Bridging scales with volume coupling — Scalable simulations of muscle contraction and electromyography

Benjamin Maier (), David Schneider (), Miriam Schulte () and Benjamin Uekermann ()
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Benjamin Maier: Universität Stuttgart, Institute for Parallel and Distributed Systems
David Schneider: Universität Stuttgart, Institute for Parallel and Distributed Systems
Miriam Schulte: Universität Stuttgart, Institute for Parallel and Distributed Systems
Benjamin Uekermann: Universität Stuttgart, Institute for Parallel and Distributed Systems

A chapter in High Performance Computing in Science and Engineering '21, 2023, pp 185-199 from Springer

Abstract: Abstract Measuring the electric signals of a contracting muscle, called electromyography (EMG), is a valuable experimental tool for biomechanics researchers. Detailed simulations of this process can deliver insights on a new level. However, the solution of appropriate biophysically based multi-scale models exhibits high computational loads, which demands for High Performance Computing. At the same time, separate parallelization strategies are required for different spatial scales. Currently, no opensource software is capable of efficiently exploiting supercomputers and handling the complexity of relevant state-of-the-art multi-scale muscle models. We employ a volume coupling scheme to augment the open-source software OpenDiHu and enable coupled simulations of muscle contraction and EMG.We investigate the weak scaling behavior of different components of the multi-scale solver and find that the new approach does not hinder scalability. As a result, we are able to simulate a respective highly resolved scenario with 100 million degrees of freedom on the supercomputer Hawk at the High Performance Computing Center Stuttgart.

Date: 2023
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DOI: 10.1007/978-3-031-17937-2_11

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