Journal
COMPUTER GRAPHICS FORUM
Volume 28, Issue 7, Pages 1791-1800Publisher
WILEY
DOI: 10.1111/j.1467-8659.2009.01556.x
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Funding
- MKE/M-CST/IITA [2008-F-033-02, 2008-F-030-02]
- MCST/KEIT [2006-S-045-1]
- MKE/IITA u-Learning
- MKE digital mask control
- MCST/KOCCA-CTRDP-2009
- MSRA E-heritage
- [KRF-2008-313-D00922]
- Korea Evaluation Institute of Industrial Technology (KEIT) [KI001818, KI001534] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- National Research Foundation of Korea [과C6A1610, 313-2008-2-D00922] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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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 oil 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 perforin 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 air order of magnitude improvement in performance rising four CPU-cores and two GPUs, compared to rising a single CPU-core. This improvement results in an interactive performance, tip to 148fps, for various deforming benchmarks consisting of tens or hundreds of thousand triangles.
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