GRiD：GPU加速的刚体动力学分析
GRiD: GPU-Accelerated Rigid Body Dynamics with Analytical Gradients
摘要：
我们介绍GRiD：一个GPU加速库，用于计算刚体动力学分析梯度。GRiD旨在加速最先进的机器人规划、控制和机器学习中使用的非线性轨迹优化子问题，这需要在每次迭代时对刚体动力学及其梯度进行数十到数百次自然并行计算。GRiD利用URDF解析和代码生成来提供优化的动态内核，这些内核不仅公开GPU友好的计算模式，而且还利用每个计算内的细粒度并行性和计算之间的粗粒度并行性。通过这种方法，当执行刚体动力学算法的多个计算时，GRiD提供了最先进的多线程CPU实现的7.2倍加速，并且在考虑I/O开销时保持了2.5倍加速。我们将GRiD作为开源库发布，供更广泛的机器人社区使用。
英文摘要：
We introduce GRiD: a GPU-accelerated library for computing rigid body dynamics with analytical gradients. GRiD was designed to accelerate the nonlinear trajectory optimization subproblem used in state-of-the-art robotic planning, control, and machine learning, which requires tens to hundreds of naturally parallel computations of rigid body dynamics and their gradients at each iteration. GRiD leverages URDF parsing and code generation to deliver optimized dynamics kernels that not only expose GPU-friendly computational patterns, but also take advantage of both fine-grained parallelism within each computation and coarse-grained parallelism between computations. Through this approach, when performing multiple computations of rigid body dynamics algorithms, GRiD provides as much as a 7.2x speedup over a state-of-the-art, multi-threaded CPU implementation, and maintains as much as a 2.5x speedup when accounting for I/O overhead. We release GRiD as an open-source library for use by the wider robotics community.
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