for this purpose, but it suffers from irregularities due to its heuristic nature. To achieve fast search, recent approaches construct the index from the database instead of the input query. However, database indexing introduces more challenges in the design of index structure and algorithm, especially for data access through the memory hierarchy on modern multicore processors. In this paper, based on existing heuristic algorithms, we design and develop a database indexed BLAST with the identical sensitivity as query indexed BLAST (i.e., NCBI-BLAST). Then, we identify that existing heuristic algorithms of BLAST can result in serious irregularities in database indexed search. To eliminate irregularities in BLAST algorithm, we propose muBLASTP, that uses multiple optimizations to improve data locality and parallel efficiency for multicore architectures and multi-node systems. Experiments on a single node demonstrate up to a 5.1-fold speedup over the multi-threaded NCBI BLAST. For the inter-node parallelism, we achieve nearly linear scaling on up to 128 nodes and gain up to 8.9-fold speedup over mpiBLAST. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/IPDPS.2017.120 | 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS) |
Keywords | Field | DocType |
BLAST,pairwise sequence alignment,Database index,Multicore,MPI | Locality,Heuristic,Memory hierarchy,Computer science,Parallel computing,Database index,Data access,Indexed search,Multi-core processor,Distributed computing,Speedup | Conference |
ISSN | ISBN | Citations |
1530-2075 | 978-1-5386-3915-3 | 2 |
PageRank | References | Authors |
0.36 | 27 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing Zhang | 1 | 70 | 6.53 |
| Sanchit Misra | 2 | 124 | 14.99 |
| Hao Wang | 3 | 402 | 24.64 |
| Wu-chun Feng | 4 | 2812 | 232.50 |