GPU-based Acceleration of Detailed Tissue-Scale Cardiac Simulations

We present a GPU based implementation for tissue-scale 3D simulations of the human cardiac ventricle using a physiologically realistic cell model. Computational challenges in such simulations arise from two factors, the first of which is the sheer amount of computation when simulating a large number of cardiac cells in a detailed model containing 104 calcium release units, 106 stochastically changing ryanodine receptors and 1.5 x 105 L-type calcium channels per cell. Additional challenges arise from the fact that the computational tasks have various levels of arithmetic intensity and control complexity, which require careful adaptation of the simulation code to the target device. By exploiting the strengths of the GPU, we obtain a performance that is far superior to that of the CPU, and also significantly higher than that of other state of the art manycore devices, thus paving the way for detailed whole-heart simulations in future generations of leadership class supercomputers.