Accelerating HMMER on GPUs by implementing hybrid data and task parallelism

Authors:
Narayan Ganesan;Roger D. Chamberlain;Jeremy Buhler;Michela Taufer
Affiliations:
University of Delaware, Newark, DE;Washington University in St. Louis, St. Louis, MO;Washington University in St. Louis, St. Louis, MO;University of Delaware, Newark, DE
Venue:
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Year:
2010

Citing 4
Cited 0

ClawHMMER: A Streaming HMMer-Search Implementatio

SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Accelerator design for protein sequence HMM search

Proceedings of the 20th annual international conference on Supercomputing
Integrating FPGA acceleration into HMMer

Parallel Computing
Evaluating the use of GPUs in liver image segmentation and HMMER database searches

IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Many biologically motivated problems are expressed as dynamic programming recurrences and are difficult to parallelize due to the intrinsic data dependencies in their algorithms. Therefore their solutions have been sped up using task level parallelism only. Emerging platforms such as GPUs are appealing parallel architectures for high-performance; at the same time they are a motivation to rethink the algorithms associated with these problems, to extract finer-grained parallelism such as data parallelism. In this paper, we consider the hmmersearch program as a representative of these problems and we re-design its computational algorithm to extract data parallelism for a more efficient execution on emerging platforms, despite the fact that hmmersearch has data dependencies. Our approach outperforms other existing methods when searching a very large database of unsorted sequences on GPUs.