Multi-GPU acceleration of large-scale density-based topology optimization. (July 2021)

• Record Type: Journal Article
• Authors: Herrero-Pérez, David; Martínez Castejón, Pedro J.
• Is Part Of: Advances in engineering software. Volume 157/158(2021)
• Publication Date: 2021-07
• Subjects: Topology optimization -- GPU computing -- Multi-GPU systems -- Finite element analysis -- Aggregation AMG; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2021.103006
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: Multi-GPU accelerated topology optimization using aggregation-based AMG. Low setup cost and device memory requirements using efficient interpolation. Multiple GPUs accelerate computing and increase the device memory available. Significant speedups in comparison with efficient multi-core computing. The use of mixed-precision increases performance. Abstract: This work presents a parallel implementation of density-based topology optimization using distributed GPU computing systems. The use of multiple GPU devices allows us accelerating the computing process and increasing the device memory available for GPU computing. This increment of device memory enables us to address large models that commonly do not fit into one GPU device. The most modern scientific computers incorporate these devices to design energy-efficient, low-cost, and high-computing power systems. However, we should adopt the proper techniques to take advantage of the computational resources of such high-performance many-core computing systems. It is well-known that the bottleneck of density-based topology optimization is the solving of the linear elasticity problem using Finite Element Analysis (FEA) during the topology optimization iterations. We solve the linear system of equations obtained from FEA using a distributed conjugate gradient solver preconditioned by a smooth aggregation-based algebraic multigrid (AMG) using GPU computing with multiple devices. The use of aggregation-based AMG reduces memory requirements and improves the efficiency of the interpolation operation. This fact is rewarding for GPU computing. We evaluate the performance and scalability of the distributed GPU system using structured and unstructured meshes. We also test the performance using different 3D finite elements and relaxing operators. Besides, we evaluate the use of numerical approaches to increase the topology optimization performance. Finally, we present a comparison between the many-core computing instance and one efficient multi-core implementation to highlight the advantages of using GPU computing in large-scale density-based topology optimization problems.