Multi-GPU acceleration of large-scale density-based topology optimization
Ver/
Compartir
Estadísticas
Ver Estadísticas de usoMetadatos
Mostrar el registro completo del ítemÁrea de conocimiento
Mecánica de Medios Continuos y Teoría de EstructurasPatrocinadores
This work has been supported by the AEI/FEDER and UE under the contract DPI2016-77538-R, and by the “Fundación Séneca – Agencia de Ciencia y Tecnología de la Región de Murcia” of Spain under the contract 20911/PI/18.Fecha de publicación
2021Editorial
ElsevierCita bibliográfica
Herrero-Pérez D, Castejón PJM (2021) Multi-GPU acceleration of large-scale density-based topology optimization. Advances in Engineering Software. 157-158. 103006. 10.1016/j.advengsoft.2021.103006.Palabras clave
Topology optimizationGPU computing
Multi-GPU systems
Finite element analysis
Aggregation AMG
Resumen
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 ...
Colecciones
- Artículos [1758]
El ítem tiene asociados los siguientes ficheros de licencia:
Redes sociales