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Design and Optimization Strategies of a High-Performance Vented Box
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CUDASW++: optimizing Smith-Waterman sequence database searches for CUDA-enabled graphics processing units.

Yongchao Liu1, Douglas L Maskell, Bertil Schmidt

  • 1School of Computer Engineering, Nanyang Technological University, Singapore. liuy0039@ntu.edu.sg

BMC Research Notes
|May 7, 2009
PubMed
Summary
This summary is machine-generated.

CUDASW++ accelerates Smith-Waterman searches on GPUs, offering significant speedups for biological sequence databases. This open-source software leverages CUDA-enabled graphics processing units for enhanced performance.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • High-Performance Computing

Background:

  • The Smith-Waterman algorithm is crucial for sensitive biological sequence database searches.
  • Its computational demands increase with growing sequence databases.
  • Many-core architectures offer potential for hardware acceleration.

Purpose of the Study:

  • To accelerate Smith-Waterman algorithm searches using commodity hardware.
  • To evaluate the performance of a GPU-accelerated implementation.

Main Methods:

  • Development of CUDASW++, a GPU-accelerated implementation of the Smith-Waterman algorithm.
  • Benchmarking on NVIDIA GeForce GTX 280 and GTX 295 graphics cards.
  • Comparison with existing implementations like SWPS3, CBESW, SW-CUDA, and NCBI-BLAST.

Main Results:

  • CUDASW++ demonstrates significant performance improvements over other implementations.
  • Single-GPU achieved 9.509 GCUPS on average for Swiss-Prot 56.6.
  • Dual-GPU achieved 14.484 GCUPS on average, showcasing scalability.

Conclusions:

  • CUDASW++ is open-source and publicly available.
  • It significantly enhances Smith-Waterman protein sequence database search performance.
  • Exploits CUDA-enabled GPUs for efficient, low-cost acceleration.