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High performance pattern matching on heterogeneous platform.

Shima Soroushnia1, Masoud Daneshtalab1, Juha Plosila1

  • 1Department of Information Technology, University of Turku, Finland.

Journal of Integrative Bioinformatics
|October 24, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a faster pattern matching algorithm for biological sequences using a heterogeneous CPU/GPU architecture. The new implementation of the Parallel Failureless Aho-Corasick (PFAC) algorithm achieves a 15x speedup for DNA and protein sequence analysis.

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Area of Science:

  • Bioinformatics
  • Computational Molecular Biology
  • Pattern Recognition

Background:

  • Pattern discovery is crucial in bioinformatics for analyzing large biological sequence databases.
  • High-performance algorithms are essential due to the exponential growth of DNA and protein sequence data.
  • Heterogeneous computing architectures offer potential for increased performance and energy efficiency.

Purpose of the Study:

  • To present an efficient implementation of the Aho-Corasick (AC) and Parallel Failureless Aho-Corasick (PFAC) algorithms on a heterogeneous CPU/GPU architecture.
  • To optimize algorithms and data structures for GPU acceleration in sequence pattern matching.

Main Methods:

  • Implementation of the exact pattern matching algorithm Aho-Corasick (AC) and its parallel version PFAC.
  • Adaptation of algorithms and data structures for efficient execution on a GPU.
  • Performance evaluation using diverse protein sequence datasets.

Main Results:

  • The redesigned PFAC algorithm on a heterogeneous CPU/GPU architecture shows significant performance improvements.
  • The new implementation achieves a speedup of 15 times compared to the original PFAC algorithm.
  • Demonstrated efficiency in handling large-scale biological sequence pattern matching tasks.

Conclusions:

  • The GPU-accelerated PFAC algorithm provides a substantial performance boost for bioinformatics sequence analysis.
  • Heterogeneous architectures are effective for accelerating computationally intensive pattern matching tasks.
  • This optimized approach addresses the growing need for faster analysis of biological sequence data.