Institute of Mathematicsand Scientific Computing, Karl Franzens University of Graz, Graz, Austria.
IEEE Trans Biomed Eng. 2012 Aug;59(8):2281-90. doi: 10.1109/TBME.2012.2202661. Epub 2012 Jun 5.
Anatomically realistic and biophysically detailed multiscale computer models of the heart are playing an increasingly important role in advancing our understanding of integrated cardiac function in health and disease. Such detailed simulations, however, are computationally vastly demanding, which is a limiting factor for a wider adoption of in-silico modeling. While current trends in high-performance computing (HPC) hardware promise to alleviate this problem, exploiting the potential of such architectures remains challenging since strongly scalable algorithms are necessitated to reduce execution times. Alternatively, acceleration technologies such as graphics processing units (GPUs) are being considered. While the potential of GPUs has been demonstrated in various applications, benefits in the context of bidomain simulations where large sparse linear systems have to be solved in parallel with advanced numerical techniques are less clear. In this study, the feasibility of multi-GPU bidomain simulations is demonstrated by running strong scalability benchmarks using a state-of-the-art model of rabbit ventricles. The model is spatially discretized using the finite element methods (FEM) on fully unstructured grids. The GPU code is directly derived from a large pre-existing code, the Cardiac Arrhythmia Research Package (CARP), with very minor perturbation of the code base. Overall, bidomain simulations were sped up by a factor of 11.8 to 16.3 in benchmarks running on 6-20 GPUs compared to the same number of CPU cores. To match the fastest GPU simulation which engaged 20 GPUs, 476 CPU cores were required on a national supercomputing facility.
在推进对健康和疾病中心脏综合功能的理解方面，具有解剖学逼真和生物物理细节的心脏多尺度计算机模型正发挥着越来越重要的作用。然而，这种详细的模拟在计算上要求极高，这是广泛采用计算机建模的一个限制因素。尽管当前高性能计算（HPC）硬件的发展趋势有望缓解这一问题，但由于需要强可扩展性算法来减少执行时间，因此充分利用这些架构的潜力仍然具有挑战性。或者，可以考虑使用图形处理单元（GPU）等加速技术。虽然 GPU 的潜力已经在各种应用中得到了证明，但在需要使用先进数值技术并行求解大型稀疏线性系统的双域模拟中，其优势并不明显。在这项研究中，通过使用最先进的兔心室模型运行强可扩展性基准测试，证明了多 GPU 双域模拟的可行性。该模型使用完全非结构化网格的有限元方法（FEM）进行空间离散化。GPU 代码是直接从一个大型现有代码——心律失常研究包（CARP）中衍生出来的，对代码库的改动非常小。总的来说，与相同数量的 CPU 核相比，在 6-20 个 GPU 上运行的基准测试中，双域模拟的速度提高了 11.8 到 16.3 倍。为了匹配最快的 GPU 模拟，需要在国家超级计算设施上使用 476 个 CPU 核。
