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A Novel Memory-Optimized Approach for Large-scale Peridynamics on the GPU

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Abstract

Peridynamics is a robust theoretical framework for modelling a variety of phenomena in continuum mechanics. However, the same aspects of the peridynamic formulation that lend it versatility compound its computational complexity, and an efficient implementation of the method remains an open problem. Fortunately, peridynamics is very well-suited for parallelism, and the rapid improvement of GPU technology in recent years has provided new opportunities for acceleration. This work details an extension of the approach used by PeriPy, a recent bond-based GPU peridynamic modeller, to state-based peridynamics. Two additional methods are presented, a naive implementation based on prior work to serve as a comparison baseline, and a novel approach which aims to minimize GPU memory consumption and thereby maximize the problem size that can be executed on a given GPU. This novel memory-optimized method yields an acceleration of 3.6× relative to PeriPy, and a 10× increase in representable problem size. Additionally, results are included for a reduced version of this implementation using single-precision operations and removing damage calculations, which yields a further 63× speedup.

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Data Availability

The state-based adaptation of PeriPy can be found as a fork from the original PeriPy project at https://github.com/jd-bartlett96/PeriPy. The naive and memory-optimized CUDA implementations can be found at https://github.com/jd-bartlett96/peridynamics. The datasets generated and analyzed in this work are available from the corresponding author on reasonable request.

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Funding

The authors gratefully acknowledge financial support from the US Army Research Office under grant number W911NF-17-1-0595.

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  1. Mechanical Engineering Department, University of Washington, NE Campus Parkway, Seattle, 98195, WA, USA

    John Bartlett & Duane Storti

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  1. John Bartlett
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Correspondence to John Bartlett.

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Bartlett, J., Storti, D. A Novel Memory-Optimized Approach for Large-scale Peridynamics on the GPU. J Peridyn Nonlocal Model 5, 472–490 (2023). https://doi.org/10.1007/s42102-022-00088-z

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