Browsing by Author "Schulte, Miriam (Prof. Dr.)"

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    Data-integrated methods for performance improvement of massively parallel coupled simulations
    (2022) Totounferoush, Amin; Schulte, Miriam (Prof. Dr.)
    This thesis presents data-integrated methods to improve the computational performance of partitioned multi-physics simulations, particularly on highly parallel systems. Partitioned methods allow using available single-physic solvers and well-validated numerical methods for multi-physics simulations by decomposing the domain into smaller sub-domains. Each sub-domain is solved by a separate solver and an external library is incorporated to couple the solvers. This significantly reduces the software development cost and enhances flexibility, while it introduces new challenges that must be addressed carefully. These challenges include but are not limited to, efficient data communication between sub-domains, data mapping between not-matching meshes, inter-solver load balancing, and equation coupling. In the current work, inter-solver communication is improved by introducing a two-level communication initialization scheme to the coupling library preCICE. The new method significantly speed-ups the initialization and removes memory bottlenecks of the previous implementation. In addition, a data-driven inter-solver load balancing method is developed to efficiently distribute available computational resources between coupled single-physic solvers. This method employs both regressions and deep neural networks (DNN) for modeling the performance of the solvers and derives and solves an optimization problem to distribute the available CPU and GPU cores among solvers. To accelerate the equation coupling between strongly coupled solvers, a hybrid framework is developed that integrates DNNs and classical solvers. The DNN computes a solution estimation for each time step which is used by classical solvers as a first guess to compute the final solution. To preserve DNN's efficiency during the simulation, a dynamic re-training strategy is introduced that updates the DNN's weights on-the-fly. The cheap but accurate solution estimation by the DNN surrogate solver significantly reduces the number of subsequent classical iterations necessary for solution convergence. Finally, a highly scalable simulation environment is introduced for fluid-structure interaction problems. The environment consists of highly parallel numerical solvers and an efficient and scalable coupling library. This framework is able to efficiently exploit both CPU-only and hybrid CPU-GPU machines. Numerical performance investigations using a complex test case demonstrate a very high parallel efficiency on a large number of CPUs and a significant speed-up due to the GPU acceleration.
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    Scalable biophysical simulations of the neuromuscular system
    (2021) Maier, Benjamin; Schulte, Miriam (Prof. Dr.)
    The human neuromuscular system consisting of skeletal muscles and neural circuits is a complex system that is not yet fully understood. Surface electromyography (EMG) can be used to study muscle behavior from the outside. Computer simulations with detailed biophysical models provide a non-invasive tool to interpret EMG signals and gain new insights into the system. The numerical solution of such multi-scale models imposes high computational work loads, which restricts their application to short simulation time spans or coarse resolutions. We tackled this challenge by providing scalable software employing instruction-level and task-level parallelism, suitable numerical methods and efficient data handling. We implemented a comprehensive, state-of-the-art, multi-scale multi-physics model framework that can simulate surface EMG signals and muscle contraction as a result of neuromuscular stimulation. This work describes the model framework and its numerical discretization, develops new algorithms for mesh generation and parallelization, covers the use and implementation of our software OpenDiHu, and evaluates its computational performance in numerous use cases. We obtain a speedup of several hundred compared to a baseline solver from the literature and demonstrate, that our distributed-memory parallelization and the use of High Performance Computing resources enables us to simulate muscular surface EMG of the biceps brachii muscle with realistic muscle fiber counts of several hundred thousands. We find that certain model effects are only visible with such high resolution. In conclusion, our software contributes to more realistic simulations of the neuromuscular system and provides a tool for applied researchers to complement in vivo experiments with in-silico studies. It can serve as a building block to set up comprehensive models for more organs in the musculoskeletal system.