A Subdomain-Based GPU Parallel Scheme for Accelerating Perdynamics Modeling with Reduced Graphics Memory

Zuokun Yang¹, Jun Li^1,2,*, Xin Lai^1,2, Lisheng Liu^1,2,*
1 Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics, Wuhan University of Technology, Wuhan, 430070, China
2 Hubei Longzhong Laboratory, Wuhan University of Technology Xiangyang Demonstration Zone, Xiangyang, 441000, China
* Corresponding Author: Jun Li. Email: email ; Lisheng Liu. Email: email

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2026.075980

Received 12 November 2025; Accepted 31 December 2025; Published online 19 January 2026

Abstract

Peridynamics (PD) demonstrates unique advantages in addressing fracture problems, however, its nonlocality and meshfree discretization result in high computational and storage costs. Moreover, in its engineering applications, the computational scale of classical GPU parallel schemes is often limited by the finite graphics memory of GPU devices. In the present study, we develop an efficient particle information management strategy based on the cell-linked list method and on this basis propose a subdomain-based GPU parallel scheme, which exhibits outstanding acceleration performance in specific compute kernels while significantly reducing graphics memory usage. Compared to the classical parallel scheme, the cell-linked list method facilitates efficient management of particle information within subdomains, enabling the proposed parallel scheme to effectively reduce graphics memory usage by optimizing the size and number of subdomains while significantly improving the speed of neighbor search. As demonstrated in PD examples, the proposed parallel scheme enhances the neighbor search efficiency dramatically and achieves a significant speedup relative to serial programs. For instance, without considering the time of data transmission, the proposed scheme achieves a remarkable speedup of nearly 1076.8× in one test case, due to its excellent computational efficiency in the neighbor search. Additionally, for 2D and 3D PD models with tens of millions of particles, the graphics memory usage can be reduced up to 83.6% and 85.9%, respectively. Therefore, this subdomain-based GPU parallel scheme effectively avoids graphics memory shortages while significantly improving the computational efficiency, providing new insights into studying more complex large-scale problems.