Abstract
Continuous collision detection (CCD) is a process to interpolate the trajectory of polygons and detect collisions between successive time steps. However, primitive-level CCD is a very time-consuming process especially for a large number of moving polygons. Over the years, a number of approaches have been proposed to improve the computational efficiency of CCD by culling out the non-colliding primitives before exact overlap tests. These approaches have two fundamental disadvantages. First, they are mainly designed for self- and pairwise CCD and thus the performance gain would be limited when they are applied to large-scale scenes that contain thousands of moving polygons. Second, they are designed as sequential processes appropriate for execution on a single processor. Therefore, deploying them on high-performance parallel computing systems would not increase their computational efficiency.
In this paper, we present a parallel CCD algorithm, which aims to accelerate N-body CCD culling by distributing the load across a high-performance GPU cluster. Our implementation integrates frameworks such as Message Passing Interface and CUDA, which is particularly suitable for large-scale distributed simulations. Experimental results, based on simulations conducted on a supercomputer, demonstrate that our approach is more computationally efficient than existing sequential CCD approaches.
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Source code
Peng Du
Hangzhou Dianzi University
dp dot zju dot edu dot cn
