A parallel fast neighbor searching algorithm for particle-based methods on CPU and GPU architectures in multi-scale simulation.

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Bibliographic Details
Title:	A parallel fast neighbor searching algorithm for particle-based methods on CPU and GPU architectures in multi-scale simulation.
Authors:	DAI Chang-wei¹, KONG Rui-lin¹, JI Zhe^1,2,3 jizhe@nwpu.edu.cn
Source:	Computer Engineering & Science / Jisuanji Gongcheng yu Kexue. Aug2024, Vol. 46 Issue 8, p1349-1360. 12p.
Abstract:	Particle-based methods are widely applied in the resolving of complex multi-scale physical phenomena in various science and engineering areas. In order to handle the challenge of increasing computational complexity and declining concurrency for the pair-wised particle searching procedure in massive multi-scale particle-based simulations, a new parallel fast neighbor searching algorithm, which features high-concurrency and low memory footprint, is developed and demonstrated on both many-core CPU and GPU architectures. An inter-level interaction strategy based on the concept of hierarchical nested data structure is proposed to resolve the issue of racing condition in cross-level particle search. An asymmetric mapping method is developed to eliminate the full mapping of particles on each level, which reduces the memory consumption. A set of numerical experiments show that, the proposed algorithm can handle multi-scale problems with particle volume ratio up to 108. Compared with traditional algorithm, the proposed algorithm can achieve 2x-8x speedups and lower memory consumption. The GPUbased implementation of the algorithm achieves state-of-the-art computational efficiency. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	Particle-based methods are widely applied in the resolving of complex multi-scale physical phenomena in various science and engineering areas. In order to handle the challenge of increasing computational complexity and declining concurrency for the pair-wised particle searching procedure in massive multi-scale particle-based simulations, a new parallel fast neighbor searching algorithm, which features high-concurrency and low memory footprint, is developed and demonstrated on both many-core CPU and GPU architectures. An inter-level interaction strategy based on the concept of hierarchical nested data structure is proposed to resolve the issue of racing condition in cross-level particle search. An asymmetric mapping method is developed to eliminate the full mapping of particles on each level, which reduces the memory consumption. A set of numerical experiments show that, the proposed algorithm can handle multi-scale problems with particle volume ratio up to 108. Compared with traditional algorithm, the proposed algorithm can achieve 2x-8x speedups and lower memory consumption. The GPUbased implementation of the algorithm achieves state-of-the-art computational efficiency. [ABSTRACT FROM AUTHOR]
ISSN:	1007130X
DOI:	10.3969/j.issn.1007-130X.2024.08.003