SIMP-Based Structural Topology Optimization Using Unstructured Mesh on GPU

Ratnakar, Shashi Kant; Sanfui, Subhajit; Sharma, Deepak

doi:10.1007/978-981-15-9956-9_1

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…Schmidt et al [33] used GPU to accelerate the SIMP method, and experimental results demonstrate that the parallel algorithm on the GeForce GTX280 runs faster than a 48-core shared memory central processing units (CPUs) system with a speed-up ratio of up to 60. Ratnakar et al [34] presented an implementation of topology optimization on the GPU for a 3D unstructured mesh by developing efficient and optimized GPU kernel functions. Karatarakis et al [35] proposed the interaction-wise approach for the parallel assembly of the stiffness matrix in IGA, which enables the efficient use of GPUs to substantially accelerate the computation.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Parallel Strategy for Isogeometric Topology Optimization via CPU/GPU Heterogeneous Computing

Xia,

Gao,

et al. 2024

CMES

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This paper aims to solve large-scale and complex isogeometric topology optimization problems that consume significant computational resources. A novel isogeometric topology optimization method with a hybrid parallel strategy of CPU/GPU is proposed, while the hybrid parallel strategies for stiffness matrix assembly, equation solving, sensitivity analysis, and design variable update are discussed in detail. To ensure the high efficiency of CPU/GPU computing, a workload balancing strategy is presented for optimally distributing the workload between CPU and GPU. To illustrate the advantages of the proposed method, three benchmark examples are tested to verify the hybrid parallel strategy in this paper. The results show that the efficiency of the hybrid method is faster than serial CPU and parallel GPU, while the speedups can be up to two orders of magnitude.

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Section: Introductionmentioning

confidence: 99%

A Hybrid Parallel Strategy for Isogeometric Topology Optimization via CPU/GPU Heterogeneous Computing

Xia,

Gao,

et al. 2024

CMES

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“…A speedup of 43 over the CPU was observed for a 3D L-beam example. Furthermore, an NbN À based matrix-free CG solver was presented by Ratnakar et al (2021b). A customized nodal connectivity storage format was used to reduce the threadglobal memory transactions.…”

Section: Introductionmentioning

confidence: 99%

“…A speedup of 4× over the CPU was observed for a 3D L-beam example. Furthermore, an NbN − based matrix-free CG solver was presented by Ratnakar et al. (2021b).…”

Section: Introductionmentioning

confidence: 99%

Acceleration of structural topology optimization using symmetric element-by-element strategy for unstructured meshes on GPU

Ratnakar

Kiran²,

Sharma³

2022

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PurposeStructural topology optimization is computationally expensive due to the involvement of high-resolution mesh and repetitive use of finite element analysis (FEA) for computing the structural response. Since FEA consumes most of the computational time in each optimization iteration, a novel GPU-based parallel strategy for FEA is presented and applied to the large-scale structural topology optimization of 3D continuum structures.Design/methodology/approachA matrix-free solver based on preconditioned conjugate gradient (PCG) method is proposed to minimize the computational time associated with solution of linear system of equations in FEA. The proposed solver uses an innovative strategy to utilize only symmetric half of elemental stiffness matrices for implementation of the element-by-element matrix-free solver on GPU.FindingsUsing solid isotropic material with penalization (SIMP) method, the proposed matrix-free solver is tested over three 3D structural optimization problems that are discretized using all hexahedral structured and unstructured meshes. Results show that the proposed strategy demonstrates 3.1× –3.3× speedup for the FEA solver stage and overall speedup of 2.9× –3.3× over the standard element-by-element strategy on the GPU. Moreover, the proposed strategy requires almost 1.8× less GPU memory than the standard element-by-element strategy.Originality/valueThe proposed GPU-based matrix-free element-by-element solver takes a more general approach to the symmetry concept than previous works. It stores only symmetric half of the elemental matrices in memory and performs matrix-free sparse matrix-vector multiplication (SpMV) without any inter-thread communication. A customized data storage format is also proposed to store and access only symmetric half of elemental stiffness matrices for coalesced read and write operations on GPU over the unstructured mesh.

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Graphics Processing Unit-Based Element-by-Element Strategies for Accelerating Topology Optimization of Three-Dimensional Continuum Structures Using Unstructured All-Hexahedral Mesh

Ratnakar

Sanfui

Sharma

2021

Journal of Computing and Information Science in Engineering

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Topology optimization has been successful in generating optimal topologies of various structures arising in real-world applications. Since these applications can have complex and large domains, topology optimization suffers from a high computational cost because of the use of unstructured meshes for discretization of these domains and their finite element analysis (FEA). This paper addresses this challenge by developing three GPU-based element-by-element strategies targeting unstructured all-hexahedral mesh for the matrix-free precondition conjugate gradient (PCG) finite element solver. These strategies mainly perform sparse matrix multiplication (SpMV) arising with the FEA solver by allocating more compute threads of GPU per element. Moreover, the strategies are developed to use shared memory of GPU for efficient memory transactions. The proposed strategies are tested with solid isotropic material with penalization (SIMP) method on four examples of 3D structural topology optimization. Results demonstrate that the proposed strategies achieve speedup up to 8.2× over the standard GPU-based SpMV strategies from the literature.

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SIMP-Based Structural Topology Optimization Using Unstructured Mesh on GPU

Cited by 3 publications

References 17 publications

A Hybrid Parallel Strategy for Isogeometric Topology Optimization via CPU/GPU Heterogeneous Computing

A Hybrid Parallel Strategy for Isogeometric Topology Optimization via CPU/GPU Heterogeneous Computing

Acceleration of structural topology optimization using symmetric element-by-element strategy for unstructured meshes on GPU

Graphics Processing Unit-Based Element-by-Element Strategies for Accelerating Topology Optimization of Three-Dimensional Continuum Structures Using Unstructured All-Hexahedral Mesh

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