A Performance Analysis of Parallel Differential Dynamic Programming on a GPU
A Performance Analysis of Parallel Differential Dynamic Programming on a GPU
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DOI：
10.1007/978-3-030-44051-0_38
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发表时间：
2018-12
期刊：
影响因子：
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通讯作者：
Brian Plancher;S. Kuindersma
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文献类型：
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作者：
Brian Plancher;S. Kuindersma
并行性可用于显著提高计算成本高的算法的吞吐量。随着gpu等并行计算平台的广泛采用，人们自然会考虑这些架构是否有利于机器人研究人员在线解决轨迹优化问题。差分动态规划（DDP）算法通过利用优化的动力学方法和CPU多线程，在机器人任务中实现了一些最佳的定时性能。本文旨在分析使用在GPU上实现的DDP的多拍摄变体的更高程度并行化的好处和权衡。我们描述了我们的实现策略，并展示了与使用几个基准控制任务的等效多线程CPU实现相比的性能结果。我们的研究结果表明，在某些情况下，基于gpu的求解器可以提供增加的每次迭代计算时间和更快的收敛速度，但通常在收敛行为和算法级并行度之间存在权衡。
Parallelism can be used to significantly increase the throughput of computationally expensive algorithms. With the widespread adoption of parallel computing platforms such as GPUs, it is natural to consider whether these architectures can benefit robotics researchers interested in solving trajectory optimization problems online. Differential Dynamic Programming (DDP) algorithms have been shown to achieve some of the best timing performance in robotics tasks by making use of optimized dynamics methods and CPU multi-threading. This paper aims to analyze the benefits and tradeoffs of higher degrees of parallelization using a multiple-shooting variant of DDP implemented on a GPU. We describe our implementation strategy and present results demonstrating its performance compared to an equivalent multi-threaded CPU implementation using several benchmark control tasks. Our results suggest that GPU-based solvers can offer increased per-iteration computation time and faster convergence in some cases, but in general tradeoffs exist between convergence behavior and degree of algorithm-level parallelism.