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High-performance recursive dynamic programming for bioinformatics using MM-like flexible kernels

Published: 20 September 2014 Publication History

Abstract

Dynamic Programming (DP) provides optimal solutions to a problem by combining optimal solutions to many overlapping subproblems. DP algorithms exploit this overlapping property to explore otherwise exponential-sized problem spaces in polynomial time, making them central to many important applications spanning from logistics to computational biology. In this paper, we present a general strategy of obtaining highly efficient parallel DP implementations using recursive cache-oblivious divide and conquer technique which turns inflexible kernels into flexible ones (kernels that read from and write to disjoint sub-matrices). We solve four non-trivial DP problems widely used in Bioinformatics, namely the parenthesis problem, Floyd-Warshall's all-pairs shortest paths, gap problem and protein accordion folding using recursive cache-oblivious technique that decompose the original inflexible looping kernel to highly optimizable flexible kernels. To the best of our knowledge no such recursive parallel DP algorithms were known for the protein folding and gap problems. The algorithms are hybrid in the same way as most high-performance matrix multiplication algorithms are recursive with iterative base cases. We show that the base cases of these recursive divide-and-conquer algorithms are predominantly matrix-multiplication-like (MM-like) flexible that expose many optimization opportunities not offered by the traditional looping DP codes. Moreover, the most costly/dominating kernel for these problems are often flexible. As a result, one can obtain highly efficient DP implementations by simply optimizing these kernels. We present a few generic optimization steps that suffices to optimize these DP implementations. Our implementations achieve 5--100x speedup over their standard loop based DP counterparts on modern multicore machines. We also present results on manycores (Xeon Phi) and clusters of multicores obtained by simple extensions for SIMD and shared-distributed-shared-memory architectures, respectively.

References

[1]
R. B. Lyngs, M. Zuker, and C. Pedersen. Fast evaluation of internal loops in rna secondary structure prediction. Bioinformatics, 15(6):440--445, 1999.
[2]
I. Mandoiu, R. Sunderraman, and A. Zelikovsky. Bioinformatics Research and Applications: Fourth International Symposium, ISBRA 2008, Atlanta, GA, USA, May 6-9, 2008, Proceedings, volume 4983. Springer, 2008.
[3]
G. Tan, S. Feng, and N. Sun. Locality and parallelism optimization for dynamic programming algorithm in bioinformatics. In Proceedings of the 2006 ACM/IEEE conference on Supercomputing, page 78. ACM, 2006.

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cover image ACM Conferences
BCB '14: Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
September 2014
851 pages
ISBN:9781450328944
DOI:10.1145/2649387
  • General Chairs:
  • Pierre Baldi,
  • Wei Wang
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 September 2014

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BCB '14
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BCB '14: ACM-BCB '14
September 20 - 23, 2014
California, Newport Beach

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Overall Acceptance Rate 254 of 885 submissions, 29%

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