GPUTopOpt: GPU Topology Optimization in Julia for Large-Scale Structural Design

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Vadillo Morillas, Abraham; Meneses Alonso, Jesús; BUSTOS, ALEJANDRO; Gismeros Moreno, Raúl; Castejón Sisamón, Cristina, 2026, "GPUTopOpt: GPU Topology Optimization in Julia for Large-Scale Structural Design", https://doi.org/10.21950/R5MOML, e-cienciaDatos, V1

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Description	GPU-accelerated topology optimization pipeline written in Julia + CUDA, targeting large-scale 3D structural design problems on heterogeneous HPC clusters such as Madroño. The code implements the SIMP (Solid Isotropic Material with Penalization) formulation with Wang–Lazarov–Sigmund Heaviside projection, solved through a matrix-free multigrid-preconditioned conjugate gradient (PCG) elasticity solver. Design updates use the Method of Moving Asymptotes (MMA). An optional porous / local-volume infill extension based on the Wu–Aage–Sigmund p-norm formulation is included for lattice and bio-inspired structures (validated on a femur reconstruction case at ~77 M DOFs). The pipeline is fully voxel-based: STL design domains are automatically voxelized; results are exported as STL meshes ready for downstream FEM validation or additive manufacturing. CPU/GPU tiled assembly minimises GPU memory pressure, enabling problems that would otherwise exceed VRAM. Distributed as a self-contained 61 MB package (15 Julia source files, example cases, pre/post-processing tools, README, user guide and Madroño-specific deployment instructions). Forked and extended from the upstream FSI-Topology-Optimisation-Portable codebase, with the porous infill and multigrid scalability improvements developed for this work. Requirements: Julia 1.10+, NVIDIA GPU with CUDA 11+, gmsh on PATH.
Subject	Engineering
Keyword	Topology optimization, SIMP, Multigrid preconditioner, Conjugate gradient, GPU computing, CUDA, Julia programming language, Structural optimization, Porous infill, Lattice structures, Helmholtz density filter, Method of Moving Asymptotes, Matrix-free finite elements, High-performance computing, Additive manufacturing
Notes	Julia 1.10+ required. Dependencies pinned in Project.toml/Manifest.toml. GPU: NVIDIA CUDA 11.x+ (tested on RTX-class hardware). Two separate Julia environments: root + multigrid_local/. See CONTEXT.md (38 KB, repo root) for full pipeline documentation: SIMP, WLS Heaviside projection, MMA, multigrid PCG, porous infill extension. For Madroño deployment, see MADRONO_DEPLOYMENT.md inside the package.
License/Data Use Agreement	GNU GPL 3.0

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	GPUTopOpt_madrono_MAQLAB_2026_v01.zip ZIP Archive - 26,1 MB Published Jun 19, 2026 0 Downloads MD5: d9f05fae1425063a43784781459373a1 Self-contained Julia + CUDA topology optimization package (15 source files, examples, pre/post-processing tools, user guide and Madroño deployment instructions). Unzip and follow README.md for setup; USER_GUIDE.md walks through end-to-end workflow; MADRONO_DEPLOYMENT.md contains cluster-specific notes (SLURM scripts, module loading, paths). Code Code	Access File File Access Public Download Options ZIP Archive Download Metadata Data File Citation Download EndNote XML Download RIS Download BibTeX
	GPUTopOpt_madrono_MAQLAB_2026_v01_readme.txt Plain Text - 11,6 KB Published Jun 19, 2026 0 Downloads MD5: 36575a29b9ba4ca61e9a42f871792fcd Documentation	Preview "GPUTopOpt_madrono_MAQLAB_2026_v01_readme.txt" Access File File Access Public Download Options Plain Text Download Metadata Data File Citation Download EndNote XML Download RIS Download BibTeX Explore Options Read Text

Citation Metadata

Persistent Identifier	doi:10.21950/R5MOML
Publication Date	2026-06-19
Title	GPUTopOpt: GPU Topology Optimization in Julia for Large-Scale Structural Design
Author	https://ror.org/03ths8210https://orcid.org/0000-0001-9888-0373 https://ror.org/03ths82100000-0002-5824-5444 https://ror.org/02msb5n36https://orcid.org/0000-0001-7513-6058 https://ror.org/03ths8210https://orcid.org/0000-0003-0593-4489 https://ror.org/03ths82100000-0002-5823-7372
Point of Contact	Use email button above to contact. Vadillo Morillas, Abraham (Universidad Carlos III de Madrid)
Description	GPU-accelerated topology optimization pipeline written in Julia + CUDA, targeting large-scale 3D structural design problems on heterogeneous HPC clusters such as Madroño. The code implements the SIMP (Solid Isotropic Material with Penalization) formulation with Wang–Lazarov–Sigmund Heaviside projection, solved through a matrix-free multigrid-preconditioned conjugate gradient (PCG) elasticity solver. Design updates use the Method of Moving Asymptotes (MMA). An optional porous / local-volume infill extension based on the Wu–Aage–Sigmund p-norm formulation is included for lattice and bio-inspired structures (validated on a femur reconstruction case at ~77 M DOFs). The pipeline is fully voxel-based: STL design domains are automatically voxelized; results are exported as STL meshes ready for downstream FEM validation or additive manufacturing. CPU/GPU tiled assembly minimises GPU memory pressure, enabling problems that would otherwise exceed VRAM. Distributed as a self-contained 61 MB package (15 Julia source files, example cases, pre/post-processing tools, README, user guide and Madroño-specific deployment instructions). Forked and extended from the upstream FSI-Topology-Optimisation-Portable codebase, with the porous infill and multigrid scalability improvements developed for this work. Requirements: Julia 1.10+, NVIDIA GPU with CUDA 11+, gmsh on PATH.
Subject	Engineering
Keyword	Topology optimization SIMP Multigrid preconditioner Conjugate gradient GPU computing CUDA Julia programming language Structural optimization Porous infill Lattice structures Helmholtz density filter Method of Moving Asymptotes Matrix-free finite elements High-performance computing Additive manufacturing
Notes	Julia 1.10+ required. Dependencies pinned in Project.toml/Manifest.toml. GPU: NVIDIA CUDA 11.x+ (tested on RTX-class hardware). Two separate Julia environments: root + multigrid_local/. See CONTEXT.md (38 KB, repo root) for full pipeline documentation: SIMP, WLS Heaviside projection, MMA, multigrid PCG, porous infill extension. For Madroño deployment, see MADRONO_DEPLOYMENT.md inside the package.
Language	English
Producer	Vadillo, Abraham (Grupo MAQLAB, Departamento de Ingeniería Mecánica, Universidad Carlos III de Madrid) https://www.uc3m.es/ss/Satellite/GruposInvestigacion/es/Detalle/Organismo_C/1371210394776/1371325143833/MAQLAB:_Laboratorio_de_Maquinas
Funding Information	Agencia Estatal de Investigación (España): PID2024-160821OB-I00
Distribution Date	2026
Depositor	Vadillo Morillas, Abraham
Deposit Date	2026-06-19
Related Material	Wang, J., Aage, N., Wu, J., Sigmund, O., & Westermann, R. (2025). Efficient large-scale 3D topology optimization with matrix-free MATLAB code. Structural and Multidisciplinary Optimization, 68(9). https://doi.org/10.1007/s00158-025-04127-3; Wu, J., Aage, N., Westermann, R., & Sigmund, O. (2018). Infill Optimization for Additive Manufacturing—Approaching Bone-Like Porous Structures. IEEE Transactions on Visualization and Computer Graphics, 24(2), 1127–1140. https://doi.org/10.1109/tvcg.2017.2655523; Wang, F., Lazarov, B. S., & Sigmund, O. (2011). On projection methods, convergence and robust formulations in topology optimization. Structural and Multidisciplinary Optimization, 43(6), 767–784. https://doi.org/10.1007/s00158-010-0602-y
Related Dataset	Wang, J., Aage, N., Wu, J., Sigmund, O., & Westermann, R. (2025). Scalable 3D Topology Optimization Matrix-free MultiGrid Matlab code [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.15399096

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Main pipeline documentation: CONTEXT.md (repo root, 38 KB), covering: - SIMP topology optimization formulation - Heaviside projection (Wang-Lazarov-Sigmund) - MMA optimizer (Method of Moving Asymptotes) - Multigrid preconditioned conjugate gradient (PCG) solver - GPU tiled assembly - Porous / local-volume infill extension (Wu-Aage-Sigmund p-norm) Deployment-specific docs (inside madrono_package/): - README.md — quick start - USER_GUIDE.md — end-to-end workflow - MADRONO_DEPLOYMENT.md — cluster-specific notes (SLURM, env setup, paths) - examples/README.md — example case walkthroughs Validations and known limitations are listed in CONTEXT.md and inline in the relevant Julia source files (topopt.jl, run_cad.jl, multigrid_local/).

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