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.

翻译：本文展示了用于解决固体机械中近效动力学问题的轻量、开放源码和高性能保温套件。开发该求解器的动机是需要快速分析工具,以实现“外环”应用所需的大量模拟,包括敏感性分析、不确定性量化和优化。我们的 Python软件工具箱利用了OpenCL的多样化性质,以便能够用CPU或GPU核心在任何平台上执行。我们通过一系列具有工业动机的例子来说明该套件的用途,这些例子应该使其他研究人员能够利用和扩展求解器,供他们自己应用。介绍了对现有技术的执行速度和功能的逐步改进。介绍了该求解码器与文献中现有的OpenCL执行速度和功能的比较,以数十万至数千万个节点的基准测试。我们展示了从VIVDIA的GForce RTX 2080 TiGPU上解解解解的可扩缩性,并分析了内存限制。在所有测试案例中,实施该软件的实施速度在1.4至10.0倍之间。