Fine-grained GPU parallelization of Pairwise Local Sequence Alignment

The Smith-Waterman algorithm is used in Bioinformatics to perform pairwise local alignment between a query sequence and a subject sequence. We present a GPU based parallel version of this algorithm that is able to perform pairwise alignment faster than previous algorithms. In particular, it parallelizes each alignment, rather than relying on parallelism across multiple pair alignments, which many other proposed GPU algorithms do. As a result it scales better. We further extend our algorithm to work efficiently on a cluster of GPUs. At a high level, our approach subdivides the iterative computation of elements of a matrix among blocks of processors such that each block can simply recompute the data it needs instead of waiting for other processors to compute them. Sometimes this may lead to excessive recomputation, however. We evaluate these cases and employ a hybrid approach, recomputing only limited data and communicating the rest. Our algorithm is also extended to produce not only the best but all 'best K' alignments. Our results on SSCA#1 benchmark show that our method is upto 5-24 times faster than previous method.