chapter

GPU accelerated computation of the longest common subsequence

Authors:

Katsuya Kawanami,

Noriyuki FujimotoAuthors Info & Claims

Facing the Multicore-Challenge II: aspects of new paradigms and technologies in parallel computing

January 2012

Pages 84 - 95

Published: 01 January 2012 Publication History

Publisher Site

Abstract

The longest common subsequence (LCS for short) for given two strings has various applications, e.g. comparison of DNAs. In this paper, we propose a GPU algorithm to accelerate Hirschberg's CPU LCS algorithm improved using Crochemore et al's bit-parallel CPU algorithm. Crochemore's algorithm includes bitwise logical operators which can be computed in embarrassingly parallel. However, it also includes an operator with less parallelism, i.e. an arithmetic sum. In this paper, we focus on how to implement these operators efficiently in parallel. Our experiments with 2.93GHz Intel Core i3 530 CPU, GeForce 8800 GTX, GTX 285, and GTX 480 GPUs show that the proposed algorithm runs maximum 12.77 times faster than the bit-parallel CPU algorithm and maximum 76.5 times faster than Hirschberg's LCS CPU algorithm. Furthermore, the proposed algorithm runs 10.9 to 18.1 times faster than Kloetzli's existing GPU algorithm.

References

[1]

Crochemore, M., Iliopoulos, C. S., Pinzon, Y. J., Reid, J. F.: A Fast and Practical Bit-Vector Algorithm for the Longest Common Subsequence Problem. Information Processing Letters 80(6), 279-285 (2001)

Digital Library

Google Scholar

[2]

Garland, M., Kirk, D. B.: Understanding Throughput-Oriented Architectures. Communications of the ACM 53(11), 58-66 (2010)

Digital Library

Google Scholar

[3]

Gusfield, D.: Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology. Cambridge University Press (1997)

Digital Library

Google Scholar

[4]

Hirschberg, D. S.: A Linear Space Algorithm for Computing Maximal Common Subsequences. Communications of the ACM 18(6), 341-343 (1975)

Digital Library

Google Scholar

[5]

Kirk, D. B., Hwu, W. W.: Programming Massively Parallel Processors: A Hands-on Approach. Morgan Kaufmann (2010)

Digital Library

Google Scholar

[6]

Kloetzli, J., Strege, B., Decker, J., Olano, M.: Parallel Longest Common Subsequence Using Graphics Hardware. In: Proc. of the 8th Eurographics Symposium on Parallel Graphics and Visualization, EGPGV (2008)

Digital Library

Google Scholar

[7]

Lindholm, E., Nickolls, J., Oberman, S., Montrym, J.: NVIDIA Tesla: A Unified Graphics and Computing Architecture. IEEE Micro 28(2), 39-55 (2008)

Digital Library

Google Scholar

[8]

Sanders, J., Kandrot, E.: CUDA by Example: An Introduction to General-Purpose GPU Programming. Addison-Wesley Professional (2010)

Digital Library

Google Scholar

[9]

Sklansky, J.: Conditional-Sum Addition Logic. IRE Trans. on Electronic Computers EC-9, 226-231 (1960)

Google Scholar

[10]

Vai, M.: VLSI Design. CRC Press (2000)

Google Scholar

Cited By

View all

Rekabdar BNicolescu MNicolescu MLouis S(2017)Using patterns of firing neurons in spiking neural networks for learning and early recognition of spatio-temporal patternsNeural Computing and Applications10.1007/s00521-016-2283-y28:5(881-897)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1007/s00521-016-2283-y
Chacón AMarco-Sola SEspinosa ARibeca PMoure JBode AGerndt MStenström PRauchwerger LMiller BSchulz M(2014)Thread-cooperative, bit-parallel computation of levenshtein distance on GPUProceedings of the 28th ACM international conference on Supercomputing10.1145/2597652.2597677(103-112)Online publication date: 10-Jun-2014
https://dl.acm.org/doi/10.1145/2597652.2597677
Ozsoy AChauhan ASwany MBalaji PGuo MHuang Z(2014)Fast Longest Common Subsequence with General Integer Scoring Support on GPUsProceedings of Programming Models and Applications on Multicores and Manycores10.1145/2578948.2560690(92-102)Online publication date: 7-Feb-2014
https://dl.acm.org/doi/10.1145/2578948.2560690
Show More Cited By

Recommendations

Fast Longest Common Subsequence with General Integer Scoring Support on GPUs
PMAM'14: Proceedings of Programming Models and Applications on Multicores and Manycores

Graphic Processing Units (GPUs) have been gaining popularity among high-performance users. Certain classes of algorithms benefit greatly from the massive parallelism of GPUs. One such class of algorithms is longest common subsequence (LCS). Combined ...
GPU-accelerated DNA Distance Matrix Computation
CHINAGRID '11: Proceedings of the 2011 Sixth Annual ChinaGrid Conference

Distance matrix calculation used in phylogeny analysis is computational intensive. The growing sequences data sets necessitate fast computation method. This paper accelerate Felsenstein's DNADIST program by using OpenCL to exploit the great computation ...
Optimizing linpack benchmark on GPU-accelerated petascale supercomputer
Special issue on Community Analysis and Information Recommendation

In this paper we present the programming of the Linpack benchmark on TianHe-1 system, the first petascale supercomputer system of China, and the largest GPU-accelerated heterogeneous system ever attempted before. A hybrid programming model consisting of ...

Comments

Information & Contributors

Information

Published In

Facing the Multicore-Challenge II: aspects of new paradigms and technologies in parallel computing

January 2012

171 pages

ISBN:9783642303968

Editors:
Rainer Keller
High Performance Computing Center Stuttgart (HLRS), Universität Stuttgart, Nobelstraße 19, Stuttgart, Germany
,
David Kramer
Institute of Computer Science and Engineering, Karlsruhe Institute of Technology (KIT), Haid-und-Neu-Straße 7, Karlsruhe, Germany
,
Jan-Philipp Weiss
Institute for Applied and Numerical Mathematics, SRG New Frontiers in High Performance Computing and Karlsruhe Institute of Technology (KIT), 4, Fritz-Erler-Straße 23, Karlsruhe, Germany

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 January 2012

Qualifiers

Chapter

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 10 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Rekabdar BNicolescu MNicolescu MLouis S(2017)Using patterns of firing neurons in spiking neural networks for learning and early recognition of spatio-temporal patternsNeural Computing and Applications10.1007/s00521-016-2283-y28:5(881-897)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1007/s00521-016-2283-y
Chacón AMarco-Sola SEspinosa ARibeca PMoure JBode AGerndt MStenström PRauchwerger LMiller BSchulz M(2014)Thread-cooperative, bit-parallel computation of levenshtein distance on GPUProceedings of the 28th ACM international conference on Supercomputing10.1145/2597652.2597677(103-112)Online publication date: 10-Jun-2014
https://dl.acm.org/doi/10.1145/2597652.2597677
Ozsoy AChauhan ASwany MBalaji PGuo MHuang Z(2014)Fast Longest Common Subsequence with General Integer Scoring Support on GPUsProceedings of Programming Models and Applications on Multicores and Manycores10.1145/2578948.2560690(92-102)Online publication date: 7-Feb-2014
https://dl.acm.org/doi/10.1145/2578948.2560690
Ozsoy AChauhan ASwany MBalaji PGuo MHuang Z(2014)Fast Longest Common Subsequence with General Integer Scoring Support on GPUsProceedings of Programming Models and Applications on Multicores and Manycores10.1145/2560683.2560690(92-102)Online publication date: 7-Feb-2014
https://dl.acm.org/doi/10.1145/2560683.2560690
Ozsoy AKimpe DChen YRoth PYu W(2014)CULZSS-bitProceedings of the 2014 International Workshop on Data Intensive Scalable Computing Systems10.1109/DISCS.2014.9(57-64)Online publication date: 16-Nov-2014
https://dl.acm.org/doi/10.1109/DISCS.2014.9

Abstract

References

Cited By

Recommendations

Fast Longest Common Subsequence with General Integer Scoring Support on GPUs

GPU-accelerated DNA Distance Matrix Computation

Optimizing linpack benchmark on GPU-accelerated petascale supercomputer

Comments

Information

Published In

Publisher

Publication History

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations