Parallel Computation of Trajectories Using Graphics Processing Units and Interpolated Gravity Models

Abstract

Seeking improvements in speed and accuracy in multiobject trajectory simulations, a solution methodology is presented that takes advantage of 1) new high-fidelity geopotential and third-body perturbation models that efficiently trade memory for speed, and 2) a graphics processing unit based integrator to achieve parallelism across multiple objects. The two methods combined lead to multiplicative speedups, making the tool three orders of magnitude faster, in some cases, compared to the same simulation performed in serial on a single central processing unit. The tool is capable of Monte Carlo simulations of a single object or of propagating the mean and covariance of all the objects in a space catalog. The tool performance is demonstrated for 1) a five-day Monte Carlo simulation of the state uncertainty point cloud modeled with over one million objects, and 2) a seven-day simulation of position and velocity states of a full space catalog with over 250,000 objects. The simulations required approximately 1 and 3 h of runtime, respectively, on a single desktop workstation using a single-core central processing unit and a single graphics processing unit. The solution method is applicable to a variety of space situational awareness applications, including orbit determination, catalog maintenance, uncertainty propagation, and conjunction analysis.