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IEEE International Conference on Acoustics, Speech and Signal processing

作者： Bin Li Yijie Wang Mingyu Liu Kele Xu Zhongyang Wang Li Cheng Yizhou Li Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

Network anomaly detection is important for detecting and reacting to the presence of network attacks. In this paper, we propose a novel method to effectively leverage the features in detecting network anomalies, named FDEn, consisting of flow-based Feature Derivation (FD) and prior knowledge incorporated Ensemble models (En pk). To mine the effective information in features, 149 features are derived to enrich the feature set of the original data with covering more characteristics of network traffic. To leverage these features effectively, an ensemble model En pk, including CatBoost and XGBoost, based on the bagging strategy is proposed to first detect anomalies by combining numerical features and categorical features. And then, En pk adjusts the predicted label of specific data by incorporating the prior knowledge of network security. We conduct empirically experiments on the data set provided by the Network Anomaly Detection Challenge (NADC), in which we obtain average improvement up to 61.6%, 31.7%, 50.2%, and 45.0%, in terms of the cost score, precision, recall and F1-score, respectively.

关键词： Knowledge engineering Conferences Telecommunication traffic Signal processing Feature extraction Numerical models Security

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Co-designing the Topology/Algorithm to Accelerate distributed Training

Co-designing the Topology/Algorithm to Accelerate Distribute...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Xiang Hou Rui Xu Sheng Ma Qiong Wang Wei Jiang Hongyi Lu Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

With the development of Deep Learning (DL), Deep Neural Network (DNN) models have become more complex. At the same time, the development of the Internet makes it easy to obtain large data sets for DL training. Large-scale model parameters and training data enhance the level of AI by improving the accuracy of DNN models. But on the other hand, they also present more severe challenges to the hardware training platform because training a large model needs a lot of computing and memory resources that can easily exceed the capacity of a single processor. In this context, integrating more processors on a hierarchical system to conduct distributed training is a direction for the development of training platforms. In distributed training, collective communication operations (including all-to-all, all-reduce, and all-gather) take up a lot of training time, making the interconnection network between computing nodes one of the most critical factors affecting the system performance. The hierarchical torus topology, combined with the Ring All-Reduce collective communication algorithm, is one of the current mainstream distributed interconnection networks. However, we believe that its communication performance is not the best. In this work, we first designed a new intra-package communication topology, i.e. the switch-based fully connected topology, which shortens the time consumed by cross-node communication. Then, considering the characteristics of this topology, we carefully devised more efficient all-reduce and all-gather communication algorithms. Finally, combined with the torus topology, we implemented a novel distributed DL training platform. Compared with the hierarchical torus, our platform improves communication efficiency and provides 1.16-2.68 times speedup in distributed training of DNN models.

关键词： Training Deep learning Network topology Multiprocessor interconnection Computational modeling Training data Switches

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DELTA: Dynamically Optimizing GPU Memory beyond Tensor Recomputation

arXiv

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arXiv 2022年

作者： Tang, Yu Wang, Chenyu Zhang, Yufan Liu, Yuliang Zhang, Xingcheng Qiao, Linbo Lai, Zhiquan Li, Dongsheng Science and Technology on Parallel and Distributed Processing Laboratory United States SenseTime Research Shanghai Artificial Intelligence Laboratory China

Training activations of deep neural networks occupy plenty of GPU memory, especially for large-scale deep neuralnetworks. However, the further development of deep neural networks is hampered by the limited GPU memory resource. Therefore, the optimal utilization of GPU memory resources is highly demanded. Swapping and recomputation are commonly applied to make better use of GPU memory in deep learning. As an emerging domain, several dilemmas remain: 1) The efficiency of recomputation is limited and swapping between GPU and CPU costs severe time delay;2) There still lacks a dynamic runtime memory manager of tensor swapping and tensor recomputation nowadays;3) Manually decisions for activations of training deep neural network require professional priors and experience. To remedy the above issues, we propose a novel memory manager named DELTA (Dynamic tEnsor offLoading and recompuTAtion). To the best of our knowledge, we are the first to propose a reasonable dynamic runtime manager on the combination of tensor swapping and tensor recomputation without user oversight. In DELTA, we firstly propose a filter algorithm to select the optimal tensors to be released out of GPU memory and secondly present a director algorithm to select a proper action for each of these tensors. Furthermore, prefetching and overlapping are deliberately considered to overcome the time cost caused by swapping and recomputing tensors. Experimental results show that DELTA not only saves 40%-70% of GPU memory, surpassing the state-of-the-art method to a great extent, but also gets comparable convergence results as the baseline with acceptable time delay. Also, DELTA gains 2.04× maximum batchsize when training ResNet-50 and 2.25× when training ResNet-101 compared with the baseline. Besides, comparisons between the swapping cost and recomputation cost in our experiments demonstrate the importance of making a reasonable decision on tensor swapping and tensor recomputation, which refutes the arguments in

关键词： Tensors

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AUModel:A Conceptual model for Adaptive Software

AUModel:A Conceptual model for Adaptive Software

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2014 IEEE 5th International Conference on Software Engineering and Service science

作者： Hui Liu Bo Ding Dianxi Shi Huaimin Wang National Key Laboratory for Parallel and Distributed Processing College of Computer ScienceNational University of Defense Technology

Pervasive software should be able to adapt itself to the changing environments and user ***,it will bring great challenges to the software engineering *** paper proposes AUModel,a conceptual model for adaptive software,which takes adaptability as an inherent feature and can act as the foundation of the engineering *** introducing AUModel,the reuse of software adaptation infrastructure as well as the separation of adaptation concerns are enabled,which can facilitate both the development and maintenance of adaptive *** paper also presents our initial attempts to realize this model,including a middleware prototype to support this model and an application to validate its effectiveness.

关键词： pervasive computing adaptive software middleware

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Security issues and solutions in cloud computing

Security issues and solutions in cloud computing

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32nd IEEE International Conference on distributed Computing Systems Workshops, ICDCSW 2012

作者： You, Pengfei Peng, Yuxing Liu, Weidong Xue, Shoufu National Key Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha China Jiangsu Automation Research Institute Jiangsu China Air Defense Forces Academy CPLA Zhengzhou Henan China

ISBN: (纸本)9781467314237

Recently, Cloud computing, as one of the hottest words in IT world, has drawn great attention. Many IT companies such as IBM, Google, Amazon, Microsoft, Yahoo and others vigorously develop cloud computing systems and related products to customers. However, there are still some difficulties for customers to adopt cloud computing, in which many security issues exist, because data for a customer is stored and processed in cloud, not in a local machine. This paper briefly introduces cloud computing and its key concepts. In particularly, we intend to discuss security requirements and security issues involving data, application and virtualization in cloud computing, as well as current solutions to these issues. © 2012 IEEE.

关键词： Virtualization

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A Scalable and Flexible Monitoring System Framework for Supercomputers

A Scalable and Flexible Monitoring System Framework for Supe...

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2017 International Conference on Computer, Electronics and Communication Engineering(CECE2017)

作者： Tong XIAO Kai LU College of Computer National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

Mankind's demand for more powerful computing capabilities is never met, which has led to the continuous improvement of supercomputers' performance. A more powerful supercomputer tends to have a larger system scale, which brings serious challenges to the system management, within which how to monitor the system's state is a critical problem. To address this problem, a scalable and flexible monitoring system framework for supercomputers is brought forward in this paper which can monitor supercomputers with tens of thousands of nodes effectively and efficiently. In this paper, we firstly give an overview of the framework and then focus on the Super Computer System Description Language(SCSDL) which is key to the framework. In the end, we explain some techniques about implementing the framework, and the client GUIs of a job monitoring system and an error monitoring system for Tianhe-2 based on this framework are given, from which we can see that the framework is well scalable and flexible to monitor Tianhe-2 which has 16,000 nodes effectively and efficiently.

关键词： Monitoring system Framework Supercomputer Scalable Flexible

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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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2010 International Conference in Information science and Applications, ICISA 2010

作者： Fu, Hongyi Ding, Yan Key Laboratory of Science and Technology for National Defence of Parallel and Distributed Processing National University of Defence Tech. Changsha Hunan 410073 China School of Computer National University of Defence Tech. Changsha Hunan 410073 China

ISBN: (纸本)9781424459438

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. ©2010 IEEE.

关键词： Fault tolerance

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IWFR:Exploiting Full-duplex in Wireless Relay Networks

IWFR:Exploiting Full-duplex in Wireless Relay Networks

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2014 4th IEEE International Conference on Information science and technology

作者： Yong Lu Shaohe Lv Xiaodong Wang Xingming Zhou National Key Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology

Traditional wireless relay networks have large endto-end time delay and low throughput because of the limit that it can't receive and forward at the same *** this paper,we proposed IWFR:Immediate Wireless Full-Duplex Relay which exploits the advantages of full-duplex to shorten the end-to-end time delay and improve the *** the same time,we designed a new implicit acknowledgement mechanism,which can eliminate the ACK overheads and evidently improve the throughput of the *** implement IWFR,we also modified the full-duplex node architecture to make it support for immediate *** shows that IWFR shortens the end-to-end time delay by 60%on average and improves the throughput to 240%of the original relay.

关键词： Relay IWFR:Exploiting Full-duplex in Wireless Relay Networks

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Who should review this pull-request: Reviewer recommendation to expedite crowd collaboration 21

Who should review this pull-request: Reviewer recommendation...

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21st Asia-Pacific Software Engineering Conference, APSEC 2014

作者： Yu, Yue Wang, Huaimin Yin, Gang Ling, Charles X. National Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha410073 China Department of Computer Science University of Western Ontario LondonONN6A 5B7 Canada

ISBN: (纸本)9781479974252

Github facilitates the pull-request mechanism as an outstanding social coding paradigm by integrating with social media. The review process of pull-requests is a typical crowdsourcing job which needs to solicit opinions of the community. Recommending appropriate reviewers can reduce the time between the submission of a pull-request and the actual review of it. In this paper, we firstly extend the traditional Machine Learning (ML) based approach of bug triaging to reviewer recommendation. Furthermore, we analyze social relations between contributors and reviewers, and propose a novel approach to recommend highly relevant reviewers by mining comment networks (CN) of given projects. Finally, we demonstrate the effectiveness of these two approaches with quantitative evaluations. The results show that CN-based approach achieves a significant improvement over the ML-based approach, and on average it reaches a precision of 78% and 67% for top-1 and top-2 recommendation respectively, and a recall of 77% for top-10 recommendation. © 2014 IEEE.

关键词： Learning systems

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Adversarial Attack and Defense for Transductive Support Vector Machine

Adversarial Attack and Defense for Transductive Support Vect...

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International Joint Conference on Neural Networks (IJCNN)

作者： Li Liu Haiyan Chen Changchun Yin Liming Fang College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

As a classic semi-supervised approach, the Transductive Support Vector Machine (TSVM) has exhibited remarkable accuracy by utilizing unlabeled data. However, the robustness of TSVM against adversarial attacks remains a subject of investigation, prompting concerns about its reliability in security-critical applications. To unveil the vulnerability of TSVM, we introduce a finite-attack model specifically tailored to its characteristics, effectively manipulating its outputs. Additionally, we present Adversarial Defense-based TSVM (AD-TSVM), the first dedicated defense scheme designed for TSVM. AD-TSVM incorporates adversarial information into the optimization process, enhancing robustness by rebuilding a customized loss function and decision margin to counteract attacks. Rigorous experiments conducted on benchmark datasets demonstrate the effectiveness of AD-TSVM in significantly improving both the accuracy and stability of TSVM when confronted with adversarial attacks. This pioneering research assesses the weaknesses of TSVM and, more importantly, offers valuable insights and solutions for developing secure and trustworthy TSVM systems in the face of emerging threats.

关键词： Training Support vector machines Accuracy Machine learning algorithms Neural networks Robustness Vectors

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