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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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A parallel approximate string matching under Levenshtein distance on graphics processing units using warp-shuffle

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  • 1School of Electronics and Electrical Engineering, Dankook University, Yongin-si, Republic of Korea.

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This study introduces an efficient parallel algorithm for approximate string matching using Graphics Processing Units (GPUs), significantly speeding up DNA sequence analysis.

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Area of Science:

  • Computer Science
  • Bioinformatics
  • Computational Biology

Background:

  • Approximate string matching with k-differences is crucial for pattern recognition and computational biology.
  • Existing methods face challenges in terms of efficiency for large datasets.

Purpose of the Study:

  • To propose an efficient memory-access algorithm for parallel approximate string matching with k-differences on Graphics Processing Units (GPUs).
  • To optimize the algorithm's performance by exploiting GPU memory structures.

Main Methods:

  • Developed a parallel algorithm utilizing warp-shuffle operations for data sharing among GPU threads, avoiding shared memory access.
  • Implemented the algorithm with specific optimizations tailored to GPU memory architecture.

Main Results:

  • Achieved significant performance improvements compared to sequential CPU algorithms.
  • Demonstrated up to 122.64 times speedup over sequential algorithms and 1.53 times over previous GPU algorithms on real DNA datasets.

Conclusions:

  • The proposed GPU-based algorithm offers a substantial performance enhancement for approximate string matching.
  • This advancement has significant implications for accelerating analyses in computational biology and pattern recognition.