A GPU parallel scheme for accelerating 2D and 3D peridynamics models. (October 2022)
- Record Type:
- Journal Article
- Title:
- A GPU parallel scheme for accelerating 2D and 3D peridynamics models. (October 2022)
- Main Title:
- A GPU parallel scheme for accelerating 2D and 3D peridynamics models
- Authors:
- Wang, Xiaoming
Wang, Qihang
An, Boyang
He, Qing
Wang, Ping
Wu, Jun - Abstract:
- Highlights: 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 . Abstract: 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. ToHighlights: 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 . Abstract: 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. … (more)
- Is Part Of:
- Theoretical and applied fracture mechanics. Volume 121(2022)
- Journal:
- Theoretical and applied fracture mechanics
- Issue:
- Volume 121(2022)
- Issue Display:
- Volume 121, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 121
- Issue:
- 2022
- Issue Sort Value:
- 2022-0121-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-10
- Subjects:
- Peridynamics -- GPU -- Parallel computing -- CUDA -- Crack -- Memory model
Fracture mechanics -- Periodicals
620.1126 - Journal URLs:
- http://www.sciencedirect.com/science/journal/01678442 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.tafmec.2022.103458 ↗
- Languages:
- English
- ISSNs:
- 0167-8442
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 8814.551850
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 23868.xml