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Related Experiment Videos

Enabling computer models of the heart for high-performance computers and the grid.

Joe Pitt-Francis1, Alan Garny, David Gavaghan

  • 1Oxford University Computing Laboratory, Wolfson Building, Parks Road, Oxford OX1 3QD, UK. joe.pitt-francis@comlab.ox.ac.uk

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|June 13, 2006
PubMed
Summary

High-performance computing is essential for complex heart disease modeling, enabling detailed in silico experiments. This research adapted a heart modeling package for multi-processor systems, improving efficiency for researchers.

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Area of Science:

  • Computational Biology
  • Biophysics
  • Cardiovascular Research

Background:

  • Current multi-cellular heart models are feasible on personal computers but insufficient for high-dimensional parameter sweeps.
  • In silico experimentation for complex heart diseases demands computational resources beyond commodity hardware.
  • Validated heart models need to be accessible to experimentalists for hypothesis generation and experimental planning.

Purpose of the Study:

  • To adapt a heart modeling package for high-performance computing (HPC) environments.
  • To enable novice users to utilize HPC resources for complex biological modeling without extensive technical expertise.
  • To facilitate in silico experimentation for heart disease research.

Main Methods:

  • Dissecting the "Cellular Open Resource" heart modeling package.

Related Experiment Videos

  • Porting the solving engine to C++.
  • Parallelizing the code using Message-Passing Interface (MPI) for multi-processor architectures.

    • Main Results:

    • Achieved good parallel efficiency on simple geometries.
    • Demonstrated realistic memory reduction on multi-processor systems.
    • Successfully adapted the heart modeling package for HPC environments.

    Conclusions:

    • High-performance computing is crucial for advancing in silico heart disease research.
    • Parallelization strategies can significantly enhance the performance of complex biological models.
    • Making HPC accessible to non-expert users is key to integrating computational tools into experimental workflows.