The rapid development of parallelization technology over the recent decades has provided a promising avenue for the acceleration of meshfree simulation methods. One such method, peridynamics, is particularly well-suited for parallelization due to the simplicity of the operations which must occur at each material point. However, while MPI-based parallelization (Message-Passing Interface; a method for CPU-based parallelization) of peridynamic problems is commonplace, GPU parallelization of peridynamics has received far less attention. While GPU technology may have once been an inferior option to MPI parallelization for peridynamics, modern GPU cards are more than capable of handling substantially sized peridynamics problems. This paper presents the parallelization of the peridynamic method for single-card GPU computing, providing a schematic for a compact parallel approach. The resulting method is tested with CUDA on a NVIDIA Tesla P100 card with 16 GB of memory. The per-node memory requirements for each data structure used are evaluated, as well as the per-node execution times for each operation in a million-node benchmark test. This setup is shown to provide speedup factors over 200 for problems sized up to several million nodes, therefore indicating such a GPU is more than adequate for the single-card parallelization of the peridynamic method.

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