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Accelerating HMMER on GPUs by implementing hybrid data and task parallelism

Published: 02 August 2010 Publication History

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.

References

[1]
Compute Unified Device Architecture. http://www.nvidia.com/cuda.
[2]
NCBI NR Protein Database. ftp://ftp.ncbi.nih.gov/blast/db/FASTA/nr.gz.
[3]
Eddy, S. Profile hidden Markov models. Bioinformatics 14 (1998), 755--863.
[4]
Eddy, S. HMMER: Profile HMMs for protein sequence analysis. http://hmmer.janelia.org, 2004.
[5]
Horn, D., Houston, M., and Hanrahan, P. ClawHMMER: A streaming HMMer-search implementation. In Proc. of ACM/IEEE Supercomputing Conf. (2005).
[6]
Krogh, A., et al. Hidden Markov models in computational biology: Applications to protein modeling. Journal of Molecular Biology 235 (1994), 1501--1531.
[7]
Lindahl, E. Altivec HMMer, version 2.3.2. http://powerdev.osuosl.org/project/hmmerAltivecGen2mod/.
[8]
Maddimsetty, R., Buhler, J., Chamberlain, R., Franklin, M., and Harris, B. Accelerator design for protein sequence HMM search. In Proc. 20th ACM International Conference on Supercomputing (2006).
[9]
Oliver, T., Yeow, L. Y., and Schmidt, B. Integrating FPGA acceleration into HMMer. Parallel Computing 34, 11 (2008), 681--691.
[10]
Takagi, T., and Maruyama, T. Accelerating HMMER search using FPGA. In IEICE Tech. Rep., RECONF2009-6 (2009), vol. 109, pp. 31--36.
[11]
Walters, J. P., Balu, V., Kompalli, S., and Chaudhary, V. Evaluating the use of gpus in liver image segmentation and hmmer database searches. In Proc. of IPDPS (2009).

Cited By

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  • (2020)Accelerating Forward Algorithm for Stochastic Automata on Graphics Processing UnitsIEEE Access10.1109/ACCESS.2020.29737418(32270-32279)Online publication date: 2020
  • (2018)CUDAMPF++: A Proactive Resource Exhaustion Scheme for Accelerating Homologous Sequence Search on CUDA-Enabled GPUIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2018.283039329:10(2206-2222)Online publication date: 1-Oct-2018
  • (2018)Combining execution pipelines to improve parallel implementation of HMMER on FPGAMicroprocessors & Microsystems10.1016/j.micpro.2015.06.00639:7(457-470)Online publication date: 28-Dec-2018
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cover image ACM Conferences
BCB '10: Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
August 2010
705 pages
ISBN:9781450304382
DOI:10.1145/1854776
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 August 2010

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Cited By

View all
  • (2020)Accelerating Forward Algorithm for Stochastic Automata on Graphics Processing UnitsIEEE Access10.1109/ACCESS.2020.29737418(32270-32279)Online publication date: 2020
  • (2018)CUDAMPF++: A Proactive Resource Exhaustion Scheme for Accelerating Homologous Sequence Search on CUDA-Enabled GPUIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2018.283039329:10(2206-2222)Online publication date: 1-Oct-2018
  • (2018)Combining execution pipelines to improve parallel implementation of HMMER on FPGAMicroprocessors & Microsystems10.1016/j.micpro.2015.06.00639:7(457-470)Online publication date: 28-Dec-2018
  • (2017)Accelerating Wright–Fisher Forward Simulations on the Graphics Processing UnitG3: Genes|Genomes|Genetics10.1534/g3.117.3001037:9(3229-3236)Online publication date: 2-Aug-2017
  • (2017)Accelerating Viterbi algorithm on graphics processing unitsComputing10.1007/s00607-017-0557-699:11(1105-1123)Online publication date: 1-Nov-2017
  • (2016)Graphics processing units in bioinformatics, computational biology and systems biologyBriefings in Bioinformatics10.1093/bib/bbw058(bbw058)Online publication date: 8-Jul-2016
  • (2015)Fine-Grained Acceleration of HMMER 3.0 via Architecture-Aware Optimization on Massively Parallel ProcessorsProceedings of the 2015 IEEE International Parallel and Distributed Processing Symposium Workshop10.1109/IPDPSW.2015.107(375-383)Online publication date: 25-May-2015
  • (2015)Accelerating Search of Protein Sequence Databases using CUDA-Enabled GPUDatabase Systems for Advanced Applications10.1007/978-3-319-18120-2_17(279-298)Online publication date: 9-Apr-2015
  • (2014)Cache-Oblivious parallel SIMD Viterbi decoding for sequence search in HMMERBMC Bioinformatics10.1186/1471-2105-15-16515:1Online publication date: 30-May-2014
  • (2013)State-of-the-Art GPGPU Applications in BioinformaticsInternational Journal of Systems Biology and Biomedical Technologies10.4018/ijsbbt.20131001032:4(24-48)Online publication date: Oct-2013
  • Show More Cited By

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