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GPU-Accelerated Collision Detection and Taichi DEM Optimization Challenge
22 décembre 2022 | Yuanming Hu, Qian Bao
Numerical simulation and computer graphics usually involve collision detection of a massive number of particles (in many cases, millions of particles). Regular operations, such as particle movement and boundary handling, can be handled in O(N) time complexity (N refers to the number of particles). But the complexity of collision detection can easily escalate to O(N^2) if no optimization is made, imposing an algorithmic bottleneck. A commonly-used technique is grid-based neighborhood search. By confining the search for collision-prone particles to a small area, we can reduce the computational complexity of collision detection back to O(N). This article takes a minimal 2D discrete element method (DEM) solver as an example and presents a highly efficient implementation of neighborhood search using Taichi's data structures.
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