@Article{cmes.2020.08104,
AUTHOR = {Jingzhe Tang, Yanfeng Zheng, Chao Yang, Wei Wang, Yaozhi Luo, *},
TITLE = {Parallelized Implementation of the Finite Particle Method for Explicit Dynamics in GPU},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {122},
YEAR = {2020},
NUMBER = {1},
PAGES = {5--31},
URL = {http://www.techscience.com/CMES/v122n1/38233},
ISSN = {1526-1506},
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.},
DOI = {10.32604/cmes.2020.08104}
}