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• W4283211181 abstract "• A GPU parallel scheme is proposed to accelerate PD models while ensuring computational accuracy. • The bond-mapping unrolls the inner loop of the particle-mapping for stronger performance. • Three different types of PD numerical experiments are performed to analyze the performance of parallel codes. • Exploring the initiation and propagation behavior of fatigue cracks in rails with hole defects. • The code is hosted on Github https://github.com/XiaoMmingW/PD_Gpu1.git . Peridynamics (PD) is prevailing in the numerical simulations of damage evolution, but with the cost of far more required computations than traditional methods. This paper proposes a massively parallel implementation scheme for PD simulations with a single-card Graphics Processing Unit (GPU) to reduce the computational cost. The GPU parallel scheme includes two-level parallel modes of particle-mapping and bond-mapping, realizing high parallelism. By reasonably setting the data structure and using the CUDA memory model, realize the efficient utilization of GPU memory resources. Three numerical experiments involving quasi-static and transient problems, 2D and 3D problems, and impact problems are performed. The results show that the proposed parallel scheme can greatly improve the computational efficiency of various PD models while ensuring accuracy. The bond-mapping unrolls the inner loop of the particle-mapping and makes full use of registers and shared memory resources for stronger performance. To further explore the fatigue behavior of rails with hole defects using the GPU parallel scheme, which is difficult to perform using the serial and OpenMP scheme. The results show that the proposed GPU parallel scheme can explore more complex structural fracture problems by greatly reducing the computational cost." @default.
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• W4283211181 date "2022-10-01" @default.
• W4283211181 modified "2023-10-17" @default.
• W4283211181 title "A GPU parallel scheme for accelerating 2D and 3D peridynamics models" @default.
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• W4283211181 doi "https://doi.org/10.1016/j.tafmec.2022.103458" @default.
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