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HPC AND GRID COMPUTING FOR INTEGRATIVE BIOMEDICAL RESEARCH.

Tahsin Kurc1, Shannon Hastings, Vijay Kumar

  • 1CENTER FOR COMPREHENSIVE INFORMATICS, EMORY UNIVERSITY, ATLANTA, GA 30322, USA.

The International Journal of High Performance Computing Applications
|January 29, 2010
PubMed
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Integrative biomedical research requires advanced software middleware to handle diverse data from multiple biological scales. This paper explores these needs and proposes solutions using Grid and high-performance computing.

Area of Science:

  • Biomedical research
  • Computational biology
  • Bioinformatics

Background:

  • Integrative biomedical research analyzes diverse data types across multiple biological scales.
  • High-throughput and high-resolution instruments generate vast, complex datasets.
  • Existing software middleware faces challenges in supporting these integrative projects.

Purpose of the Study:

  • To identify and analyze the software middleware requirements for integrative biomedical research.
  • To explore the potential of Grid and high-performance computing (HPC) in addressing these requirements.
  • To provide insights into designing effective middleware for future biological research.

Main Methods:

  • Analysis of research pattern templates to define middleware requirements.

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  • Discussion of software middleware incorporating Grid and HPC.
  • Examination of data integration, querying, and analysis challenges.

    • Main Results:

    • Identified specific challenges in handling multi-scale biological data.
    • Highlighted the need for scalable and robust middleware solutions.
    • Demonstrated the applicability of Grid and HPC for integrative research.

    Conclusions:

    • Middleware systems leveraging Grid and HPC are crucial for advancing integrative biomedical research.
    • Addressing these middleware requirements will accelerate discovery across biological scales.
    • Future software development should focus on integrating diverse data and computational resources.