Computational performance of musculoskeletal simulation in OpenSim Moco using parallel computing

Abstract

AbstractOptimal control musculoskeletal simulation is a valuable approach for studying fundamental and clinical aspects of human movement. However, the high computational demand has long presented a substantial challenge, creating a need to improve simulation performance. The OpenSim Moco software package permits musculoskeletal simulation problems to be solved in parallel on multicore processors using the CasADi optimal control library, potentially reducing the computational demand. However, the computational performance of this framework has not been thoroughly examined. Thus, we aimed to investigate the computational speed‐up obtained via multicore parallel computing relative to solving problems serially (i.e., using a single core) in optimal control simulations of human movement in OpenSim Moco. Simulations were solved using up to 18 cores with a variety of temporal mesh interval densities and using two different initial guess strategies. We examined a range of musculoskeletal models and movements that included two‐ and three‐dimensional models, tracking and predictive simulations, and walking and reaching tasks. The maximum overall parallel speed‐up was problem specific and ranged from 1.7 to 7.7 times faster than serial, with most of the speed‐up achieved by about 6 processor cores. Parallel speed‐up was generally greater on finer temporal meshes, while the initial guess strategy had minimal impact on speed‐up. Considerable speed‐up can be achieved for some optimal control simulation problems in OpenSim Moco by leveraging the multicore processors often available in modern computers. However, since improvements are problem specific, achieving optimal computational performance will require some degree of exploration by the end user.