HPCCD: Hybrid Parallel Continuous Collision Detection using CPUs and GPUs

Abstract

Abstract We present a novel, hybrid parallel continuous collision detection (HPCCD) method that exploits the availability of multi‐core CPU and GPU architectures. HPCCD is based on a bounding volume hierarchy (BVH) and selectively performs lazy reconstructions. Our method works with a wide variety of deforming models and supports self‐collision detection. HPCCD takes advantage of hybrid multi‐core architectures – using the general‐purpose CPUs to perform the BVH traversal and culling while GPUs are used to perform elementary tests that reduce to solving cubic equations. We propose a novel task decomposition method that leads to a lock‐free parallel algorithm in the main loop of our BVH‐based collision detection to create a highly scalable algorithm. By exploiting the availability of hybrid, multi‐core CPU and GPU architectures, our proposed method achieves more than an order of magnitude improvement in performance using four CPU‐cores and two GPUs, compared to using a single CPU‐core. This improvement results in an interactive performance, up to 148 fps, for various deforming benchmarks consisting of tens or hundreds of thousand triangles.