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In everyday conversation, accelerating means speeding up. Acceleration is a vector in the same direction as the change in velocity, Δv, therefore the greater the acceleration, the greater the change in velocity over a given time. Since velocity is a vector, it can change in magnitude, direction, or both. Thus acceleration is a change in speed or direction, or both. For example, if a runner traveling at 10 km/h due east slows to a stop, reverses direction, and continues their run at 10 km/h due...
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CUDA-BLASTP: accelerating BLASTP on CUDA-enabled graphics hardware.

Weiguo Liu1, Bertil Schmidt, Wolfgang Müller-Wittig

  • 1Fraunhofer IDM@NTU, Nanyang Technological University, NS-05-01, Singapore 639798. liuweiguo@ntu.edu.sg

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|February 23, 2011
PubMed
Summary
This summary is machine-generated.

Accelerating protein sequence database scans using Graphics Processing Units (GPUs) with Compute Unified Device Architecture (CUDA) significantly reduces computational time. This GPU-accelerated BLASTP method offers substantial speedups for bioinformatics tasks.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • High-Performance Computing

Background:

  • Protein sequence database scanning is crucial in computational biology.
  • Traditional BLASTP on sequential architectures is time-consuming due to large databases.
  • There is a growing need for faster sequence analysis methods.

Purpose of the Study:

  • To demonstrate the use of Graphics Processing Units (GPUs) with Compute Unified Device Architecture (CUDA) for accelerating the BLASTP algorithm.
  • To improve the efficiency of scanning large protein sequence databases.

Main Methods:

  • Implementation of a compressed deterministic finite state automaton for hit detection.
  • Utilizing a hybrid parallelization scheme to leverage GPU capabilities.
  • Porting the BLASTP algorithm to run on NVIDIA GPUs using CUDA.

Main Results:

  • Achieved speedups of up to 10.0x compared to sequential NCBI BLASTP 2.2.22.
  • Demonstrated the effectiveness of GPUs as an efficient computational platform for BLASTP.
  • Successful acceleration of protein sequence database scanning.

Conclusions:

  • GPUs powered by CUDA offer a viable and efficient solution for accelerating BLASTP.
  • The developed CUDA-BLASTP implementation significantly reduces runtimes for large-scale sequence analysis.
  • This approach addresses the demand for faster computational biology tools.