The EMI model represents excitable cells in a more accurate manner than traditional homogenized models at the price of increased computational complexity. The increased complexity of solving the EMI model stems from a significant increase in the number of computational nodes and from the form of the linear systems that need to be solved. Here, we will show that the latter problem can be solved by careful use of operator splitting of the spatially coupled equations. By using this method, the linear systems can be broken into sub-problems that are of the classical type of linear, elliptic boundary value problems. Therefore, the vast collection of methods for solving linear, elliptic partial differential equations can be used. We demonstrate that this enables us to solve the systems using shared-memory parallel computers. The computing time scales perfectly with the number of physical cells. For a collection of 512 × 256 cells, we solved linear systems with about 2.5×108 unknows. Since the computational effort scales linearly with the number of physical cells, we believe that larger computers can be used to simulate millions of excitable cells and thus allow careful analysis of physiological systems of great importance.
We want to be able to perform accurate simulations of a large number of cardiac cells based on mathematical models where each individual cell is represented in the model. This implies that the computational mesh has to have a typical resolution of a few µm leading to huge computational challenges. In this paper we use a certain operator splitting of the coupled equations and showthat this leads to systems that can be solved in parallel. This opens up for the possibility of simulating large numbers of coupled cardiac cells.
Ion channels on the membrane of cardiomyocytes regulate the propagation of action potentials from cell to cell and hence are essential for the proper function of the heart. Through computer simulations with the classical monodomain model for cardiac tissue and the more recent extracellular-membrane-intracellular (EMI) model where individual cells are explicitly represented, we investigated how conduction velocity (CV) in cardiac tissue depends on the strength of various ion currents as well as on the spatial distribution of the ion channels. Our simulations show a sharp decrease in CV when reducing the strength of the sodium (Na+) currents, whereas independent reductions in the potassium (K1 and Kr) and L-type calcium currents have negligible effect on the CV. Furthermore, we find that an increase in number density of Na+ channels towards the cell ends increases the CV, whereas a higher number density of K1 channels slightly reduces the CV. These findings contribute to the understanding of ion channels (e.g. Na+ and K+ channels) in the propagation velocity of action potentials in the heart.
A new trend in processor architecture design is the packaging of thousands of small processor cores into a single device, where there is no device-level shared memory but each core has its own local memory. Thus, both the work and data of an application code need to be carefully distributed among the small cores, also termed as tiles. In this paper, we investigate how numerical computations that involve unstructured meshes can be efficiently parallelized and executed on a massively tiled architecture. Graphcore IPUs are chosen as the target hardware platform, to which we port an existing monodomain solver that simulates cardiac electrophysiology over realistic 3D irregular heart geometries. There are two computational kernels in this simulator, where a 3D diffusion equation is discretized over an unstructured mesh and numerically approximated by repeatedly executing sparse matrix-vector multiplications (SpMVs), whereas an individual system of ordinary differential equations (ODEs) is explicitly integrated per mesh cell. We demonstrate how a new style of programming that uses Poplar/C++ can be used to port these commonly encountered computational tasks to Graphcore IPUs. In particular, we describe a per-tile data structure that is adapted to facilitate the inter-tile data exchange needed for parallelizing the SpMVs. We also study the achievable performance of the ODE solver that heavily depends on special mathematical functions, as well as their accuracy on Graphcore IPUs. Moreover, topics related to using multiple IPUs and performance analysis are addressed. In addition to demonstrating an impressive level of performance that can be achieved by IPUs for monodomain simulation, we also provide a discussion on the generic theme of parallelizing and executing unstructured-mesh multiphysics computations on massively tiled hardware.
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