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Virtual Grid Engine: a simulated grid engine environment for large-scale supercomputers.

Satoshi Ito1, Masaaki Yadome2, Tatsuo Nishiki3

  • 1The Institute of Medical Science, The University of Tokyo, Shirokanedai 4-6-1, Minato-ku, Tokyo, 108-8639, Japan. sito@hgc.jp.

BMC Bioinformatics
|December 3, 2019
PubMed
Summary
This summary is machine-generated.

Virtual Grid Engine (VGE) middleware enables bioinformatics software to run on supercomputers by enabling asynchronous processing. This overcomes limitations, allowing large-scale analyses efficiently.

Keywords:
Grid engineHigh performance computingMPIPythonTOP500

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Area of Science:

  • Computational Biology
  • High-Performance Computing

Background:

  • Supercomputers are crucial for scientific advancements but underutilized in bioinformatics.
  • Existing supercomputers lack asynchronous parallel processing support (e.g., Grid Engine).
  • This limits the application of massively parallel supercomputers in bioinformatics research.

Purpose of the Study:

  • To develop middleware enabling bioinformatics software to leverage supercomputers.
  • To facilitate the use of massively parallel supercomputers in the bioinformatics field.
  • To enable software pipelines to automatically perform tasks as Message Passing Interface (MPI) programs on supercomputers.

Main Methods:

  • Developed Virtual Grid Engine (VGE) middleware.
  • Designed VGE to meet requirements: non-privileged server, multiple job handling, dependency control, and usability.
  • Tested VGE's job assignment overhead and scalability on the K computer.

Main Results:

  • VGE demonstrated a low job assignment overhead of 246 microseconds.
  • The software efficiently managed thousands of jobs on the K computer.
  • A practical bioinformatics test (FASTQ data split and BWA alignment) was completed in three hours using 25,055 nodes (2,000,440 cores).

Conclusions:

  • VGE successfully meets key software requirements for supercomputer utilization in bioinformatics.
  • The developed middleware achieves good performance for large-scale bioinformatics analyses.
  • VGE enhances the accessibility and efficiency of supercomputing resources for bioinformatics.