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12th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2016

作者： Jiao, Xianlong Chen, Guirong Wang, Xiaodong Chen, Yuli Yang, Li Information and Navigation College Air Force Engineering University Xi'an710077 China College of Information System and Management National University of Defense Technology Changsha410073 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Chongqing Guanyinqiao Elementary School Chongqing400020 China Chongqing Liangjiangxinqu Renhe Experimental School Chongqing400021 China

ISBN: (纸本)9781509056965

This paper investigates the problem of maximizing uniform multicast throughput (MUMT) for multi-channel dense wireless sensor networks, where all nodes locate within one-hop transmission range and can communicate with each other on multiple orthogonal channels. This kind of networks show wide application in the real world, and maximizing uniform multicast throughput for these networks is worth deep studying. Previous researches have proved MUMT problem is NP-hard. However, previous researches are either hard to implement, or use too many relay nodes to complete the multicast task, and thus incur high overhead or poor performance. To efficiently solve MUMT problem, we adopt the concept of the maximum independent set with the size constraint, and present one novel Single-Broadcast based Multicast algorithm called SBM based on the concept. We prove that SBM algorithm achieves a constant ratio to the theoretical throughput upper bound. Extensive experimental results demonstrate that, SBM performs better than existing work in terms of both the uniform multicast throughput and the total number of transmissions. © 2016 IEEE.

关键词： Throughput

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Using Redundant Threads for Fault Tolerance of OpenMP Programs

Using Redundant Threads for Fault Tolerance of OpenMP Progra...

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International Conference on Information science and Applications (ICISA)

作者： Hongyi Fu Yan Ding Key Laboratory of Science and Technology for National Defence of Parallel and Distributed Processing National University of Defense Technology Changsha Hunan China Section #620 School of Computer National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9781424459421

As the wide application of multi-core processor architecture in the domain of high performance computing, fault tolerance for shared memory parallel programs becomes a hot spot of research. For years, checkpointing has been the dominant fault tolerance technology in this field, and recently, many research works have been engaged with it. However, to those programs which deal with large amount of data, checkpointing may induce massive I/O transfer, which will adversely affect scalability. To deal with such a problem, this paper proposes a fault tolerance approach, making use of redundancy, for shared memory parallel programs. Our scheme avoids saving and restoring computational state during the program's execution, hence does not involve I/O operations, so presents explicit advantage over checkpointing in scalability. In this paper, we introduce our approach and the related compiler tool in detail, and give the experimental evaluation result.

关键词： Fault tolerance Checkpointing Scalability Hardware Application software Multicore processing High performance computing Laboratories distributed processing

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Personalized Reputation Model in Cooperative distributed Systems

Personalized Reputation Model in Cooperative Distributed Sys...

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International Conference on parallel and distributed Systems (ICPADS)

作者： Wei Liu Yang-Bin Tang Huai-Min Wang School of Computer National University of Defense Technology Changsha Hunan China National Laboratory for Parallel and Distributed Processing Changsha Hunan China Institute of Science National University of Defense Technology Changsha Hunan China

Reputation systems provide a promising way to build trust relationships between users in distributed cooperation systems, such as file sharing, streaming, distributed computing and social network, through which a user can distinguish good services or users from malicious ones and cooperate with them. However, most reputation models mainly focus on evaluating the qualities of different services in one dimension, but care less about the preferences of different users. This paper proposes a personalized reputation model which provides each user a personalized trust view on others according to his preference. In our approach, we aggregate the users' preferences with collaborative filtering method and qualify it with user similarity which is integrated into the computing of reputation values. The experimental results suggest that our model can resist possible kinds of malicious behaviors efficiently.

关键词： Computational modeling Measurement Indexes Aggregates Resists Peer to peer computing Electronic mail

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DREAMS: Dynamic resource allocation for MapReduce with data skew

DREAMS: Dynamic resource allocation for MapReduce with data ...

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IFIP/IEEE International Symposium on Integrated Network Management

作者： Zhihong Liu Qi Zhang Mohamed Faten Zhani Raouf Boutaba Yaping Liu Zhenghu Gong College of Computer National University of Defense Technology Changsha China David R. Cheriton School of Computer Science University of Waterloo Waterloo ON Canada Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9781479982424

MapReduce has become a popular model for large-scale data processing in recent years. However, existing MapRe-duce schedulers still suffer from an issue known as partitioning skew, where the output of map tasks is unevenly distributed among reduce tasks. In this paper, we present DREAMS, a framework that provides run-time partitioning skew mitigation. Unlike previous approaches that try to balance the workload of reducers by repartitioning the intermediate data assigned to each reduce task, in DREAMS we cope with partitioning skew by adjusting task run-time resource allocation. We show that our approach allows DREAMS to eliminate the overhead of data repartitioning. Through experiments using both real and synthetic workloads running on a 11-node virtual virtualised Hadoop cluster, we show that DREAMS can effectively mitigate negative impact of partitioning skew, thereby improving job performance by up to 20.3%.

关键词： Resource management Containers Predictive models Mathematical model Monitoring Biomedical monitoring Yarn

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Sparse Matrix Reordering Method Selection with parallel Computing and Deep Learning

Sparse Matrix Reordering Method Selection with Parallel Comp...

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International Joint Conference on Neural Networks (IJCNN)

作者： Rui Xia Jihu Guo Huajian Zhang Shun Yang Qinglin Wang Jie Liu College of Computer Science and Techonology National University of Defense Technology Changsha China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Sparse matrix reordering is an important step in Cholesky decomposition. By reordering the rows and columns of the matrix, the time of computation and storage cost can be greatly reduced. With the proposal of various reordering algorithms, the selection of suitable reordering methods for various matrices has become an important research topic. In this paper, we propose a method to predict the optimal reordering method by visualizing sparse matrices in chunks in a parallel manner and feeding them into a deep convolutional neural network. The results show that the theoretical performance can reach 95% of the optimal performance, the prediction accuracy of the method can reach up to 85%, the parallel framework achieves an average speedup ratio of 11.35 times over the serial framework, and the performance is greatly improved compared with the traversal selection method on large sparse matrices.

关键词： Deep learning Visualization Accuracy Costs Neural networks parallel processing Prediction algorithms

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MilKBQA: A Complex Knowledge Base Question Answering Dataset on Chinese Military Field

MilKBQA: A Complex Knowledge Base Question Answering Dataset...

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IEEE International Conference on Data science in Cyberspace (DSC)

作者： Pike Xu Tiejun Li Zhen Huang Min Guo Jiezhong He Ding Sun Institute of Computer National University of Defense Technology Changsha China Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha China School of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665418164

Facts in military field tend to involve elements of time, space, quantity, status, and so on. Existing methods of representing knowledge in the form of triples fail to adequately express these facts, and also cause obstacles to knowledge storage and updating. Furthermore, question answering on these facts introduces new complexity dimension, which are complicated to be supported by existing corpus. Thus, we construct a Chinese knowledge base for military field covering entities and events centric knowledge, referred as MilKB. It consists of 965 entities and 3,017 facts. Moreover, we classify the natural questions into 26 types and construct a complex question answering dataset derived from MilKB, referred as MilKBQA. It consists of 2,829 question-answer pairs, in which 600 are event-centric ones. Experiments with three recent strong baseline models demonstrate that MilKBQA requires further research.

关键词： Triples (Data structure) Conferences Knowledge based systems Cyberspace Knowledge representation Data science Cognition

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HybridSwap: A scalable and synthetic framework for guest swapping on virtualization platform

HybridSwap: A scalable and synthetic framework for guest swa...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Pengfei Zhang Xi Li Rui Chu Huaimin Wang National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China School of Information Science and Engineering Central South University Changsha China

In IaaS cloud environments, peak memory demand caused by hotspot applications in Virtual Machine (VM) often results in performance degradation within and outside of this VM. Some solutions such as host swapping and ballooning for memory consolidation and overcommitment have been proposed. These solutions, however, have no help for addressing guest swapping issues inside VM. Even though host holds sufficient memory pages, guest OS is unable to utilize free pages in host directly due to the semantic gap between VMM and it. Our goal is to alleviate the performance degradation by decreasing disk I/O operations generated by guest swapping. Based on the insight analysis of behavioral features of guest swapping, we design HybridSwap, a distributed scalable framework which organize surplus memory in all hosts within data center into virtual pools for swapping. This framework builds up a synthetic swapping mechanism in a peer-to-peer way, which VM can adaptively choose suitable pools for swapping. We implement the prototype of HybridSwap and evaluate it with different benchmarks. The results demonstrate that our solution has the ability to promote the guest swapping efficiency indeed. Even in some cases, it shows 2-5 times of performance promotion compared with the baseline setup.

关键词： Benchmark testing Virtualization Semantics Servers Operating systems Degradation Instruction sets

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Towards Vision Transformer Unrolling Fixed-Point Algorithm: a Case Study on Image Restoration

arXiv

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arXiv 2023年

作者： Qiao, Peng Liu, Sidun Sun, Tao Yang, Ke Dou, Yong The Science and Technology on Parallel and Distributed Laboratory School of Computer National University of Defense Technology Changsha China National Innovation Institute of Defense Technology Beijing China

The great success of Deep Neural Networks (DNNs) has inspired the algorithmic development of DNN-based Fixed-Point (DNN-FP) for computer vision tasks. DNN-FP methods, trained by Back-Propagation Through Time or computing the inaccurate inversion of the Jacobian, suffer from inferior representation ability. Motivated by the representation power of the Transformer, we propose a framework to unroll the FP and approximate each unrolled process via Transformer blocks, called FPformer. To reduce the high consumption of memory and computation, we come up with FPRformer by sharing parameters between the successive blocks. We further design a module to adapt Anderson acceleration to FPRformer to enlarge the unrolled iterations and improve the performance, called FPAformer. In order to fully exploit the capability of the Transformer, we apply the proposed model to image restoration, using self-supervised pre-training and supervised fine-tuning. 161 tasks from 4 categories of image restoration problems are used in the pre-training phase. Hereafter, the pre-trained FPformer, FPRformer, and FPAformer are further fine-tuned for the comparison scenarios. Using self-supervised pre-training and supervised fine-tuning, the proposed FPformer, FPRformer, and FPAformer achieve competitive performance with state-of-the-art image restoration methods and better training efficiency. FPAformer employs only 29.82% parameters used in SwinIR models, and provides superior performance after fine-tuning. To train these comparison models, it takes only 26.9% time used for training SwinIR models. It provides a promising way to introduce the Transformer in low-level vision tasks. © 2023, CC BY.

关键词： Image reconstruction

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Translation Non-negative Matrix Factorization with Fast Optimization

Translation Non-negative Matrix Factorization with Fast Opti...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Yuanyuan Wang Naiyang Guan Bin Mao Xuhui Huang Zhigang Luo Department of Basic Courses Army Officer Academy Science and Technology on Parallel and Distributed Processing Laboratory College of Computer Science National University of Defense Technology College of Science National University of Defense Technology College of Computer Science National University of Defense Technology

ISBN: (纸本)9781479938414

Non-negative matrix factorization (NMF) reconstructs the original samples in a lower dimensional space and has been widely used in pattern recognition and data mining because it usually yields sparse representation. Since NMF leads to unsatisfactory reconstruction for the datasets that contain translations of large magnitude, it is required to develop translation NMF (TNMF) to first remove the translation and then conduct a decomposition. However, existing multiplicative update rule based algorithm for TNMF is not efficient enough. In this paper, we reformulate TNMF and show that it can be efficiently solved by using the state-of-the-art solvers such as NeNMF. Experimental results on face image datasets confirm both efficiency and effectiveness of the reformulated TNMF.

关键词： Non-negative matrix factorization (NMF) Translation transformation NeNMF

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High-performance Network Traffic Classification Based on Graph Neural Network

High-performance Network Traffic Classification Based on Gra...

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IEEE Information technology, Networking, Electronic and Automation Control Conference

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

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.

关键词： Deep learning Automation Telecommunication traffic Predictive models Network security Feature extraction Graph neural networks

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