Efficient topology optimization using GPU computing with multilevel granularity

Martnez-Frutos, Jess; Martnez-Castejn, Pedro J.; Herrero-Prez, David

doi:10.1016/j.advengsoft.2017.01.009

Cited by 50 publications

(13 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This partitioning pattern is also demonstrated in Figure 6. The special treatment of halo data on the region boundary has been explored through several GPU computing methods, both the context of the global domain and for local computation [24,23,26]. We next discuss our global memory halo.…”

Section: Implementation 3: Fg Dbd With Memory Coalescingmentioning

confidence: 99%

Analysis of heterogeneous computing approaches to simulating heat transfer in heterogeneous material

Loeb

Earls

2019

Journal of Parallel and Distributed Computing

View full text Add to dashboard Cite

The simulation of heat flow through heterogeneous material is important for the design of structural and electronic components. Classical analytical solutions to the heat equation PDE are not known for many such domains, even those having simple geometries. The finite element method can provide approximations to a weak form continuum solution, with increasing accuracy as the number of degrees of freedom in the model increases. This comes at a cost of increased memory usage and computation time; even when taking advantage of sparse matrix techniques for the finite element system matrix. We summarize recent approaches in solving problems in structural mechanics and steady state heat conduction which do not require the explicit assembly of any system matrices, and adapt them to a method for solving the time-depended flow of heat. These approaches are highly parallelizable, and can be performed on graphical processing units (GPUs). Furthermore, they lend themselves to the simulation of heterogeneous material, with a minimum of added complexity. We present the mathematical framework of assembly-free FEM approaches, through which we summarize the benefits of GPU computation. We discuss our implementation using the OpenCL computing framework, and show how it is further adapted for use on multiple GPUs. We compare the performance of single and dual GPUs implementations of our method with previous GPU computing strategies from the literature and a CPU sparse matrix approach. The utility of the novel method is demonstrated through the solution of a real-world coefficient inverse problem that requires thousands of transient heat flow simulations, each of which involves solving a 1 million degree of freedom linear system over hundreds of time steps.

show abstract

Section: Implementation 3: Fg Dbd With Memory Coalescingmentioning

confidence: 99%

Analysis of heterogeneous computing approaches to simulating heat transfer in heterogeneous material

Loeb

Earls

2019

Journal of Parallel and Distributed Computing

View full text Add to dashboard Cite

show abstract

“…Recently, parallelization of topology optimization [103] in the FEM has received interest in the HPC community [104][105][106][107] for elasticity problems. Topology optimization is used to find an optimal design model for particular structures.…”

Section: Some Extraordinary Strategies Favourable To Parallelization mentioning

confidence: 99%

“…Over the last two years, Ram and Sharma [106] and Martínez-Frutos and Herrero-Pérez [107] have successfully exploited the computing power of GPUs to accelerate the topology optimization strategy. Martínez-Frutos et al [104] and Martínez-Frutos and Herrero-Pérez [107] adopted the FG-FEM to accelerate the topology optimization in structural analysis.…”

Section: Some Extraordinary Strategies Favourable To Parallelization mentioning

confidence: 99%

GPGPU-based parallel computing applied in the FEM using the conjugate gradient algorithm: a review

2018

View full text Add to dashboard Cite

Parallelization of the finite-element method (FEM) has been contemplated by the scientific and high-performance computing community for over a decade. Most of the computations in the FEM are related to linear algebra that includes matrix and vector computations. These operations have the single-instruction multiple-data (SIMD) computation pattern, which is beneficial for shared-memory parallel architectures. General-purpose graphics processing units (GPGPUs) have been effectively utilized for the parallelization of FEM computations ever since 2007. The solver step of the FEM is often carried out using conjugate gradient (CG)type iterative methods because of their larger convergence rates and greater opportunities for parallelization. Although the SIMD computation patterns in the FEM are intrinsic for GPU computing, there are some pitfalls, such as the underutilization of threads, uncoalesced memory access, lower arithmetic intensity, limited faster memories on GPUs and synchronizations. Nevertheless, FEM applications have been successfully deployed on GPUs over the last 10 years to achieve a significant performance improvement. This paper presents a comprehensive review of the parallel optimization strategies applied in each step of the FEM. The pitfalls and tradeoffs linked to each step in the FEM are also discussed in this paper. Furthermore, some extraordinary methods that exploit the tremendous amount of computing power of a GPU are also discussed. The proposed review is not limited to a single field of engineering. Rather, it is applicable to all fields of engineering and science in which FEM-based simulations are necessary.

show abstract

“…Efficient solution schemes and new methods, for example, multiscale finite element methods (Liu, H. et al 2018), multi-resolution TO (Nguyen et al 2012, Groen et al 2017, Liu, C. et al 2018) and others (Guo et al 2016, Li et al 2018, have been developed to reduce the computational cost. Strategies utilizing computer hardware such as parallel computing techniques (Aage et al 2017, Martínez-Frutos et al 2017 have also been proposed. A brief review of various methods and strategies can be found in (Liu, H. et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Accelerating gradient-based topology optimization design with dual-model artificial neural networks

Qian

2020

Struct Multidisc Optim

View full text Add to dashboard Cite

Topology optimization (TO) is a common technique used in free-form designs. However, conventional TO-based design approaches suffer from high computational cost due to the need for repetitive forward calculations and/or sensitivity analysis, which are typically done using high-dimensional simulations such as Finite Element Analysis (FEA). In this work, neural networks are used as efficient surrogate models for forward and sensitivity calculations in order to greatly accelerate the design process of topology optimization. To improve the accuracy of sensitivity analyses, dual-model neural networks that are trained with both forward and sensitivity data are constructed and are integrated into the Solid Isotropic Material with Penalization (SIMP) method to replace FEA. The performance of the accelerated SIMP method is demonstrated on two benchmark design problems namely minimum compliance design and metamaterial design. The efficiency gained in the problem with size of 64x64 is 137 times in forward calculation and 74 times in sensitivity analysis. In addition, effective data generation methods suitable for TO designs are investigated and developed, which lead to a great saving in training time. In both benchmark design problems, a design accuracy of 95% can be achieved with only around 2000 training data.

show abstract

Efficient topology optimization using GPU computing with multilevel granularity

Cited by 50 publications

References 55 publications

Analysis of heterogeneous computing approaches to simulating heat transfer in heterogeneous material

Analysis of heterogeneous computing approaches to simulating heat transfer in heterogeneous material

GPGPU-based parallel computing applied in the FEM using the conjugate gradient algorithm: a review

Accelerating gradient-based topology optimization design with dual-model artificial neural networks

Contact Info

Product

Resources

About