.png){kind=icon}
返回
A high-performance backend-agnostic Material Point Method solver in Julia
.png){kind=icon}
OA
AI
DOI：10.1016/j.compgeo.2025.107189.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We develop a user-friendly, high-performance Material Point Method (MPM) solver, MaterialPointSolver.jl, using the Julia language. The dual storage and interaction between material particles and the background grid limits the use of high-resolution models, and few solutions offer both performance and ease of use. Our backend-agnostic solver leverages GPU computing, enabling high-performance implementations across various platforms with a single codebase. We propose an effective memory throughput approach to evaluate GPU efficiency. The accuracy and efficiency of the solver are validated by four numerical examples, showing a 3.5x speedup on a single-threaded CPU and a 19.4x speedup on a 20-threaded CPU compared to optimized vectorized MATLAB code. Among four tested GPUs, it achieves a maximum average memory bandwidth utilization of 67%, reaching up to 78% (1453 GiB/s) in 2D tests. On the GH200 platform, the GPU shows a 21.3x speedup over its multi-threaded CPU. Our solver can handle approximately 19.7 million double-precision material particles on a consumer-grade GPU with 10 GB memory, while on the GH200, it can manage around 190 million material particles. This study introduces advanced software capabilities along with backend-agnostic framework expertise to the field of geotechnical engineering. By utilizing parallel algorithms specifically designed for MPM and integrating a newly developed performance evaluation approach, it ensures rapid prototyping and seamless transition to production. This implementation empowers researchers and practitioners to promote the widespread adoption of high-resolution MPM models in geotechnical engineering.
Keyword:
MPM
Julia language
Heterogeneous computing
Effective memory throughput
期刊
IF:
6.2
论文数:
7.2K
被引数:
2.9W
机构
引用论文
暂无论文信息