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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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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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DaCP: Accelerating Synchronization-Free SpTRSV via GPU-Friendly Data Communication and parallelism Strategies 20th

DaCP: Accelerating Synchronization-Free SpTRSV via GPU-Frie...

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20th IFIP WG 10.3 International Conference on Network and parallel Computing, NPC 2024

作者： Guo, Mingfeng Deng, Liang Dai, Zhe Li, Ruitian Lin, Gaofeng Liu, Jie Computational Aerodynamics Institute China Aerodynamics Research and Development Center Mianyang China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9789819628292

Sparse triangular solve (SpTRSV) is a vital component in various scientific applications, and numerous GPU-based SpTRSV algorithms have been proposed. Synchronization-free SpTRSV is currently the mainstream algorithm on GPU due to its short preprocessing time and outstanding performance. However, we observed that this algorithm still has two performance bottlenecks. Firstly, the thread-level parallel mode can introduce to thread divergence issues within GPU warps during the writing phase. Secondly, the thread-level and warp-level fusion mode may struggles to fully exploit GPU resources due to suboptimal mapping relationships between rows and threads. To address these issues, this paper proposes DaCPSpTRSV, a new synchronization-free algorithm with GPU-friendly data communication and parallelism strategies. Specifically, we first develop a fast-forward thread-level approach, incorporating an efficient global memory access pattern and a light-weight dependency control mechanism, to optimize data communication and alleviate thread divergence. A fine-grained fusion strategy is then proposed to maximize GPU parallelism by adaptively selecting the suitable thread-level or warp-level modes. Moreover, the commonly-used compressed sparse row (CSR) format is employed in our DaCPSpTRSV, enhancing the versatility of our algorithm. We evaluate our approach using 245 matrices from the SuiteSparse Matrix Collection on two NVIDIA GPUs, demonstrating speedup ratios of up to 4.77×, 4.94×, 1.67×, and 1.62× compared to cuSPARSE, Sync-Free, CapelliniSpTRSV, and YuenyeungSpTRSV, respectively. The project is open-sourced at https://***/gmfff12334/DaCP. © IFIP International Federation for Information processing 2025.

关键词： Graphics processing unit

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AFMA-Track: Adaptive Fusion of Motion and Appearance for Robust Multi-object Tracking 27th

AFMA-Track: Adaptive Fusion of Motion and Appearance for ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Liao, Wei Luo, Lei Zhang, Chunyuan College of Computer Science and Technology National University of Defence Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9783031784439

Motion and appearance cues play a crucial role in Multi-object Tracking (MOT) algorithms for associating objects across consecutive frames. While most MOT methods prioritize accurate motion modeling and distinctive appearance representations, the use of appearance and motion cues is often confined to simplistic association techniques. For instance, fixed weights are commonly employed to combine the intersection-over-union (IoU) matrix and appearance similarity matrix, yielding an association cost matrix. To harness the full potential of motion and appearance cues across diverse scenarios, we propose an innovative approach that dynamically balances motion and appearance cues based on scene and object information during the association process. Furthermore, we introduce a new mechanism for updating appearance representations, effectively mitigating noise introduced by occlusion. Our method demonstrates state-of-the-art performance on the MOT17 and MOT20 test sets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Object detection

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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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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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CoEdge: A Collaborative Architecture for Efficient Task Offloading among Multiple Edge Service Providers

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IEEE Internet of Things Journal 2025年

作者： Xie, Xingrui Cheng, Geyao Liu, Han Luo, Lailong Ren, Bangbang Guo, Deke National University of Defense Technology National Key Laboratory of Information Systems Engineering Hunan Changsha410073 China National University of Defense Technology State Key Laboratory of Complex and Critical Software Environment College of Information and Communication Hubei Wuhan430019 China National University of Defense Technology National Key Laboratory of Information Systems Engineering National Laboratory for Parallel and Distributed Processing Hunan Changsha410073 China

Edge computing is an emerging paradigm poised to process a substantial portion of latency-sensitive and computation-intensive tasks through edge service providers (ESPs). However, these ESPs typically operate independently and locally to serve their registered users. When processing burst tasks, the ESPs have to either scale up their respective capacities by introducing additional hardware or compromise user experience by rejecting some user requests, leading to high commercial investment or service degradation. Inspired by the promise of the win-win situation for ESPs and users, we envision a novel task offloading strategy that realizes the following rationales simultaneously: 1) collaborative service, 2) rapid response, and 3) sustainable profitability, while the existing methods fail to achieve them at one shot. To this end, we report CoEdge, a collaborative architecture for efficient task offloading among multiple ESPs in the edge network, aiming at simultaneously minimizing service delay for users and enhancing service profit for ESPs. To achieve this, CoEdge employs a central optimizer to implement a two-stage strategy that determines the task scheduling and service pricing hierarchically. We then formulate these problems and prove their NP-hardness. Additionally, we also propose efficient approximate algorithms to accommodate large-scale computing scenarios with low complexity. Experimental results using real-world datasets demonstrate that our CoEdge can significantly reduce service delay by 2.87x to 4.15x for users and considerably increase service profit by 32% for ESPs. © 2014 IEEE.

关键词： NP-hard

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A Unified Co-Processor Architecture for Matrix Decomposition

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Journal of Computer Science & Technology 2010年第4期25卷 874-885页

作者：窦勇周杰邬贵明姜晶菲雷元武倪时策 National Laboratory for Parallel & Distributed Processing National University of Defense Technology National Laboratory for Parallel & Distributed Processing.National University of Defense Technology

QR and LU decompositions are the most important matrix decomposition algorithms. Many studies work on accelerating these algorithms by FPGA or ASIC in a case by case style. In this paper, we propose a unified framework for the matrix decomposition algorithms, combining three QR decomposition algorithms and LU algorithm with pivoting into a unified linear array structure. The QR and LU decomposition algorithms exhibit the same two-level loop structure and the same data dependency. Utilizing the similarities in loop structure and data dependency of matrix decomposition, we unify a fine-grained algorithm for all four matrix decomposition algorithms. Furthermore, we present a unified co-processor structure with a scalable linear array of processing elements （PEs）, in which four types of PEs are same in the structure of memory channels and PE connections, but the only difference exists in the internal structure of data path. Our unified co-processor, which is IEEE 32-bit floating-point precision, is implemented and mapped onto a Xilinx Virtex5 FPGA chip. Experimental results show that our co-processors can achieve speedup of 2.3 to 14.9 factors compared to a Pentium Dual CPU with double SSE threads.

关键词： co-processor matrix decomposition fine-grained parallel FPGA

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Demand-Driven Memory Leak Detection Based on Flow-and Context-Sensitive Pointer Analysis

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Journal of Computer Science & Technology 2009年第2期24卷 347-356页

作者：王戟马晓东董威徐厚峰刘万伟 National Laboratory for Parallel and Distributed Processing National University of Defense Technology

We present a demand-driven approach to memory leak detection algorithm based on flow- and context-sensitive pointer analysis. The detection algorithm firstly assumes the presence of a memory leak at some program point and then runs a backward analysis to see if this assumption can be disproved. Our algorithm computes the memory abstraction of programs based on points-to graph resulting from flow- and context-sensitive pointer analysis. We have implemented the algorithm in the SUIF2 compiler infrastructure and used the implementation to analyze a set of C benchmark programs. The experimental results show that the approach has better precision with satisfied scalability as expected.

关键词： flow-sensitive memory leak detection demand-driven static analysis

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VirtMan:design and implementation of a fast booting system for homogeneous virtual machines in iVCE

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Frontiers of Information Technology & Electronic Engineering 2016年第2期17卷 110-121页

作者： Zi-yang LI Yi-ming ZHANG Dong-sheng LI Peng-fei ZHANG Xi-cheng LU National Laboratory for Parallel and Distributed Processing School of ComputerNational University of Defense Technology

Internet-based virtual computing environment （iVCE） has been proposed to combine data centers and other kinds of computing resources on the Internet to provide efficient and economical services. Virtual machines （VMs） have been widely used in iVCE to isolate different users/jobs and ensure trustworthiness, but traditionally VMs require a long period of time for booting, which cannot meet the requirement of iVCE＇s large-scale and highly dynamic applications. To address this problem, in this paper we design and implement VirtMan, a fast booting system for a large number of virtual machines in iVCE. VirtMan uses the Linux Small Computer System Interface （SCSI） target to remotely mount to the source image in a scalable hierarchy, and leverages the homogeneity of a set of VMs to transfer only necessary image data at runtime. We have implemented VirtMan both as a standalone system and for OpenStack. In our 100-server testbed, VirtMan boots up 1000 VMs （with a 15 CB image of Windows Server 2008） on 100 physical servers in less than 120 s, which is three orders of magnitude lower than current public clouds.

关键词： Virtual machine Fast booting Homogeneity Internet-based virtual computing environment （iVCE）

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