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6th IEEE Information technology, Networking, Electronic and Automation Control Conference, ITNEC 2023

作者： Pang, Bo Fu, Yongquan Ren, Siyuan Jia, Yan Harbin Institute of Technology College of Computer Science and Technology Shenzhen China National University of Defense Technology National Key Laboratory for Parallel and Distributed Processing College of Computer Changsha China Peng Cheng Laboratory Shen Zhen China

ISBN: (纸本)9781665460033

Network traffic classification is crucial for network security and network management and is one of the most important network tasks. Current state-of-the-art traffic classifiers are based on deep learning models to automatically extract features from packet streams. Unfortunately, current approaches fail to effectively combine the structural information of traffic packets with the content features of the packets, resulting in limited classification accuracy. In this paper, we propose a graph neural network model for network traffic classification, which can well perceive the interaction feature of packets in traffic. Firstly, we design a graph structure for packets' flows to hold the interaction information between packets, which embeds both packet contents and sequence relationships into a unified graph. Secondly, we propose a graph neural network framework for graph classification to automatically learn the structural features of the packets' flows together with the packets' features. Extensive evaluation results on real-world traffic data show that the proposed model improves the prediction accuracy of improves the prediction accuracy by 2% to 37% for malicious traffic classification. © 2023 IEEE.

关键词： Graph neural networks

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Routing with uncertainty in wireless mesh networks

Routing with uncertainty in wireless mesh networks

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2010 IEEE 18th International Workshop on Quality of Service, IWQoS 2010

作者： Chen, Fajun Liu, Jiangchuan Li, Zongpeng Wang, Yijie Key Laboratory of Science and Technology for National Defence of Parallel and Distributed Processing School of Computer National University of Defense Technology China School of Computing Science Simon Fraser University Canada Department of Computer Science University of Caglary Canada

ISBN: (纸本)9781424459889

Existing routing protocols for Wireless Mesh Networks (WMNs) are generally optimized with statistical link measures, while not addressing on the intrinsic uncertainty of wireless links. We show evidence that, with the transient link uncertainties at PHY and MAC layers, a pseudo-deterministic routing protocol that relies on average or historic statistics can hardly explore the full potentials of a multi-hop wireless mesh. We study optimal WMN routing using probing-based online anypath forwarding, with explicit consideration of transient link uncertainties. We show the underlying connection between WMN routing and the classic Canadian Traveller Problem (CTP) [1]. Inspired by a stochastic recoverable version of CTP (SRCTP), we develop a practical SRCTP-based online routing algorithm under link uncertainties. We study how dynamic next hop selection can be done with low cost, and derive a systematic selection order for minimizing transmission delay. We conduct simulation studies to verify the effectiveness of the SRCTP algorithms under diverse network configurations. In particular, compared to deterministic routing, reduction of end-to-end delay (51:15∼73:02%) and improvement on packet delivery ratio (99:76%) are observed.

关键词： Routing protocols

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Inducing taxonomy from tags: An agglomerative hierarchical clustering framework

Inducing taxonomy from tags: An agglomerative hierarchical c...

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8th International Conference on Advanced Data Mining and Applications, ADMA 2012

作者： Li, Xiang Wang, Huaimin Yin, Gang Wang, Tao Yang, Cheng Yu, Yue Tang, Dengqing National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha China College of Mechatronics Engineering and Automation National University of Defense Technology Changsha China

ISBN: (纸本)9783642355264

By amassing 'wisdom of the crowd', social tagging systems draw more and more academic attention in interpreting Internet folk knowledge. In order to uncover their hidden semantics, several researches have attempted to induce an ontology-like taxonomy from tags. As far as we know, these methods all need to compute an overall or relative generality for each tag, which is difficult and error-prone. In this paper, we propose an agglomerative hierarchical clustering framework which relies only on how similar every two tags are. We enhance our framework by integrating it with a topic model to capture thematic correlations among tags. By experimenting on a designated online tagging system, we show that our method can disclose new semantic structures that supplement the output of previous approaches. Finally, we demonstrate the effectiveness of our method with quantitative evaluations. © Springer-Verlag Berlin Heidelberg 2012.

关键词： Taxonomies

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Optimizing Depthwise Convolutions on ARMv8 Architecture 23rd

Optimizing Depthwise Convolutions on ARMv8 Architecture

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23rd International Conference on parallel and distributed Computing, Applications, and Technologies, PDCAT 2022

作者： Hao, Ruochen Wang, Qinglin Yin, Shangfei Zhou, Tianyang Zhang, Qingyang Mei, Songzhu Shen, Siqi Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China College of Computer National University of Defense Technology Changsha410073 China Xiamen University Xiamen China

ISBN: (纸本)9783031299261

Depthwise convolutions are widely used in lightweight convolutional neural networks (CNNs). The performance of depthwise convolutions is mainly bounded by the memory access rather than the arithmetic operations for classic convolutions so that direct algorithms are often more efficient than indirect ones (matrix multiplication-, Winograd-, and FFT-based convolutions) with additional memory accesses. However, the existing direct implementations of depthwise convolutions on ARMv8 architectures feature a bad trade-off between register-level reuse of different tensors, which usually leads to sub-optimal performance. In this paper, we propose a new direct implementation of depthwise convolutions by means of implicit padding, register tiling, etc. Compared to the existing ones, our new implementations can incur much less communication overhead between registers and cache. Experimental results on two ARMv8 CPUs show that our implementation can averagely deliver 4.88 × performance improvement over the existing direct ones in open-source libraries. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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An irregular cutting based distributed mapping algorithm for DVE systems

An irregular cutting based distributed mapping algorithm for...

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2009 2nd Asia-Pacific Conference on Computational Intelligence and Industrial Applications, PACIIA 2009

作者： Zhang, Wei Zhou, Hangjun Peng, Yuxing Li, Sikun Xi, Min National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha 410073 China Department of Computer Science Xi'an Jiaotong University Xi'an 710049 China

ISBN: (纸本)9781424446070

distributed Virtual Environment (DVE) systems have become more and more important both in academic communities and the industries. To guarantee the load constrain, the physical world integrity and the virtual world integrity, the virtual environment should be divided into several parts and mapped to multiple servers, which are located in geographically different places. Many centralized algorithms based on regular cutting method are proposed to solve this problem. However, the dynamic property of clients and distributed property of the system make the existing algorithms unable to undertake it. In this paper, we propose a metric to measure the cutting quality and design a distributed mapping algorithm based on an irregular cutting. Simulation results show that our algorithm greatly decreases the costs of DVE systems for migration of clients and effectively improves the satisfaction degree of users. ©2009 IEEE.

关键词： Virtual reality

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Large-scale network topology generation for emulation environment

Large-scale network topology generation for emulation enviro...

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1st International Conference on Networking and distributed Computing, ICNDC 2010

作者： Kuang, Xiao-Hui Huang, Min-Huan Che, Yong-Gang Jin, Li Fei, Xu National Key Laboratory of Science and Technology on Information System Security Beijing Institute of System and Engineering Beijing China Department of Computer Science and Technology Tsinghua University Beijing China National Laboratory for Parallel and Distributed Process National University of Defense Technology Changsha China

ISBN: (纸本)9780769542072

Network emulation environment is great importance to the research of network protocols, applications and security mechanism. Large-scale network topology generation is one of key technologies to construct network emulation environment. In this paper, a new large-scale network topology generation framework called LSNTGF is proposed, which divides the network topology into three hierarchies: AS, router and terminal node. It contains the assignment mechanism to bandwidth of each link and IP address of each node. The framework is evaluate by implementation, and the result illustrate it is efficient to network emulation environment for network protocols and security research. © 2010 IEEE.

关键词： Network topology

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Fast flow field prediction of pollutant leakage diffusion based on deep learning

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Environmental science and Pollution Research 2024年第36期31卷 49393-49412页

作者： YunBo, Wan Zhong, Zhao Jie, Liu KuiJun, Zuo Yong, Zhang Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Computational Aerodynamics Institute China Aerodynamics Research and Development Center Mianyang621000 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China School of Aeronautics Northwestern Polytechnical University Xi’an710072 China

Abstract: Predicting pollutant leakage and diffusion processes is crucial for ensuring people’s safety. While the deep learning method offers high simulation efficiency and superior generalization, there is currently a lack of research on predicting pollutant leakage and diffusion flow field using deep learning. Therefore, it is necessary to conduct further studies in this area. This paper introduces a two-level network method to model the flow characteristics of pollutant diffusion. The proposed method in this study demonstrates a significant enhancement in flow field prediction accuracy compared to traditional deep learning methods. Moreover, it improves computational efficiency by over 800 times compared to traditional computational fluid dynamics (CFD) methods. Unlike conventional CFD methods that require grid expansion to calculate all operation conditions, the deep learning method is not confined by grid limitations. While deep learning methods may not entirely replace CFD methods, they can serve as a valuable supplementary tool, expanding the versatility of CFD methods. The findings of this research establish a robust foundation for incorporating deep learning methods in addressing pollutant leakage and diffusion challenges. Graphical abstract: A deep learning method for simulating pollutant leakage diffusion. (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Diffusion

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Adaptive Self-Supervised Continual Learning 26

Adaptive Self-Supervised Continual Learning

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Wu, Lilei Wang, Zhen Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China Tsinghua University Beijing100084 China

ISBN: (纸本)9781643684369

Continual Learning (CL) studies the problem of developing a robust model that can learn new tasks while retaining previously learned knowledge. However, the current CL methods exclusively focus on data with annotations, disregarding that unlabelled data is the mainstream in real-world applications. To close this research gap, this study concentrates on continual self-supervised learning, which is plagued by challenges of memory over-fitting and class imbalance. Besides, these challenges are exacerbated throughout incremental training. Aimed at addressing these challenges from both loss and data perspectives, we introduce a framework, Adaptive Self-supervised Continual Learning (ASCL). Specifically, we devise an Adaptive Sharpness-Aware Minimization (ASAM) module responsible for identifying flatter local minima in the loss landscape with a smaller memory over-fitting risk. Additionally, we design an Adaptive Memory Enhancement (AME) module responsible for rebalancing self-supervised loss with new and old tasks from a data perspective. Finally, the adaptive mechanisms in AME and ASAM modules dynamically adjust the loss landscape sharpness and memory enhancement strength with the feedback of intermediate training results. The results of our extensive experiments demonstrate the state-of-the-art performance of our methods in continual self-supervised learning scenarios across multiple datasets. © 2023 The Authors.

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Accelerating FDTD simulation of microwave pulse coupling into narrow slots on the Intel MIC architecture

Accelerating FDTD simulation of microwave pulse coupling int...

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IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, PACRIM 2015

作者： Wang, Qinglin Liu, Jie Cui, Xiantao Fu, Guitao Gong, Chunye Xing, Zuocheng Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China School of Computer Science National University of Defense Technology Changsha410073 China Beijing Satellite Navigation Center Beijing100094 China

ISBN: (纸本)9781467377881

The coupling of microwaves into apertures plays an important part in many electromagnetic physics and engineering fields. When the width of apertures is very small, Finite Difference Time Domain (FDTD) simulation of the coupling is very time-consuming. As a many-core architecture, the Intel's Many Integrated Core (MIC) architecture owns 512-bit vector units and more than 200 threads. In this paper, we parallelize FDTD simulation of microwave pulse coupling into narrow slots on the Intel MIC architecture. In the implementation, the parallel programming model OpenMP is used to exploit thread parallelism while loop unrolling and SIMD intrinsic functions are utilized to accomplish vectorization. Compared with the serial version on Intel Xeon E5-2670 CPU, the implementation on the MIC coprocessor including 57 cores obtains a speedup of 11.57 times. The experiment results also demonstrate that the parallelization has good scalability in performance. Additionally, how binding relationship between OpenMP threads and hardware threads in MIC influences performance is also reported. © 2015 IEEE.

关键词： Scalability

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Accelerating Embarrassingly parallel Algorithm on Intel MIC

Accelerating Embarrassingly Parallel Algorithm on Intel MIC

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2014 IEEE International Conference on Progress in Informatics and Computing

作者： Qinglin Wang Jie Liu XiantuoTang Feng Wang Guitao Fu Zuocheng Xing Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

The Embarrassingly parallel(EP) algorithm which is typical of many Monte Carloapplications provides an estimate of the upper achievable limits for double precision performance of parallel supercomputers. Recently, Intel released Many Integrated Core(MIC) architecture as a many-core co-processor. MIC often offers more than 50 cores each of which can run four hardware threads as well as 512-bit vector instructions. In this paper,we describe how the EP algorithm is accelerated effectively on the platforms containing MIC using the offload execution model. The result shows that the efficientimplementation of EP algorithm on MIC can take full advantage of MIC's computational resources and achieves a speedup of 3.06 compared with that on Intel Xeon E5-2670 CPU. Based on the EP algorithm on MIC and an effective task distribution model, the implementation of EP algorithm on a CPU-MIC heterogeneous platform achieves the performance of up to2134.86 Mop/s and 4.04 times speedup compared with that on Intel Xeon E5-2670 CPU.

关键词： NPB embarrassingly parallel algorithm heterogeneous platform many integrated core architecture

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