Year: 2023
Type: conference-paper
Abstract: Cardiac models are an important tool for understanding the causes of arrhythmias and other types of heart disease.Numerical simulations of these models require small time steps to make accurate predictions, leading to long simulation times even with programs parallelized over space.Parallelization over time presents a promising approach to increase the speed of cardiac simulations and to fully utilize highly parallel modern architectures.This study evaluates the use of the parareal algorithm, a parallel-in-time integration method for solving differential equations, for simulations of cardiac cells and tissue.The parareal algorithm estimates the system state at fixed times, then iteratively refines these estimates by solving from the previous time estimate in parallel.Thus, fast convergence to an accurate solution is necessary for this method to be viable.We perform simulations of the Beeler-Reuter (BR) model to evaluate the accuracy and speed of this method in comparison to sequential algorithms.We demonstrate that the parareal algorithm converges exponentially to the true solution in the single-cell case and accurately reproduces APD dynamics in tissue simulations, while attaining speedup relative to the serial algorithm in both cases.
Source: Computing in cardiology
Authors: Maxfield Comstock, Elizabeth Cherry
Institution: Georgia Institute of Technology
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4390446766