On the parallelization of Hirschberg's algorithm for multi‐core and many‐core systems

Summary Finding the longest common subsequence between two strings in acceptable time frames is crucial to solving various problems in different fields of study. To ensure the optimal solution is found, algorithms based on dynamic programming are employed almost exclusively. While the most commonly...

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Published in:	Concurrency and computation Vol. 31; no. 18
Main Authors:	João, Mario, Sena, Alexandre C., Rebello, Vinod E. F.
Format:	Journal Article
Language:	English
Published:	Hoboken Wiley Subscription Services, Inc 25.09.2019
Subjects:	Algorithms Complexity Dynamic programming Hirschberg's algorithm longest common subsequence (LCS) problem many‐core processors multi‐core systems Parallel processing Performance enhancement SIMD instructions
ISSN:	1532-0626, 1532-0634
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Abstract	Summary Finding the longest common subsequence between two strings in acceptable time frames is crucial to solving various problems in different fields of study. To ensure the optimal solution is found, algorithms based on dynamic programming are employed almost exclusively. While the most commonly adopted algorithm, proposed by Needleman and Wunsch, has quadratic time and space complexity, the linear space complexity of Hirschberg's algorithm favors the comparisons of longer sequences. However, it too has a quadratic time complexity and therefore the effective exploitation of parallelism has become essential. This paper focuses on improving the execution efficiency of Hirschberg's algorithm on multi‐core and many‐core systems. To achieve this goal, first, enhancements to the sequential version are proposed to take advantage of SIMD instructions available on modern processors. Second, the impact on the performance of different parallelization strategies is investigated and evaluated. Results show that combining these two aspects can greatly improve the performance of Hirschberg's algorithm on these architectures. In relation to the original version, speedups of over 46 were achieved on a dual 18‐core server for sequences of 1.6 million characters. Furthermore, experiments with a 68‐core Intel Xeon Phi (many‐core) system obtained speedups of up to 105 for the same sequence size.
AbstractList	Finding the longest common subsequence between two strings in acceptable time frames is crucial to solving various problems in different fields of study. To ensure the optimal solution is found, algorithms based on dynamic programming are employed almost exclusively. While the most commonly adopted algorithm, proposed by Needleman and Wunsch, has quadratic time and space complexity, the linear space complexity of Hirschberg's algorithm favors the comparisons of longer sequences. However, it too has a quadratic time complexity and therefore the effective exploitation of parallelism has become essential. This paper focuses on improving the execution efficiency of Hirschberg's algorithm on multi‐core and many‐core systems. To achieve this goal, first, enhancements to the sequential version are proposed to take advantage of SIMD instructions available on modern processors. Second, the impact on the performance of different parallelization strategies is investigated and evaluated. Results show that combining these two aspects can greatly improve the performance of Hirschberg's algorithm on these architectures. In relation to the original version, speedups of over 46 were achieved on a dual 18‐core server for sequences of 1.6 million characters. Furthermore, experiments with a 68‐core Intel Xeon Phi (many‐core) system obtained speedups of up to 105 for the same sequence size. Summary Finding the longest common subsequence between two strings in acceptable time frames is crucial to solving various problems in different fields of study. To ensure the optimal solution is found, algorithms based on dynamic programming are employed almost exclusively. While the most commonly adopted algorithm, proposed by Needleman and Wunsch, has quadratic time and space complexity, the linear space complexity of Hirschberg's algorithm favors the comparisons of longer sequences. However, it too has a quadratic time complexity and therefore the effective exploitation of parallelism has become essential. This paper focuses on improving the execution efficiency of Hirschberg's algorithm on multi‐core and many‐core systems. To achieve this goal, first, enhancements to the sequential version are proposed to take advantage of SIMD instructions available on modern processors. Second, the impact on the performance of different parallelization strategies is investigated and evaluated. Results show that combining these two aspects can greatly improve the performance of Hirschberg's algorithm on these architectures. In relation to the original version, speedups of over 46 were achieved on a dual 18‐core server for sequences of 1.6 million characters. Furthermore, experiments with a 68‐core Intel Xeon Phi (many‐core) system obtained speedups of up to 105 for the same sequence size.
Author	Sena, Alexandre C. Rebello, Vinod E. F. João, Mario
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Snippet	Summary Finding the longest common subsequence between two strings in acceptable time frames is crucial to solving various problems in different fields of... Finding the longest common subsequence between two strings in acceptable time frames is crucial to solving various problems in different fields of study. To...
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SubjectTerms	Algorithms Complexity Dynamic programming Hirschberg's algorithm longest common subsequence (LCS) problem many‐core processors multi‐core systems Parallel processing Performance enhancement SIMD instructions
Title	On the parallelization of Hirschberg's algorithm for multi‐core and many‐core systems
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