Scalable collision detection using p-partition fronts on many-core processors

Xinyu Zhang, Young J. Kim

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

We present a new parallel algorithm for collision detection using many-core computing platforms of CPUs or GPUs. Based on the notion of a $(p)$-partition front, our algorithm is able to evenly partition and distribute the workload of BVH traversal among multiple processing cores without the need for dynamic balancing, while minimizing the memory overhead inherent to the state-of-the-art parallel collision detection algorithms. We demonstrate the scalability of our algorithm on different benchmarking scenarios with and without using temporal coherence, including dynamic simulation of rigid bodies, cloth simulation, and random collision courses. In these experiments, we observe nearly linear performance improvement in terms of the number of processing cores on the CPUs and GPUs.

Original languageEnglish
Article number6620867
Pages (from-to)447-456
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Volume20
Issue number3
DOIs
StatePublished - Mar 2014

Keywords

  • (p)-partition
  • Collision detection
  • static workload balancing

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