DISTRIBUTED MEMORY AND MULTI-GPU PARALLELIZATION OF A CARDIAC ELECTROPHYSIOLOGY SIMULATOR
GPGPU, Cardic Modelling, Parellel computing.
Cardiovascular diseases are the main causes of death in the world. Many of these dis-
eases require a deep and detailed understanding of electrophysiological changes for
to the study of new drugs and clinical devices to aid in the treatment. Consequently,
numerical simulations emerge as a relevant tool in the investigation of these electro-
physiological changes in heart disease. However, the complexity of applying mathe-
matical models of cardiac electrophysiology falls into systems of ordinary differential
equations (ODE) with a high number of unknowns, demanding great computational
effort. The work developed here consisted of the implementation of a distributed
memory parallelization and multi-GPU (Graphics Processing Unit) version of an
existing cardiac electrophysiology simulator with the aim of accelerate the solution
of models of this class. The monodomain cell membrane model and Bondarenko cell
dynamics model were adopted, together with adaptive time steps. The simulator
was run in a benchmark domain. Graphics cards of different performance were used
to carry out the tests. The combination of two lower performance graphics cards
allowed acceleration of more than 1.5 times for cases where the most refined mesh
was used. Load balancing between graphics cards of different performance provided
the best results. The results showed that the proposed implementation was able to
accelerate the solution of ODEs in different scenarios, proving to be an important
tool for numerical simulations of complex cardiac electrophysiology problems.