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Accelerating pairwise statistical significance estimation for local alignment by harvesting GPU's power.

Yuhong Zhang1, Sanchit Misra, Ankit Agrawal

  • 1School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China. yuhongzhang@uestc.edu.cn

BMC Bioinformatics
|April 28, 2012
PubMed
Summary

We developed a GPU implementation to accelerate pairwise statistical significance estimation for local sequence alignment. This approach achieves significant speedups, offering a scalable solution for bioinformatics applications.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • High-Performance Computing

Background:

  • Pairwise statistical significance is crucial for identifying related sequences in bioinformatics.
  • Current methods are computationally intensive, limiting performance and scalability.

Purpose of the Study:

  • To accelerate pairwise statistical significance estimation for local sequence alignment using Graphics Processing Units (GPUs).
  • To improve the performance and scalability of sequence comparison in bioinformatics.

Main Methods:

  • Developed a GPU implementation for pairwise statistical significance estimation.
  • Utilized a tile-based scheme for contiguous data access in GPU global memory.
  • Extended parallelization for position-specific substitution matrices and dual-GPU utilization.

Main Results:

  • Achieved end-to-end speedups of nearly 250x (single-GPU) and 370x (dual-GPU) compared to CPU implementations.
  • Demonstrated high GPU occupancy through optimized data access and thread management.
  • Successfully applied the method to position-specific substitution matrices for improved accuracy.

Conclusions:

  • Leveraging modern GPU performance is an effective strategy for accelerating pairwise statistical significance estimation.
  • The developed GPU implementation offers a significant performance improvement for local sequence alignment tasks.