.png){kind=icon}
.png){kind=icon}
Return
A reconfigurable accelerator for Smith-Waterman algorithm
.png){kind=icon}
PRE
AI
DOI：10.1109/TCSII.2007.909857.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Scanning bio-sequence database and finding similarities among DNA and protein sequences is basic and important work in bioinformatics field. To solve this problem, Needleman-Wunschh (NW) algorithm is a classical and precise tool, and Smith-Waterman (SW) algorithm is more practical for its capability to find similarities between subsequences. Such algorithms have computational complexity proportional to the length product of both involved sequences, hence processing time becomes insufferable due to exponential growth speed and great amount of bio-sequence database. To alleviate this serious problem, a reconfigurable accelerator for SW algorithm is presented. In the accelerator, a modified equation is proposed to improve mapping efficiency of a processing element (PE), and a special floor plan is applied to a fine-grain parallel PE array and interface components to cut down their routing delay. Basing on the two techniques, the proposed accelerator can reach at 82-MHz frequency in an Altera EP1S30 device. Experiments demonstrate the accelerator provides more than 330 speedup as compared to a standard desktop platform with a 2.8-GHz Xeon processor and 4-GB memory and has 50% improvement on the peak performance of a transferred traditional implementation without using the two special techniques. Our implementation is also about 9% faster than the fastest implementation in a most recent family of SW algorithm accelerators.
Keywords:
bioinformatics
computational complexity
field-programmable gate array (FPGA)
reconfigurable accelerator
Smith-Waterman (SW) algorithm
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
4.9
Papers:
8.8K
Citations:
2.5W