Hierarchical Parallelism of Bit-Parallel Algorithm for Approximate String Matching on GPUs

Approximate string matching has been widely used in many areas, such as web searching, and deoxyribonucleic acid sequence matching, etc. Approximate string matching allows difference between a string and a pattern caused by insertion, deletion and substitution. Because approximate string matching is...

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Vydané v:2014 IEEE Symposium on Computer Applications and Communications s. 76 - 81
Hlavní autori: Cheng Hung Lin, Guan Hong Wang, Chun Cheng Huang
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.07.2014
Predmet:
Acceleration
approximate string matching
Approximation algorithms
bit-parallel algorithm
Data transfer
graphic processing units
Graphics processing units
Kernel
Parallel processing
Pattern matching
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Abstract Approximate string matching has been widely used in many areas, such as web searching, and deoxyribonucleic acid sequence matching, etc. Approximate string matching allows difference between a string and a pattern caused by insertion, deletion and substitution. Because approximate string matching is a data-intensive task, accelerating approximate string matching has become crucial for processing big data. In this paper, we propose a hierarchical parallelism approach to accelerate the bit-parallel algorithm on NVIDIA GPUs. A data parallelism approach is used to accelerate the kernel of the bit-parallel algorithm while a task parallelism approach is used to overlap data transfer with kernel computation. In addition, we propose to use hashing to reduce the memory usage and achieve 98.4% of memory reduction. The experimental results show that the bit-parallel algorithm performed on GPUs achieves 7 to 11 times faster than the multithreaded CPU implementation. Compared to the state-of-the-art approaches, the proposed approach achieves 2.8 to 104.8 times improvement.
AbstractList Approximate string matching has been widely used in many areas, such as web searching, and deoxyribonucleic acid sequence matching, etc. Approximate string matching allows difference between a string and a pattern caused by insertion, deletion and substitution. Because approximate string matching is a data-intensive task, accelerating approximate string matching has become crucial for processing big data. In this paper, we propose a hierarchical parallelism approach to accelerate the bit-parallel algorithm on NVIDIA GPUs. A data parallelism approach is used to accelerate the kernel of the bit-parallel algorithm while a task parallelism approach is used to overlap data transfer with kernel computation. In addition, we propose to use hashing to reduce the memory usage and achieve 98.4% of memory reduction. The experimental results show that the bit-parallel algorithm performed on GPUs achieves 7 to 11 times faster than the multithreaded CPU implementation. Compared to the state-of-the-art approaches, the proposed approach achieves 2.8 to 104.8 times improvement.
Author Chun Cheng Huang
Cheng Hung Lin
Guan Hong Wang
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  surname: Guan Hong Wang
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  organization: Dept. of Electr. Eng., Nat. Taiwan Normal Univ., Taipei, Taiwan
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  surname: Chun Cheng Huang
  fullname: Chun Cheng Huang
  organization: Dept. of Electr. Eng., Nat. Taiwan Normal Univ., Taipei, Taiwan
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Snippet Approximate string matching has been widely used in many areas, such as web searching, and deoxyribonucleic acid sequence matching, etc. Approximate string...
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SubjectTerms Acceleration
approximate string matching
Approximation algorithms
bit-parallel algorithm
Data transfer
graphic processing units
Graphics processing units
Kernel
Parallel processing
Pattern matching
Title Hierarchical Parallelism of Bit-Parallel Algorithm for Approximate String Matching on GPUs
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