Optimal 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.

中文翻译：

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使用并行计算在 OpenSim Moco 中进行肌肉骨骼模拟的计算性能


最佳控制肌肉骨骼模拟是研究人类运动的基础和临床方面的一种有价值的方法。然而，高计算需求长期以来一直是一个巨大的挑战，需要提高模拟性能。 OpenSim Moco 软件包允许使用 CasADi 最优控制库在多核处理器上并行解决肌肉骨骼模拟问题，从而可能减少计算需求。然而，该框架的计算性能尚未得到彻底检验。因此，我们的目的是研究通过多核并行计算相对于在 OpenSim Moco 中人体运动的最优控制模拟中串行解决问题（即使用单核）获得的计算速度。使用多达 18 个具有各种时间网格间隔密度的核心并使用两种不同的初始猜测策略来解决模拟问题。我们检查了一系列肌肉骨骼模型和运动，包括二维和三维模型、跟踪和预测模拟以及行走和到达任务。最大总体并行加速是针对特定问题的，比串行快 1.7 到 7.7 倍，其中大部分加速是通过大约 6 个处理器内核实现的。并行加速通常在更精细的时间网格上更大，而初始猜测策略对加速的影响最小。通过利用现代计算机中常用的多核处理器，OpenSim Moco 中的一些最优控制仿真问题可以实现相当大的加速。然而，由于改进是针对特定问题的，因此实现最佳计算性能将需要最终用户进行一定程度的探索。