Evaluating DNA sequence searching algorithms on multicore.
Sequence searching is one of major operations in modern bioengineering. Recent emerging multicore provides a promising technology to enhance sequence searching performance. However, efficiently employing the multicore for a uniprocessor-oriented algorithm is a difficult task. This paper presents a methodology to profile processing requirements for DNA sequence search algorithms, parallelise them onto multicore, and analytically evaluate their performance. The key feature of this methodology is that entire processes are automated and it requires users little understanding of the complexity of algorithms and multicore hardware architecture. Our methodology considers three approaches to parallelise searching operations: queries, database, and task segmentation.
Weng
Ning
N
Department of Electrical and Computer Engineering, Southern Illinois University, 1230 Lincoln Drive, Carbondale, IL 62901, USA. weng@engr.siu.edu
Soewito
Benfano
B
D000465:Algorithms; D001483:Base Sequence; D019295:Computational Biology; D003198:Computer Simulation; D003199:Computer Systems; D004247:DNA / Q000235:genetics*; D030561:Databases, Nucleic Acid; D010363:Pattern Recognition, Automated; D016415:Sequence Alignment / Q000706:statistics & numerical data*
D004247:DNA