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Speedup bioinformatics applications on multicore-based processor using vectorizing and multithreading strategies.

Kridsadakorn Chaichoompu1, Surin Kittitornkun, Sissades Tongsima

  • 1Genome Institute, National Center for Genetic Engineering and Biotechnology, 113 Thailand Science Park, Paholyothin Road, Klong 1, Klong Luang, Pathumtani 12120, Thailand.

Bioinformation
|February 29, 2008
PubMed
Summary
This summary is machine-generated.

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This study introduces a compiling strategy to optimize C/C++ bioinformatics software for multicore processors. The approach enhances performance by enabling multithreading and vectorization, improving computational efficiency.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Computer Science

Background:

  • Many computationally intensive bioinformatics applications written in C/C++ are not optimized for multicore processors.
  • Modern multicore processors utilize the Single Instruction Multiple Data-stream (SIMD) paradigm, but compilers often fail to generate SIMD code automatically.
  • Existing compilers like Microsoft Visual C/C++ 6.0 and gcc do not fully leverage multicore advancements.

Purpose of the Study:

  • To present a generic compiling strategy for improving the performance of C/C++ bioinformatics applications on multicore architectures.
  • To enable bioinformatics software to harness the full potential of multicore processors through specific compiler techniques.

Main Methods:

  • The proposed framework involves a two-step strategy: multithreading and vectorizing.
Keywords:
multicore processoroptimizationspeedupvectorization

Related Experiment Videos

  • This generic strategy assists compilers in generating optimized code for bioinformatics applications.
  • The focus is on leveraging multicore architecture advancements for computational speedup.

    • Main Results:

    • The implemented strategies lead to significant speedup in bioinformatics applications.
    • The optimization successfully utilizes multicore architecture technology.
    • The approach offers advantages over cluster parallelization due to lower network latency and higher bandwidth.

    Conclusions:

    • The proposed compiling strategy effectively enhances the performance of C/C++ bioinformatics software on multicore processors.
    • This method is a viable alternative to cluster parallelization for intensive computational tasks.
    • Further adoption of this strategy can accelerate bioinformatics research and discovery.