TY - JOUR 
A1 - Martínez Frutos, Jesús
AU - Martínez Castejón, Pedro Jesús
AU - Herrero Pérez, David
T1 - Efficient topology optimization using GPU computing with multilevel granularity
Y1 - 2017
SN - 0965-9978
UR - http://hdl.handle.net/10317/7200
AB - This paper proposes a well-suited strategy for High Performance Computing (HPC) of density-based topology optimization using Graphics Processing Units (GPUs). Such a strategy takes advantage of Massively Parallel Processing (MPP) architectures to overcome the computationally demanding procedures of density-based topology design, both in terms of memory consumption
and processing time. This is done exploiting data locality and minimizing both memory consumption and data transfers. The proposed GPU instance makes use of different granularities for the topology optimization pipeline, which are selected to properly balance the workload between the threads exploiting the parallelization potential of massive parallel architectures. The performance of the fine-grained GPU instance of the solving stage is evaluated using two preconditioning techniques. The proposal is also compared with the classical CPU implementation for diverse topology optimization problems, including stiffness
maximization, heat sink design and compliant mechanism design.
KW - Mecánica de Medios Continuos y Teoría de Estructuras
KW - GPU computing
KW - Topology optimization
KW - Compliance
KW - Compliant mechanism
KW - Heat conduction
KW - 1210 Topología
LA - eng
PB - Elsevier Science
ER -