GPGPU accelerated cardiac arrhythmia simulations.

Computational modeling of cardiac electrophysiology is a powerful tool for studying arrhythmia mechanisms. In particular, cardiac models are useful for gaining insights into experimental studies, and in the foreseeable future they will be used by clinicians to improve therapy for the patients suffering from complex arrhythmias. Such models are highly intricate, both in their geometric structure and in the equations that represent myocyte electrophysiology. For these models to be useful in a clinical setting, cost-effective solutions for solving the models in real time must be developed. In this work, we hypothesized that low-cost GPGPU-based hardware systems can be used to accelerate arrhythmia simulations. We ported a two dimensional monodomain cardiac model and executed it on various GPGPU platforms. Electrical activity was simulated during point stimulation and rotor activity. Our GPGPU implementations provided significant speedups over the CPU implementation: 18X for point stimulation and 12X for rotor activity. We found that the number of threads that could be launched concurrently was a critical factor in optimizing the GPGPU implementations.