Isaac T S Li1, Warren Shum, Kevin Truong. 1. Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada. isaac.li@utoronto.ca <isaac.li@utoronto.ca>
Abstract
BACKGROUND: To infer homology and subsequently gene function, the Smith-Waterman (SW) algorithm is used to find the optimal local alignment between two sequences. When searching sequence databases that may contain hundreds of millions of sequences, this algorithm becomes computationally expensive. RESULTS: In this paper, we focused on accelerating the Smith-Waterman algorithm by using FPGA-based hardware that implemented a module for computing the score of a single cell of the SW matrix. Then using a grid of this module, the entire SW matrix was computed at the speed of field propagation through the FPGA circuit. These modifications dramatically accelerated the algorithm's computation time by up to 160 folds compared to a pure software implementation running on the same FPGA with an Altera Nios II softprocessor. CONCLUSION: This design of FPGA accelerated hardware offers a new promising direction to seeking computation improvement of genomic database searching.
BACKGROUND: To infer homology and subsequently gene function, the Smith-Waterman (SW) algorithm is used to find the optimal local alignment between two sequences. When searching sequence databases that may contain hundreds of millions of sequences, this algorithm becomes computationally expensive. RESULTS: In this paper, we focused on accelerating the Smith-Waterman algorithm by using FPGA-based hardware that implemented a module for computing the score of a single cell of the SW matrix. Then using a grid of this module, the entire SW matrix was computed at the speed of field propagation through the FPGA circuit. These modifications dramatically accelerated the algorithm's computation time by up to 160 folds compared to a pure software implementation running on the same FPGA with an Altera Nios II softprocessor. CONCLUSION: This design of FPGA accelerated hardware offers a new promising direction to seeking computation improvement of genomic database searching.