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Multi-GPU Immersed Boundary Method Hemodynamics Simulations.

Jeff Ames1, Daniel F Puleri2, Peter Balogh2

  • 1Department of Computer Science, Duke University, Durham, NC USA.

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|August 6, 2020
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Summary
This summary is machine-generated.

This study presents the largest GPU-accelerated fluid structure interaction (FSI) simulations of blood flow, involving over 17 million red blood cells. Data movement between CPUs and GPUs emerged as the primary performance bottleneck in these large-scale simulations.

Keywords:
GPUdistributed parallelizationfluid structure interactionimmersed boundary methodlattice Boltzmann method

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Area of Science:

  • Computational fluid dynamics
  • Biomedical engineering
  • High-performance computing

Background:

  • Cell-resolved blood flow simulations are gaining traction due to algorithmic and computational advancements.
  • Fluid structure interaction (FSI) methods, particularly those using the immersed boundary method, are common for modeling cell-resolved hemodynamics.
  • Graphics Processing Units (GPUs) offer acceleration but introduce communication challenges in heterogeneous CPU-GPU systems.

Purpose of the Study:

  • To conduct and analyze the largest distributed GPU-accelerated FSI simulations of high hematocrit, cell-resolved blood flow.
  • To compare the performance and scalability of these simulations on different GPU configurations (fat nodes vs. single GPU per node).
  • To identify performance bottlenecks in multiscale multi-grid FSI simulations on heterogeneous computing architectures.

Main Methods:

  • Implementation of distributed GPU-accelerated fluid structure interaction (FSI) algorithms.
  • Simulation of blood flow with over 17 million individual red blood cell models.
  • Comparative performance analysis across heterogeneous systems with varying GPU densities (e.g., 6 GPUs/node vs. 1 GPU/node).
  • Benchmarking of CPU-based versus GPU-based implementations to quantify data movement costs.

Main Results:

  • Successfully executed the largest distributed GPU-accelerated FSI simulations for high hematocrit blood flow to date.
  • Identified data movement between CPU and GPU as the most significant performance bottleneck in multiscale multi-grid FSI simulations on heterogeneous systems.
  • Demonstrated performance differences based on GPU node architecture.

Conclusions:

  • GPU acceleration is feasible for large-scale cell-resolved blood flow simulations.
  • Optimizing data transfer strategies is crucial for maximizing performance and scalability on heterogeneous CPU-GPU systems.
  • Further research is needed to mitigate data movement bottlenecks for exascale computing.