Optimizing performance, cost, and sensitivity in pairwise sequence search on a cluster of PlayStations

The Smith-Waterman algorithm is a dynamic programming method for determining optimal local alignments between nucleotide or protein sequences. However, it suffers from quadratic time and space complexity. As a result, many algorithmic and architectural enhancements have been proposed to solve this problem, but at the cost of reduced sensitivity in the algorithms or significant expense in hardware, respectively. This paper presents a highly efficient parallelization of the Smith-Waterman algorithm on the Cell Broadband Engine platform, a novel hybrid multicore architecture that drives the low-cost PlayStation 3 (PS3) game consoles as well as the IBM BladeCenter Q22, which currently powers the fastest supercomputer in the world, Roadrunner at Los Alamos National Laboratory. Through an innovative mapping of the optimal Smith-Waterman algorithm onto a cluster of PlayStation 3 nodes, our implementation delivers 21 to 55-fold speed-up over a high-end multicore architecture and up to 449-fold speed-up over the PowerPC processor in the PS3. Next, we evaluate the trade-offs between our Smith- Waterman implementation on the Cell with existing software and hardware implementations and show that our solution achieves the best performance-to-price ratio, when aligning realistic sequences sizes and generating the actual alignment. Finally, we show that our low-cost solution on a PS3 cluster approaches the speed of BLAST while achieving ideal sensitivity. To quantify the relationship between the two algorithms in terms of speed and sensitivity, we formally define and quantify the sensitivity of homology search methods so that trade-offs between sequence-search solutions can be evaluated in a quantitative manner.