Key result
An optimized data structure and memory access pattern for multi-GPU platforms accelerated cardiac electrical dynamics simulations, achieving results approximately 1.4 times faster than a standard GPU method.
Why the study?
Simulations of cardiac electrophysiology require extensive computational resources, and GPU acceleration faces memory access bottlenecks from spatial discretization of large tissue domains.
A novel data structure and multi-GPU approach significantly accelerates 3D cardiac electrophysiological simulations, enabling complex models to run locally at near real-time speeds.
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May accelerate local cardiac simulation research; leaves open clinical validation and workflow integration.
Simulations of cardiac electrophysiological models in tissue, particularly in 3D require the solutions of billions of differential equations even for just a couple of milliseconds, thus highly demanding in computational resources. In fact, even studies in small domains with very complex models may take several hours to reproduce seconds of electrical cardiac behavior. Today's Graphics Processor Units (GPUs) are becoming a way to accelerate such simulations, and give the added possibilities to run them locally without the need for supercomputers. Nevertheless, when using GPUs, bottlenecks related to global memory access caused by the spatial discretization of the large tissue domains being simulated, become a big challenge. For simulations in a single GPU, we propose a strategy to accelerate the computation of the diffusion term through a data-structure and memory access pattern designed to maximize coalescent memory transactions and minimize branch divergence, achieving results approximately 1.4 times faster than a standard GPU method. We also combine this data structure with a designed communication strategy to take advantage in the case of simulations in multi-GPU platforms. We demonstrate that, in the multi-GPU approach performs, simulations in 3D tissue can be just 4× slower than real time.
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Vasconcellos et al. (2019) studied Cardiac electrical dynamics (simulations). Optimized data structure and multi-GPU platform vs. Standard GPU method was evaluated on Computation speed of the diffusion term (1.4 times faster). An optimized data structure and memory access pattern for multi-GPU platforms accelerated cardiac electrical dynamics simulations, achieving results approximately 1.4 times faster than a standard GPU method.
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