Vigmond Edward J, Boyle Patrick M, Leon L, Plank Gernot
Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:3290-3. doi: 10.1109/IEMBS.2009.5333738.
Simulations of cardiac bioelectric phenomena remain a significant challenge despite continual advancements in computational machinery. Spanning large temporal and spatial ranges demands millions of nodes to accurately depict geometry, and a comparable number of timesteps to capture dynamics. This study explores a new hardware computing paradigm, the graphics processing unit (GPU), to accelerate cardiac models, and analyzes results in the context of simulating a small mammalian heart in real time. The ODEs associated with membrane ionic flow were computed on traditional CPU and compared to GPU performance, for one to four parallel processing units. The scalability of solving the PDE responsible for tissue coupling was examined on a cluster using up to 128 cores. Results indicate that the GPU implementation was between 9 and 17 times faster than the CPU implementation and scaled similarly. Solving the PDE was still 160 times slower than real time.
尽管计算机技术不断进步，但心脏生物电现象的模拟仍然是一项重大挑战。要涵盖较大的时间和空间范围，需要数百万个节点才能准确描绘几何形状，并且需要相当数量的时间步长来捕捉动态变化。本研究探索了一种新的硬件计算范式——图形处理单元（GPU），以加速心脏模型，并在实时模拟小型哺乳动物心脏的背景下分析结果。与膜离子流相关的常微分方程在传统CPU上进行计算，并与一至四个并行处理单元的GPU性能进行比较。在一个使用多达128个内核的集群上研究了解决负责组织耦合的偏微分方程的可扩展性。结果表明，GPU实现比CPU实现快9至17倍，并且具有相似的扩展性。求解偏微分方程的速度仍然比实时速度慢160倍。
