Cardiovascular disease affects millions of people worldwide and its social and economic cost clearly motivates scientific research. Computer simulation can lead to a better understanding of cardiac physiology, and for pathology presents opportunities for low-cost and low-risk design and testing of therapies, including surgical and pharmacological intervention as well as automated diagnosis and screening. Currently, the simulation of a whole heart model, including the interaction of electrophysiology, solid mechanics and fluid dynamics is the subject of ongoing research in computational science. Typically, the computation of a single heartbeat requires many processor hours on a supercomputer. The financial and ultimately environmental cost of such a computation prevents it from becoming a viable clinical or research solution. We re-formulate the standard mathematical models of continuum mechanics, such as the Bidomain Model, Finite Strain Theory and the Navier-Stokes Equations, specifically for parallel processing and show proof-of-concept of a computational approach that can generate a complete description of a human heartbeat on a single Graphics Processing Unit (GPU) within a few minutes. The approach is based on a Finite Volume Method (FVM) discretisation which is both matrix- and mesh-free, ideally suited to voxel-based medical imaging data. The solution of nonlinear ordinary and partial differential equations proceeds via the method of lines and operator-splitting. The resulting algorithm is implemented in the OpenCL standard and can run on almost any platform. It does not perform any CPU processing and has no dependence on third-party software libraries.

翻译：心血管疾病影响着全球数百万人，其社会和经济成本明确推动了科学研究。计算机模拟能够加深对心脏生理学的理解，并为病理学研究提供低成本、低风险的疗法设计与测试机会，包括手术和药物干预以及自动化诊断与筛查。目前，涵盖电生理学、固体力学和流体动力学相互作用的全心脏模型模拟仍是计算科学领域的前沿研究课题。通常，单次心跳的模拟需要在超级计算机上耗费大量处理器时间。这种计算产生的资金成本和最终的环境成本使其难以成为可行的临床或研究解决方案。我们重新构建了连续介质力学的标准数学模型（如双域模型、有限应变理论和纳维-斯托克斯方程），专门针对并行处理进行优化，并展示了一种计算方法的概念验证：该方法可在单个图形处理器（GPU）上于数分钟内生成人类心跳的完整描述。该方案基于有限体积法（FVM）离散化，既无需矩阵也无需网格，特别适用于基于体素的医学影像数据。非线性常微分方程和偏微分方程的求解采用线方法和算子分裂技术。所得到的算法基于OpenCL标准实现，可在几乎所有平台上运行。该算法不执行任何CPU处理，且不依赖第三方软件库。