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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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Bidirectional Influence and Interaction for Multiagent Reinforcement Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 4984-4995页

作者： Sun, Shaoqi Xu, Kele Feng, Dawei Ding, Bo National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing Changsha410003 China

In recent years, multiagent reinforcement learning (MARL) has demonstrated considerable potential across diverse applications. However, in reinforcement learning environments characterized by sparse rewards, the scarcity of reward signals may give rise to reward conflicts among agents. In these scenarios, each agent tends to compete to obtain limited rewards, deviating from collaborative efforts aimed at achieving collective team objectives. This not only amplifies the learning challenge but also imposes constraints on the overall learning performance of agents, ultimately compromising the attainment of team goals. To mitigate the conflicting competition for rewards among agents in MARL, we introduce the bidirectional influence and interaction (BDII) MARL framework. This innovative approach draws inspiration from the collaborative ethos observed in human social cooperation, specifically the concept of "sharing joys and sorrows." The fundamental concept behind BDII is to empower agents to share their individual rewards with collaborators, fostering a cooperative rather than competitive behavioral paradigm. This strategic shift aims to resolve the pervasive issue of reward conflicts among agents operating in sparse-reward environments. BDII incorporates two key factors—namely, the Gaussian kernel distance between agents (physical distance) and policy diversity among agents (logical distance). The two factor collectively contribute to the dynamic adjustment of reward allocation coefficients, culminating in the formation of reward distribution weights. The incorporation of these weights facilitates the equitable sharing of agents’ contributions to rewards, promoting a cooperative learning environment. Through extensive experimental evaluations, we substantiate the efficacy of BDII in addressing the challenge of reward conflicts in MARL. Our research findings affirm that BDII significantly mitigates reward conflicts, ensuring that agents consistently align with the origi

关键词： Reinforcement learning

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Isolate Sets Based parallel Louvain Method for Community Detection

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Journal of Computer science & technology 2023年第2期38卷 373-390页

作者：郄航窦勇黄震熊运生 Science and Technology on Parallel and Distributed Laboratory School of Computer National University of Defense TechnologyChangsha 410073China

Community detection is a vital task in many fields,such as social networks and financial analysis,to name a *** Louvain method,the main workhorse of community detection,is a popular heuristic *** apply it to large-scale graph networks,researchers have proposed several parallel Louvain methods(PLMs),which suffer from two challenges:the latency in the information synchronization,and the community *** tackle these two challenges,we propose an isolate sets based parallel Louvain method(IPLM)and a fusion IPLM with the hashtables based Louvain method(FIPLM),which are based on a novel graph partition *** graph partition algorithm divides the graph network into subgraphs called isolate sets,in which the vertices are relatively decoupled from *** first describe the concepts and properties of the isolate *** we propose an algorithm to divide the graph network into isolate sets,which enjoys the same computation complexity as the breadth-first ***,we propose IPLM,which can efficiently calculate and update vertices information in parallel without latency or community ***,we achieve further acceleration by FIPLM,which maintains a high quality of community detection with a faster speedup than *** two methods are for shared-memory architecture,and we implement our methods on an 8-core PC;the experiments show that IPLM achieves a maximum speedup of 4.62x and outputs higher modularity(maximum 4.76%)than the serial Louvain method on 14 of 18 ***,FIPLM achieves a maximum speedup of 7.26x.

关键词： parallel computing isolate set graph partition Louvain method community detection

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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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A Heterogeneous KBA parallel Algorithm for the Cartesian Discrete Ordinates for Multizone Heterogeneous System 8

A Heterogeneous KBA Parallel Algorithm for the Cartesian Dis...

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8th International Conference on Computer and Communication Systems, ICCCS 2023

作者： Li, Runhua Liu, Jie National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory Changsha China

ISBN: (纸本)9781665456128

Innovations in powerful high-performance computing (HPC) architecture are enabling high-fidelity whole-core neutron transport simulations at reasonable time. Especially, the currently fashionable heterogeneous architectures make the cost of such simulations at very low level. Neutron distribution of a reactor core is governed by the Boltzmann neutron transport equation (BTE), first viable solutions of which need tremendous computer resources. Among of the high-fidelity numerical methods, the discrete ordinates method (SN) is becoming popular in the reaction design community by taking a good balance between computational cost and accuracy. Recently, MT-3000, which is a multizone heterogeneous architecture with a peak double precision performance of 11.6 TFLOPS, is proposed. In this work, the BTE is solved by the SN with heterogenous Koch-Baker-Alcouffe (KBA) parallel algorithms based on the MT-3000 architecture. A communication mechanism has been established to efficiently transmit data among the acceleration cores and the CPU cores. The kernel computation procedure is largely accelerated by the vectorization and instruction pipelining techniques. Numerical experiments show that our formulation could achieve 1.37 TFLOPs with single MT-3000, that is 11.8% of its peak performance. © 2023 IEEE.

关键词： parallel algorithms

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SIGNGD with Error Feedback Meets Lazily Aggregated Technique:Communication-Efficient Algorithms for distributed Learning

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Tsinghua science and technology 2022年第1期27卷 174-185页

作者： Xiaoge Deng Tao Sun Feng Liu Dongsheng Li National Laboratory for Parallel and Distributed Processing(PDL) College of ComputerNational University of Defense TechnologyChangsha 410073China

The proliferation of massive datasets has led to significant interests in distributed algorithms for solving large-scale machine learning ***,the communication overhead is a major bottleneck that hampers the scalability of distributed machine learning *** this paper,we design two communication-efficient algorithms for distributed learning *** first one is named EF-SIGNGD,in which we use the 1-bit(sign-based) gradient quantization method to save the communication ***,the error feedback technique,i.e.,incorporating the error made by the compression operator into the next step,is employed for the convergence *** second algorithm is called LE-SIGNGD,in which we introduce a well-designed lazy gradient aggregation rule to EF-SIGNGD that can detect the gradients with small changes and reuse the outdated ***-SIGNGD saves communication costs both in transmitted bits and communication ***,we show that LE-SIGNGD is convergent under some mild *** effectiveness of the two proposed algorithms is demonstrated through experiments on both real and synthetic data.

关键词： distributed learning communication-efficient algorithm convergence analysis

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An unsupervised deep learning framework for gene regulatory network inference from single-cell expression data

An unsupervised deep learning framework for gene regulatory ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Mao, Guo Liu, Jie National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory Changsha410073 China National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Laboratory of Software Engineering for Complex System Changsha410073 China

ISBN: (纸本)9798350337488

Recent advances in single-cell RNA sequencing (scRNA-seq) technology provides unprecedented opportunities for reconstruction gene regulation networks (GRNs). At present, many different models have been proposed to infer GRN from a large number of RNA-seq data, but most deep learning models use a priori gene regulatory network to infer potential GRNs. It is a challenge to reconstruct GRNs from scRNA-seq data due to the noise and sparsity introduced by the dropout effect. Here, we propose GAALink, a novel unsupervised deep learning method. It first constructs the gene similarity matrix and then refines it by threshold value. It then learns feature representations of genes through a graphical attention autoencoder that propagates information across genes with different weights. Finally, we use gene feature expression for matrix completion such that the GRNs are reconstructed. Compared with seven existing GRNs reconstruction methods, GAALink achieves more accurate performance on seven scRNA-seq dataset with four ground truth networks. GAALink can provide a useful tool for inferring GRNs for scRNA-seq expression data. © 2023 IEEE.

关键词： Feature representations Gene similarity matrix Graphical attention autoencoder Matrix completion Unsupervised deep learning method

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Smoothing Point Adjustment-Based Evaluation of Time Series Anomaly Detection 48

Smoothing Point Adjustment-Based Evaluation of Time Series A...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Liu, Mingyu Wang, Yijie Xu, Hongzuo Zhou, Xiaohui Li, Bin Wang, Yongjun National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory College of Computer Changsha China

ISBN: (纸本)9781728163277

Anomalies in time series appear consecutively, forming anomaly segments. Applying the classical point-based evaluation metrics to evaluate the detection performance of segments leads to considerable underestimation, so most related studies resort to point adjustment. This operation treats all points as true positives within a segment equally when only one individual point alarms, resulting in significant overestimation and creating an illusion of superior performance. This paper proposes smoothing point adjustment, a novel range-based evaluation protocol for time series anomaly detection. Our protocol reflects detection performance impartially by carefully considering the specific location and frequency of alarms in the raw results. It is achieved by smoothly determining the adjustment range and rewarding early detection via a ranging function and a rewarding function. Compared with other evaluation metrics, experiments on different datasets show that our protocol can yield a performance ranking of various methods more consistent with the desired situation. © 2023 IEEE.

关键词： Anomaly Detection Evaluation Protocol Point Adjustment Time Series

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Guided Spatio-Temporal Learning Method for 4K Video Super-Resolution 5

Guided Spatio-Temporal Learning Method for 4K Video Super-Re...

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5th ACM International Conference on Multimedia in Asia, MMAsia 2023

作者： Jiang, Qin Wang, Qinglin Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology China

ISBN: (纸本)9798400702051

4K Video Super-Resolution (VSR) presents a challenging task in video processing, as most existing VSR models have high computational complexity, limiting their application to high-resolution videos, particularly for 4K resolution videos. To address this issue, we propose a novel Guided Spatio-Temporal Video Super-Resolution network (GST-VSR) designed to perform 4K VSR on a single GPU. The proposed method comprises two key components: the Spatio-Temporal Alignment Network (STAN) and the Super-resolution Reconstruction Network (SRN), which work together to enhance the quality of the output frames. The STAN is responsible for extracting highly relevant features in frames and aligning the reference frame with the neighboring frames at the feature level to maintain temporal consistency. The SRN fuses high-quality features into the final high-resolution frames. Unlike existing methods, our proposed approach does not require explicit optical flow estimation, making it more efficient and less computationally demanding. To facilitate the training and testing of the compared models, we have established a new dataset, Pixabay-Set, consisting of 145 videos suitable for the 4K VSR task. Experimental results on the test dataset show that the proposed method achieves competitive performance compared to state-of-the-art models. In summary, our proposed GST-VSR network provides an effective solution to the challenging task of 4K VSR. © 2023 Copyright held by the owner/author(s).

关键词： Statistical tests

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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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