Towards realistic HPC models of the neuromuscular system

作     者：Bradley, Chris Emamy, Nehzat Ertl, Thomas Göddeke, Dominik Hessenthaler, Andreas Klotz, Thomas Krämer, Aaron Krone, Michael Maier, Benjamin Mehl, Miriam Rau, Tobias Röhrle, Oliver 

作者机构： SimTech Research Group on Continuum Biomechanics and Mechanobiology University of Stuttgart Institute for Applied Analysis and Numerical Simulation University of Stuttgart Institute for Parallel and Distributed Systems University of Stuttgart  University of Stuttgart Stuttgart Centre for Simulation Sciences University of Stuttgart Germany Auckland Bioengineering Institute University of Auckland New Zealand 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2018年

核心收录：

主　　题：Scalability 

摘      要：Realistic simulations of detailed, biophysics-based, multi-scale models require very high resolution and, thus, large-scale compute facilities. Existing simulation environments, especially for biomedical applications, are designed to allow for a high flexibility and generality in model development. Flexibility and model development, however, are often a limiting factor for large-scale simulations. Therefore, new models are typically tested and run on small-scale compute facilities. By using a detailed biophysics-based, chemo-electromechanical skeletal muscle model and the international open-source software library OpenCMISS as an example, we present an approach to upgrade an existing muscle simulation framework from a moderately parallel version towards a massively parallel one that scales both in terms of problem size and in terms of the number of parallel processes. For this purpose, we investigate different modeling, algorithmic and implementational aspects. We present improvements addressing both numerical and parallel scalability. In addition, our approach includes a novel visualization environment, which is based on the MegaMol environment capable of handling large amounts of simulated data. It offers a platform for fast visualization prototyping, distributed rendering, and advanced visualization techniques. We present results of a variety of scaling studies at the Tier-1 supercomputer HazelHen at the High Performance Computing Center Stuttgart (HLRS). We improve the overall runtime by a factor of up to 2.6 and achieved good scalability on up to 768 cores, where the previous implementation used only 4 *** Codes 65L99, 65M99 Copyright © 2018, The Authors. All rights reserved.