PeriPy -- A High Performance OpenCL Peridynamics Package

TL;DR

PeriPy is a high-performance, open-source Python package utilizing OpenCL for scalable peridynamics simulations in solid mechanics, enabling faster analysis on CPU and GPU platforms for industrial applications.

Contribution

This work introduces PeriPy, a novel open-source peridynamics solver that significantly improves execution speed and scalability over existing GPU implementations.

Findings

Implementation is 1.4 to 10 times faster than existing solutions.

Demonstrates scalability on high-end GPUs like NVIDIA RTX 2080 Ti.

Effectively utilizes local GPU memory for performance gains.

Abstract

This paper presents a lightweight, open-source and high-performance python package for solving peridynamics problems in solid mechanics. The development of this solver is motivated by the need for fast analysis tools to achieve the large number of simulations required for `outer-loop' applications, including sensitivity analysis, uncertainty quantification and optimisation. Our python software toolbox utilises the heterogeneous nature of OpenCL so that it can be executed on any platform with CPU or GPU cores. We illustrate the package use through a range of industrially motivated examples, which should enable other researchers to build on and extend the solver for use in their own applications. Step improvements in execution speed and functionality over existing techniques are presented. A comparison between this solver and an existing OpenCL implementation in the literature is presented, tested on benchmarks with hundreds of thousands to tens of millions of nodes. We demonstrate the scalability of the solver on the GeForce RTX 2080 TiGPU from NVIDIA, and the memory-bound limitations are analysed. In all test cases, the implementation is between 1.4 and 10.0 times faster than a similar existing GPU implementation in the literature. In particular, this improvement has been achieved by utilising local memory on the GPU.