Cardiac electrophysiology numerical models using symmetric multiprocessing (SMP)

Area of Science:

• Computational electrophysiology
• Biophysics
• Parallel computing

Background:

• Electrophysiological models simulate electrical propagation in tissues using complex differential equations.
• Detailed simulations are computationally intensive, requiring efficient computational tools.
• Modern multi-core processors are underutilized by traditional sequential simulation methodologies.

Purpose of the Study:

• To identify performance bottlenecks in symmetric multiprocessing (SMP) for cardiac tissue electrophysiological models.
• To demonstrate scalable and effective computational methodologies for parallel simulations.
• To enhance the efficiency of simulating electrical propagation phenomena.

Main Methods:

• Analysis of performance bottlenecks in symmetric multiprocessing (SMP).
• Implementation and evaluation of discretisation schemes for parallel processing.
• Assessment of message passing strategies in SMP environments.

Main Results:

• Identified performance limitations in sequential simulation approaches on multi-core CPUs.
• Demonstrated the scalability of proposed discretisation and message passing methodologies.
• Showcased improved computational efficiency for cardiac electrophysiological simulations.

Conclusions:

• Parallel computing strategies are crucial for efficient simulation of complex electrophysiological models.
• Optimized discretisation and message passing enhance the performance of SMP systems.
• The presented methodologies improve the feasibility of detailed cardiac tissue simulations.