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Distributed computing for membrane-based modeling of action potential propagation.

D Porras1, J M Rogers, W M Smith

  • 1Department of Biomedical Engineering, University of Alabama at Birmingham 35294, USA.

IEEE Transactions on Bio-Medical Engineering
|August 16, 2000
PubMed
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Simulating action potential propagation is computationally intensive. This study optimized distributed computing schemes, achieving up to 89% of theoretical speedups for faster cardiac electrophysiology simulations.

Area of Science:

  • Computational biology
  • Biophysics
  • Scientific computing

Background:

  • Action potential propagation simulations are crucial for understanding cardiac electrophysiology but are computationally expensive.
  • Physiologic membrane currents and macroscopic tissue dimensions contribute to high computational load.

Purpose of the Study:

  • To analyze distributed computing schemes for reducing execution time in workstation clusters.
  • To parallelize solutions for computationally intensive cardiac electrophysiology simulations using message passing.

Main Methods:

  • Four distributed computing schemes were evaluated in 2D monodomain simulations using Beeler-Reuter membrane equations.
  • Parallel speedups were measured and compared against theoretical speedups, considering serial code execution.

Related Experiment Videos

  • A data decomposition scheme based on total ionic current was identified as the most performant.

    • Main Results:

    • The data decomposition scheme achieved 89% +/- 2% and 75% +/- 8% of theoretical speedups in homogeneous and heterogeneous clusters, respectively.
    • A load-balancing algorithm was developed based on communication latency analysis.
    • Speedups exceeding 3.0 were observed with the Luo-Rudy equations on eight distributed workstations.

    Conclusions:

    • Distributed computing schemes, particularly data decomposition with load balancing, significantly reduce execution time for cardiac electrophysiology simulations.
    • Achieved speedups are comparable to those on shared memory systems, making workstation clusters a viable alternative.
    • Optimized parallelization strategies are essential for advancing complex biophysical modeling.