  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.
