Microparallelism and high-performance protein matching
Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Scan primitives for GPU computing
Proceedings of the 22nd ACM SIGGRAPH/EUROGRAPHICS symposium on Graphics hardware
Sequence alignment with GPU: Performance and design challenges
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
The new SIMD Implementation of the Smith-Waterman Algorithm on Cell Microprocessor
Fundamenta Informaticae
Bio-sequence database scanning on a GPU
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
GPU accelerated smith-waterman
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
GPU accelerated simulations of 3D deterministic particle transport using discrete ordinates method
Journal of Computational Physics
Parallel syntenic alignment on GPUs
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Direct numerical simulation of turbulence using GPU accelerated supercomputers
Journal of Computational Physics
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Fast Longest Common Subsequence with General Integer Scoring Support on GPUs
Proceedings of Programming Models and Applications on Multicores and Manycores
Frequency-based re-sequencing tool for short reads on graphics processing units
International Journal of Computational Science and Engineering
International Journal of Computational Science and Engineering
Hi-index | 31.46 |
Finding regions of similarity between two very long data streams is a computationally intensive problem referred to as sequence alignment. Alignment algorithms must allow for imperfect sequence matching with different starting locations and some gaps and errors between the two data sequences. Perhaps the most well known application of sequence matching is the testing of DNA or protein sequences against genome databases. The Smith-Waterman algorithm is a method for precisely characterizing how well two sequences can be aligned and for determining the optimal alignment of those two sequences. Like many applications in computational science, the Smith-Waterman algorithm is constrained by the memory access speed and can be accelerated significantly by using graphics processors (GPUs) as the compute engine. In this work we show that effective use of the GPU requires a novel reformulation of the Smith-Waterman algorithm. The performance of this new version of the algorithm is demonstrated using the SSCA#1 (Bioinformatics) benchmark running on one GPU and on up to four GPUs executing in parallel. The results indicate that for large problems a single GPU is up to 45 times faster than a CPU for this application, and the parallel implementation shows linear speed up on up to 4GPUs.