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Distributed Acceleration of Adhesive Dynamics Simulations.

Daniel F Puleri1, Aristotle X Martin1, Amanda Randles1

  • 1Department of Biomedical Engineering, Duke University, Durham, NC, USA.

Proceedings of 2022 29Th European MPI Users' Group Meeting (Eurompi/Usa'2022) : September 26-28, 2022, Chattanooga, TN. European MPI Users' Group Meeting (29Th : 2022 : Chattanooga, Tenn.)
|January 11, 2024
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Summary
This summary is machine-generated.

This study introduces a hybrid parallelization method to accelerate cell adhesion modeling. This computational advance enables large-scale simulations of cell transport in microvessels, crucial for understanding cancer metastasis.

Keywords:
IBMLBMMPIOpenMPadhesive cellsdistributed parallelizationfluid-structure interaction

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Area of Science:

  • Computational biology
  • Biophysics
  • Scientific computing

Background:

  • Cell adhesion is vital for biological processes like leukocyte migration and cancer metastasis.
  • Modeling cell adhesion over large distances in microvessels is computationally demanding.
  • Current models struggle to balance sub-micron resolution for adhesion with large-scale domain simulation.

Purpose of the Study:

  • To develop an accelerated computational model for cell adhesion dynamics.
  • To enable efficient simulation of large field-of-view domains in microvessel networks.
  • To bridge the gap between high-resolution cell models and large-scale fluid-structure interaction (FSI) models.

Main Methods:

  • Introduced a hybrid parallelization scheme using on-node and distributed computing.
  • Implemented a fully deformable adhesive dynamics cell model.
  • Augmented on-node acceleration with spatial data structures and algorithmic changes.

Main Results:

  • Achieved performant system usage on modern supercomputers.
  • Successfully accelerated the deformable adhesive cell model.
  • Enabled simulations that bridge sub-micron adhesive interactions with large-scale FSI.

Conclusions:

  • The hybrid parallelization scheme significantly enhances computational efficiency for cell adhesion models.
  • This accelerated model allows for the study of complex phenomena like cancer cell transport in microcirculation.
  • Facilitates previously unfeasible research questions in microvessel network dynamics.