Employing phenomenological model in load-balancing optimization of parallel multi-scale muscle simulations

Since multi-scale models of muscles rely on the integration of physical and biochemical properties across multiple length and time scales, these models are highly CPU consuming and memory intensive. Therefore, their practical implementation and usage in real-world applications is limited by their high requirements for computational power. There are various reported solutions to the problems of the distributed computation of the complex systems that could also be applied to the multi-scale muscle simulations. In this paper, we present a novel load balancing method for parallel multi-scale muscle simulations on distributed computing resources. The method uses data obtained from simple Hill phenomenological model in order to predict computational weights of the integration points within the multi-scale model. Using obtained weights it is possible to improve domain decomposition prior to multi-scale simulation run and consequently significantly reduce computational time. The method is applied to two-scale muscle model where a finite element (FE) macro model is coupled with Huxley's model of cross-bridge kinetics on the microscopic level. The massive parallel solution is based on decomposition of micro model domain and static scheduling policy. It was verified on real-world example, showing high utilization of all involved CPUs and ensuring high scalability, thanks to the novel scheduling approach. Performance analysis clearly shown that inclusion of complexities prediction in reducing the execution time of parallel run by about 40% compared to the same model with scheduler that assumes equal complexities of all micro models.