A scalable parallel method for large collision detection problems

Abstract

This paper discusses a parallel collision detection algorithm. Implemented using software executed on ubiquitous Graphics Processing Unit (GPU) cards, the algorithm demonstrates two orders of magnitude speedup over a state-of-the art sequential implementation when handling multimillion object collision detection tasks. GPUs are composed of many (on the order of hundreds) scalar processors that can simultaneously execute an operation; this strength is leveraged in the proposed algorithm, which combines the use of multiple CPU cores with multiple GPUs. The software implementation of the algorithm can be used to detect collisions between five million objects in less than two seconds and was used to detect 1.4 billion contact events in less than 40 seconds. A spherical padding approach is used to represent surface geometries as large collections of spheres when dealing with collision detection between bodies with complex geometries. The proposed methodology is expected to be relevant in computational mechanics with applications in granular flow dynamics and smoothed particle hydrodynamics (SPH), where the number of contact events ranges from millions to billions.