基于图像的大规模骨有限元建模的高性能多平台计算。
High performance multi-platform computing for large-scale image-based finite element modeling of bone.
机构信息
Department of Radiology, University of Calgary, Calgary, AB, Canada; McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, AB, Canada.
McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, AB, Canada.
出版信息
Comput Methods Programs Biomed. 2022 Oct;225:107051. doi: 10.1016/j.cmpb.2022.107051. Epub 2022 Jul 30.
BACKGROUND
Image-based finite element (FE) modeling of bone is a non-invasive method to estimate bone stiffness and strength. High-resolution imaging data as input allows for inclusion of bone microarchitecture but results in large amounts of data unsuitable for traditional FE solvers. Bone-specific mesh-free solvers have been developed over the past 20 years to improve on memory efficiency in simulated bone loading applications. The objective of this study was to provide linear performance benchmarking for a bone-specific, mesh-free solver (FAIM) using µCT and HR-pQCT image data on Mac, Linux, and Windows operating systems using both single- and multi-thread CPU and GPU processing.
METHODS
The focus is on the linear gradient-descent solver using standardized uniaxial loading of bone models from µCT, and first- and second-generation HR-pQCT scans of the radius and tibia. Convergence, speedup, memory, and batch performance tests were completed using CPUs and GPUs on three laboratory-based systems with Windows, Linux, and Mac operating systems.
RESULTS
Although varying by system and model size, time-per-iteration was as low as 0.03 s when an HR-pQCT-based radius model (6.45 million DOF) was solved with 3 GPUs. Strong scaling was achieved with GPU and CPU parallel processing, with strong parallel efficiencies when models were solved using 3 GPUs or ≤ 10 CPU threads. Errors in force, strain energy density, and Von Mises stress were as low as 0.1% when a convergence tolerance of 10 or smaller was used.
CONCLUSION
The results of this study indicate that to maximize computational efficiency and minimize model solution times using FAIM software under the standardized tested conditions using µCT, XCT1 and XCT2 HR-pQCT image data, convergence tolerance set to 10, and 10 threads or 2 GPUs are sufficient for efficient solution times. Less strict convergence tolerances will improve solution times but will introduce more error in the outcome measures.
背景
基于图像的有限元（FE）建模是一种非侵入性方法，可用于估计骨的刚度和强度。高分辨率成像数据作为输入，可以包含骨微观结构，但会导致大量不适合传统 FE 求解器的数据。在过去的 20 年中，已经开发出了针对骨骼的无网格求解器，以提高模拟骨骼加载应用中的内存效率。本研究的目的是使用 µCT 和 HR-pQCT 图像数据，在 Mac、Linux 和 Windows 操作系统上，通过单线程和多线程 CPU 和 GPU 处理，为特定于骨骼的无网格求解器（FAIM）提供线性性能基准测试。
方法
本研究的重点是使用标准的单轴加载，对 µCT 生成的骨模型，以及第一代和第二代 HR-pQCT 桡骨和胫骨扫描进行线性梯度下降求解。在三个实验室系统上，使用 CPU 和 GPU 完成了收敛性、加速比、内存和批处理性能测试，这三个系统分别运行 Windows、Linux 和 Mac 操作系统。
结果
尽管每个系统和模型大小不同，但当使用 3 个 GPU 求解基于 HR-pQCT 的桡骨模型（645 万个自由度）时，每次迭代的时间低至 0.03s。使用 GPU 和 CPU 并行处理实现了强缩放，当使用 3 个 GPU 或 ≤10 个 CPU 线程求解模型时，具有很强的并行效率。当使用 10 或更小的收敛容差时，力、应变能密度和 Von Mises 应力的误差低至 0.1%。
结论
本研究的结果表明，在使用 µCT、XCT1 和 XCT2 HR-pQCT 图像数据，在标准化测试条件下，使用 FAIM 软件最大化计算效率并最小化模型求解时间，收敛容差设置为 10，使用 10 个线程或 2 个 GPU 就足以实现高效的求解时间。更严格的收敛容差将提高求解时间，但会在结果测量中引入更多误差。
Zotero 插件