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CUDASW++4.0: ultra-fast GPU-based Smith-Waterman protein sequence database search.

Bertil Schmidt1, Felix Kallenborn2, Alejandro Chacon3

  • 1Department of Computer Science, Johannes Gutenberg University Mainz, Mainz, Germany. bertil.schmidt@uni-mainz.de.

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Summary

CUDASW++4.0 accelerates protein sequence database searches using the Smith-Waterman algorithm on GPUs. This new tool offers significant speedups and energy efficiency, outperforming previous GPU and CPU-based methods.

Keywords:
CUDADPXDynamic programmingGPUProtein sequence database searchSmith–Waterman

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • High-Performance Computing

Background:

  • The Smith-Waterman algorithm is crucial for protein sequence database searches due to its sensitivity.
  • Its quadratic time complexity presents computational challenges, limiting performance.
  • Existing GPU tools do not fully utilize modern hardware capabilities.

Purpose of the Study:

  • To develop a highly efficient GPU implementation of the Smith-Waterman algorithm for protein sequence database searching.
  • To achieve close-to-peak performance on modern GPU architectures.

Main Methods:

  • Developed CUDASW++4.0, a software tool leveraging CUDA-enabled GPUs.
  • Optimized dynamic programming computation by minimizing memory accesses and instructions.
  • Implemented efficient matrix tiling and sequence database partitioning.
  • Utilized next-generation floating-point arithmetic and novel DPX instructions.

Main Results:

  • CUDASW++4.0 achieves high throughput rates on modern GPUs (e.g., up to 5.71 TCUPS on H100).
  • Demonstrates over an order-of-magnitude performance improvement compared to prior GPU approaches.
  • Shows significant speedups against leading CPU-based tools like BLASTP.
  • Exhibits linear scaling on multi-GPU nodes and high energy efficiency (up to 15.7 GCUPS/Watt).

Conclusions:

  • CUDASW++4.0 establishes a new performance benchmark for GPU-accelerated Smith-Waterman alignment in protein sequence database searches.
  • The software is freely available, promoting wider adoption and research.
  • This advancement significantly enhances the utility of GPUs for large-scale biological sequence analysis.