用于数万亿自由度模拟的大规模并行多尺度FE2框架

C. Moulinec, G. Houzeaux, R. Borrell, Adria Quintanas Corominas, G. Oyarzun, Judicael Grasset, G. Giuntoli, M. Vázquez
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摘要

混合CPU和加速器超级计算机的出现为超大规模的多尺度模拟打开了大门。这种多尺度技术的一个例子是FE2方法,它被设计用来模拟材料变形,通过对材料特性进行更好的估计,这实际上减少了在宏观尺度上引入物理建模的需要,例如本构定律。采用有限元法对宏观尺度和微观尺度进行求解,微观尺度在宏观尺度网格的高斯点处进行求解。由于微观尺度的模拟不需要彼此的任何信息,因此是并发运行的,因此所述问题是令人尴尬的并行。因此,FE2方法直接受益于混合机器,宏观尺度在CPU上解决,而微观尺度则卸载到加速器上。以一个由不同材料制成的平板为例,说明了该方法的潜力。为了保证分布式存储机器上良好的负载平衡,采用空间填充曲线技术,根据板材的材料类型进行加权。在美国能源部橡树岭国家实验室高端机器Summit的多达2,048个节点(49,152个cpu和12,288个GPU)上进行了超过5万亿自由度的模拟,显示了框架组装部分的出色加速,其中微尺度是在GPU上使用CUDA进行计算。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Massively Parallel Multi-Scale FE2 Framework for Multi-Trillion Degrees of Freedom Simulations
The advent of hybrid CPU and accelerator supercomputers opens the door to extremely large multi-scale simulations. An example of such a multi-scale technique, the FE2 approach, has been designed to simulate material deformations, by getting a better estimation of the material properties, which, in effect, reduces the need to introduce physical modelling at macro-scale level, such as constitutive laws, for instance. Both macro- and micro-scales are solved using the Finite Element method, the micro-scale being resolved at the Gauss points of the macro-scale mesh. As the micro-scale simulations do not require any information from each other, and are thus run concurrently, the stated problem is embarrassingly parallel. The FE2 method therefore directly benefits from hybrid machines, the macro-scale being solved on CPU whereas the micro-scale is offloaded to accelerators. The case of a flat plate, made of different materials is used to illustrate the potential of the method. In order to ensure good load balance on distributed memory machines, weighting based on the type of materials the plate is made of is applied by means of a Space Filling Curve technique. Simulations have been carried out for over 5 trillions of degrees of freedom on up to 2,048 nodes (49,152 CPUs and 12,288 GPUs) of the US DOE Oak Ridge National Laboratory high-end machine, Summit, showing an excellent speed-up for the assembly part of the framework, where the micro-scale is computed on GPU using CUDA.
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