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A memory optimization method combined with adaptive time-step method for cardiac cell simulation based on multi-GPU.

Ching-Hsing Luo1,2, Haiyi Ye3, Xingji Chen3,4

  • 1School of Data and Computer Science, Sun Yat-sen University, Guangzhou, Guangdong, China. luojinx5@mail.sysu.edu.cn.

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Summary

This study introduces a novel memory allocation method to optimize adaptive time-step simulations of cardiac electrophysiology on GPUs. This approach significantly accelerates heart cell simulations, overcoming previous synchronization challenges.

Keywords:
Adaptive time-step methodComputer simulationHigh performance computingMemory optimizationVentricular cell

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Area of Science:

  • Computational Biology
  • Biophysics
  • High-Performance Computing

Background:

  • Cardiac electrophysiological simulations are computationally intensive.
  • Graphics Processing Units (GPUs) offer significant computational acceleration.
  • Adaptive time-stepping methods speed up simulations but face GPU synchronization issues.

Purpose of the Study:

  • To propose an optimized memory allocation method for adaptive time-step simulations on GPUs.
  • To overcome synchronization problems and enhance the efficiency of GPU-accelerated cardiac simulations.
  • To improve computational cost-efficiency in simulating cardiac electrophysiology.

Main Methods:

  • Developed a memory allocation strategy focusing on stimulus points and potential arrangement for optimal storage efficiency.
  • Implemented calculations on GPUs using column-order arrangement for large matrices like potential.
  • Validated the method using Luo-Rudy passive (LR1) and dynamic (LRd) ventricular action potential models with Traditional Hybrid Method (THM) and Chen-Chen-Luo's (CCL) quadratic adaptive algorithm.

Main Results:

  • Achieved significant acceleration ratios: ×34 (THM) and ×75 (CCL) for LR1 model on a single GPU compared to fixed time-step.
  • Demonstrated substantial speedups (e.g., ×75 for CCL on 4 GPUs) for LRd model on multi-GPUs, with linear scalability.
  • Maintained simulation accuracy with Mixed Root Mean Square Error (MRMSE) below 5%.

Conclusions:

  • The proposed memory arrangement method effectively implements adaptive time-stepping on GPUs, overcoming synchronization bottlenecks.
  • This optimization significantly reduces computational cost and accelerates cardiac electrophysiological simulations.
  • The findings pave the way for more efficient and faster computational modeling of heart function.