Open Access

ARTICLE

Parallelized Implementation of the Finite Particle Method for Explicit Dynamics in GPU

Jingzhe Tang¹, Yanfeng Zheng¹, Chao Yang¹, Wei Wang¹, Yaozhi Luo^{1, *}

1 College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China.

* Corresponding Author: Yaozhi Luo. Email: .

(This article belongs to the Special Issue: Nonlinear Computational and Control Methods in Aerospace Engineering)

Computer Modeling in Engineering & Sciences 2020, 122(1), 5-31. https://doi.org/10.32604/cmes.2020.08104

Received 28 July 2019; Accepted 06 December 2019; Issue published 01 January 2020

Abstract

As a novel kind of particle method for explicit dynamics, the finite particle method (FPM) does not require the formation or solution of global matrices, and the evaluations of the element equivalent forces and particle displacements are decoupled in nature, thus making this method suitable for parallelization. The FPM also requires an acceleration strategy to overcome the heavy computational burden of its explicit framework for time-dependent dynamic analysis. To this end, a GPU-accelerated parallel strategy for the FPM is proposed in this paper. By taking advantage of the independence of each step of the FPM workflow, a generic parallelized computational framework for multiple types of analysis is established. Using the Compute Unified Device Architecture (CUDA), the GPU implementations of the main tasks of the FPM, such as evaluating and assembling the element equivalent forces and solving the kinematic equations for particles, are elaborated through careful thread management and memory optimization. Performance tests show that speedup ratios of 8, 25 and 48 are achieved for beams, hexahedral solids and triangular shells, respectively. For examples consisting of explicit dynamic analyses of shells and solids, comparisons with Abaqus using 1 to 8 CPU cores validate the accuracy of the results and demonstrate a maximum speed improvement of a factor of 11.2.

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Citations