Performance evaluation of GPU parallelization, space-time adaptive algorithms, and their combination for simulating...

Area of Science:

• Computational Biology
• Biophysics
• Cardiovascular Research

Background:

• Cardiac electrophysiology research relies on complex computational models.
• Increasing model complexity necessitates faster simulation techniques for realistic applications.

Purpose of the Study:

• To accelerate cardiac electrophysiology simulations.
• To evaluate the combined impact of parallel computing and adaptive algorithms on simulation speed and accuracy.

Main Methods:

• Implementation of multicore and Graphics Processing Unit (GPU) parallel computing.
• Development and application of a novel space-time adaptive algorithm.
• Evaluation of techniques individually and in combination across various simulation scenarios (3D slabs, complex geometries, different rhythms).

Main Results:

• Multicore/GPU parallel computing achieved ~33x speedup.
• Space-time adaptive algorithms yielded ~48x speedup.
• Combined techniques resulted in speedups ranging from 165x to 498x, with minimal loss of accuracy.

Conclusions:

• The integration of parallel computing and space-time adaptivity dramatically enhances cardiac simulation efficiency.
• These advanced computational methods enable faster, more accurate, and feasible simulations of cardiac electrophysiology.
• This approach facilitates deeper understanding and more realistic modeling of cardiac function and dysfunction.