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GPU-DAEMON: GPU algorithm design, data management & optimization template for array based big omics data.

Muaaz Gul Awan1, Taban Eslami1, Fahad Saeed2

  • 1Department of Computer Science, Western Michigan University, Kalamazoo, MI, USA.

Computers in Biology and Medicine
|August 27, 2018
PubMed
Summary
This summary is machine-generated.

GPU-DAEMON accelerates big omics data processing using Graphics Processing Units (GPUs). This technique offers significant speedups, making complex data analysis more efficient and accessible.

Keywords:
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Area of Science:

  • Computational Biology
  • Bioinformatics
  • High-Performance Computing

Background:

  • The increasing volume of big data necessitates faster processing algorithms.
  • Graphics Processing Units (GPUs) offer a cost-effective high-performance computing solution.
  • Designing efficient GPU algorithms requires specialized expertise and understanding of hardware architecture.

Purpose of the Study:

  • To introduce GPU-DAEMON, a novel technique for managing data, designing algorithms, and optimizing performance on GPUs.
  • To provide a generic template for developing scalable GPU algorithms for array-based big omics data.
  • To demonstrate the effectiveness of GPU-DAEMON through practical implementations.

Main Methods:

  • Developed GPU-DAEMON, a GPU algorithm design and optimization template.
  • Applied the GPU-DAEMON template to three distinct big data problems involving omics data.
  • Implemented and evaluated GPU-DAEMON based algorithms, including GPU-ArraySort, G-MSR, and GPU-PCC.

Main Results:

  • Achieved speedups of up to 386x compared to sequential algorithms.
  • Demonstrated speedups of up to 50x compared to naive GPU design methods.
  • Validated the scalability and efficiency of GPU-DAEMON for big omics data processing.

Conclusions:

  • GPU-DAEMON effectively addresses critical bottlenecks in GPU-based big data processing.
  • The proposed template facilitates the development of high-performance, scalable GPU algorithms.
  • GPU-DAEMON significantly enhances the efficiency of omics data analysis on GPUs.