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An intelligent mesh-smoothing method with graph neural networks

Frontiers of Information Technology & Electronic Engineering 2025年第3期26卷 367-384页

作者： Zhichao WANG Xinhai CHEN Junjun YAN Jie LIU Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangsha 410073China Laboratory of Digitizing Software for Frontier Equipment National University of Defense TechnologyChangsha 410073China

In computational fluid dynamics(CFD),mesh-smoothing methods are widely used to refine the mesh quality for achieving high-precision numerical ***,optimization-based smoothing is used for high-quality mesh smoothing,but it incurs significant computational *** works have improved its smoothing efficiency by adopting supervised learning to learn smoothing methods from high-quality ***,they pose difficulties in smoothing the mesh nodes with varying degrees and require data augmentation to address the node input sequence ***,the required labeled high-quality meshes further limit the applicability of the proposed *** this paper,we present graph-based smoothing mesh net(GMSNet),a lightweight neural network model for intelligent mesh *** adopts graph neural networks(GNNs)to extract features of the node’s neighbors and outputs the optimal node *** smoothing,we also introduce a fault-tolerance mechanism to prevent GMSNet from generating negative volume *** a lightweight model,GMSNet can effectively smooth mesh nodes with varying degrees and remain unaffected by the order of input data.A novel loss function,MetricLoss,is developed to eliminate the need for high-quality meshes,which provides stable and rapid convergence during *** compare GMSNet with commonly used mesh-smoothing methods on two-dimensional(2D)triangle *** results show that GMSNet achieves outstanding mesh-smoothing performances with 5%of the model parameters compared to the previous model,but offers a speedup of 13.56 times over the optimization-based smoothing.

关键词： Unstructured mesh Mesh smoothing Graph neural network Optimization-based smoothing

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Componentization of Thermo-Fluid Coupling Simulation Based on LBM

Componentization of Thermo-Fluid Coupling Simulation Based o...

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Electrical Automation and Artificial Intelligence (ICEAAI), International Conference on

作者： Xue Chen Xuesong Wu Jianwen Cao Baoping Ye Parallel Software and Computational Science Laboratory Chinese Academy of Sciences Institute of Software Beijing China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

The solution of the collision-propagation equation plays an important role in the thermal-fluid multi-physics coupling simulation. Through the collision-propagation operation of different physical quantities, LBM can integrate physical fields such as fluid and thermal into the same framework to achieve coupling solution. In this paper, the collision-propagation equation is realized through LBM, and the simulation process is further componentized to improve the efficiency and reusability of the simulation. The specific component library includes: the main control module, geometric modeling of simulation field, simulation parameter setting, simulation computing, and visualization component. In LBM, since the particle motion on each lattice is independent, the componentized approach makes it easier to assign simulation tasks to multiple processors or compute nodes, thereby increasing computational efficiency.

关键词： Couplings Heating systems Visualization Program processors Fluid dynamics Geometric modeling Lattices Computer architecture Libraries computational efficiency

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Componentization of Thermo-Fluid Coupling Simulation Based on LBM

Componentization of Thermo-Fluid Coupling Simulation Based o...

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2025 International Conference on Electrical Automation and Artificial Intelligence, ICEAAI 2025

作者： Chen, Xue Wu, Xuesong Cao, Jianwen Ye, Baoping Institute of Software Parallel Software and Computational Science Laboratory Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798331506797

关键词： collision-propagation Componentization coupling LBM thermo-fluid

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FMCC-RT: a scalable and fine-grained all-reduce algorithm for large-scale SMP clusters

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science China(Information sciences) 2025年第5期68卷 362-379页

作者： Jintao PENG Jie LIU Jianbin FANG Min XIE Yi DAI Zhiquan LAI Bo YANG Chunye GONG Xinjun MAO Guo MAO Jie REN School of Computer Science and Technology National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology National Supercomputer Center in Tianjin School of Computer Science Shaanxi Normal University

All-reduce is a widely used communication technique for distributed and parallel applications typically implemented using either a tree-based or ring-based scheme. Each of these approaches has its own limitations: tree-based schemes struggle with efficiently exchanging large messages, while ring-based solutions assume constant communication throughput,an unrealistic expectation in modern network communication infrastructures. We present FMCC-RT, an all-reduce approach that combines the advantages of tree-and ring-based implementations while mitigating their drawbacks. FMCC-RT dynamically switches between tree and ring-based implementations depending on the size of the message being processed. It utilizes an analytical model to assess the impact of message sizes on the achieved throughput, enabling the derivation of optimal work partitioning parameters. Furthermore, FMCC-RT is designed with an Open MPI-compatible API, requiring no modification to user code. We evaluated FMCC-RT through micro-benchmarks and real-world application tests. Experimental results show that FMCC-RT outperforms state-of-the-art tree-and ring-based methods, achieving speedups of up to 5.6×.

关键词： all-reduce collective communication MPI scalability

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YFLM: An Improved Levenberg-Marquardt Algorithm for Global Bundle Adjustment 41st

YFLM: An Improved Levenberg-Marquardt Algorithm for Global ...

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41st Computer Graphics International Conference, CGI 2024

作者： Peng, Jiaxin Li, Tao Jiang, Qin Liu, Jie Wang, Ruibo Laboratory of Software Engineering for Complex Systems School of Computer Science National University of Defense Technology Hunan Changsha410073 China Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Hunan Changsha410073 China

ISBN: (纸本)9783031820205

The conventional Levenberg-Marquardt (LM) algorithm is a state-of-the-art trust-region optimization method for solving bundle adjustment problems in the Structure-from-Motion community, which not only takes advantage of the fast convergence of the Gauss-Newton method, but also the stability of the gradient descent method when approaching optimal solutions. However, the damping ratio of LM is simply provided by trial-and-error, which causes slow convergence rate for large-scale problems. This paper proposes the Yamashita-Fukushima LM (YFLM) algorithm to reduce the time complexity for global bundle adjustment, where the damping factor is determined by Yamashita and Fukushima’s method. YFLM dynamically calculates a more reasonable and optimal damping ratio according to the newest reprojection error. The experimental results show that the YFLM algorithm outperforms the conventional LM algorithm for most public bundle adjustment datasets. Besides this, the convergence of the YFLM algorithm is also evaluated with different σ∈(0,2]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Damping

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MARO: Enabling Full MPI Automatic Refactoring in DSL-Based Programming Framework 24th

MARO: Enabling Full MPI Automatic Refactoring in DSL-Based ...

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24th International Conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

作者： Lei, Tong Chen, Zongjing Che, Yonggang Xu, Chuanfu Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789819615445

Currently, the landscape of computer hardware architecture presents the characteristics of heterogeneity and diversity, prompting widespread attention to cross-platform portable parallel programming techniques. Most existing portable programming approaches adopt the "MPI+X" strategy, focusing mainly on node-level "X" parallelism, while inter-node parallelization still relies on manual MPI programming. OP2 is a domain-specific language-based portable parallel programming framework for unstructured mesh applications that supports the automatic generation of MPI parallel code. However, programmers must manually write pre-processing code during code refactoring, such as handling the mapping relationship between global and process-local variables and the initial distribution of process-local data. In this paper, we propose MARO (MPI Automatic Refactoring for OP2), an automatic refactoring method for generating pre-processing code targeting MPI code generation within the OP2 programming framework for unstructured mesh applications. This method automatically generates the pre-processing code required for MPI parallelism through the source-to-source translator, enabling OP2 to generate MPI parallel code without requiring users to write MPI-related code manually. We compare the automatically refactored OP2 applications using the proposed method with those manually refactored to validate its effectiveness. The proposed method enhances OP2’s automatic parallel code generation capability, enabling it to achieve fully automatic MPI parallelization. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Automatic programming

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Unveiling the role of high-order anharmonicity in thermal expansion: A first-principles perspective

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Chinese Physics B 2025年第4期34卷 212-219页

作者： Tianxu Zhang Kun Zhou Yingjian Li Chenhao Yi Muhammad Faizan Yuhao Fu Xinjiang Wang Lijun Zhang State Key Laboratory of Integrated Optoelectronics Key Laboratory of Automobile Materials of the Ministry of Education and College of Materials Science and EngineeringJilin UniversityChangchun 130012China Jiangxi Guanyi Grinding Co. Ltd.Fengxin 330700China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchun 130012China International Center of Computational Method and Software Jilin UniversityChangchun 130012China

Thermal expansion is crucial for various industrial processes and is increasingly the focus of research endeavors aimed at improving material ***,it is the continuous advancements in first-principles calculations that have enabled researchers to understand the microscopic origins of thermal *** this study,we propose a coefficient of thermal expansion(CTE)calculation scheme based on self-consistent phonon theory,incorporating the fourth-order *** selected four structures(Si,CaZrF_(6),SrTiO_(3),NaBr)to investigate high-order anharmonicity’s impact on their CTEs,based on bonding *** results indicate that our method goes beyond the second-order quasi-harmonic approximation and the third-order perturbation theory,aligning closely with experimental ***,we observed that an increase in the ionicity of the structures leads to a more pronounced influence of high-order anharmonicity on CTE,with this effect primarily manifesting in variations of the Grüneisen *** research provides a theoretical foundation for accurately predicting and regulating the thermal expansion behavior of materials.

关键词： high-order anharmonicity Grüneisen parameter thermal expansion first-principles calculations

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Going Beyond Passages: Readability Assessment for Book-Level Long Texts 23rd

Going Beyond Passages: Readability Assessment for Book-Leve...

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23rd China National Conference on computational Linguistics, CCL 2024

作者： Li, Wenbiao Sun, Rui Zhang, Tianyi Wu, Yunfang MOE Key Laboratory of Computational Linguistics Peking University Beijing China School of Software and Microelectronics Peking University Beijing China School of Computer Science Peking University Beijing China

ISBN: (纸本)9789819783663

Readability assessment for book-level long text is widely needed in real educational applications. However, most of the current researches focus on passage-level readability assessment and little work has been done to process ultra-long texts. In order to process the long sequence of book texts better and to enhance pretrained models with difficulty knowledge, we propose a novel model DSDR, difficulty-aware segment pre-training and difficulty multi-view representation. Specifically, we split all books into multiple fixed-length segments and employ unsupervised clustering to obtain difficulty-aware segments, which are used to re-train the pretrained model to learn difficulty knowledge. Accordingly, a long text is represented by averaging multiple vectors of segments with varying difficulty levels. We construct a new dataset of Graded Children’s Books to evaluate model performance. Our proposed model achieves promising results, outperforming both the traditional SVM classifier and several popular pretrained models. In addition, our work establishes a new prototype for book-level readability assessment, which provides an important benchmark for related research in future work. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Difficulty knowledge Readability Assessment Ultra-long texts processing

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Visual Acuity Consistent Foveated Rendering towards Retinal Resolution

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IEEE Transactions on Visualization and Computer Graphics 2025年 PP卷 PP页

作者： Zhang, Zhi Gai, Meng Li, Sheng Peking University School of Software and Microelectronics China Peking University School of Computer Science China National Key Laboratory of Intelligent Parallel Technology China

Prior foveated rendering methods often suffer from a limitation where the shading load escalates with increasing display resolution, leading to decreased efficiency, particularly when dealing with retinal-level resolutions. To tackle this challenge, we begin with the essence of the human visual system (HVS) perception and present visual acuity-consistent foveated rendering (VaFR), aiming to achieve exceptional rendering performance at retinal-level resolutions. Specifically, we propose a method with a novel log-polar mapping function derived from the human visual acuity model, which accommodates the natural bandwidth of the visual system. This mapping function and its associated shading rate guarantee a consistent output of rendering information, regardless of variations in the display resolution of the VR HMD. Consequently, our VaFR outperforms alternative methods, improving rendering speed while preserving perceptual visual quality, particularly when operating at retinal resolutions. We validate our approach using both the rasterization and ray-casting rendering pipelines. We also validate our approach using different binocular rendering strategies for HMD devices. In diverse testing scenarios, our approach delivers better perceptual visual quality than prior foveated rendering while achieving an impressive speedup of 6.5×-9.29× for deferred rendering of 3D scenarios and an even more powerful speedup of 10.4×-16.4× for ray-casting at retinal resolution. Additionally, our approach significantly enhances the rendering performance of binocular 8K path tracing, achieving smooth frame rates. © 1995-2012 IEEE.

关键词： Binoculars

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MeshONet: A Generalizable and Efficient Operator Learning Method for Structured Mesh Generation

arXiv

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arXiv 2025年

作者： Xiao, Jing Chen, Xinhai Peng, Jiaming Wang, Qingling Liu, Jie Laboratory of Digitizing Software for Frontier Equipment Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China

Mesh generation plays a crucial role in scientific computing. Traditional mesh generation methods, such as TFI and PDE-based methods, often struggle to achieve a balance between efficiency and mesh quality. To address this challenge, physics-informed intelligent learning methods have recently emerged, significantly improving generation efficiency while maintaining high mesh quality. However, physics-informed methods fail to generalize when applied to previously unseen geometries, as even small changes in the boundary shape necessitate burdensome retraining to adapt to new geometric variations. In this paper, we introduce MeshONet, the first generalizable intelligent learning method for structured mesh generation. The method transforms the mesh generation task into an operator learning problem with multiple input and solution functions. To effectively overcome the multivariable mapping restriction of operator learning methods, we propose a dual-branch, shared-trunk architecture to approximate the mapping between function spaces based on input-output pairs. Experimental results show that MeshONet achieves a speedup of up to four orders of magnitude in generation efficiency over traditional methods. It also enables generalization to different geometries without retraining, greatly enhancing the practicality of intelligent methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Mesh generation

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