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arXiv 2019年

作者： Geng, Mingyang Xu, Kele Li, Yiying Liu, Shuqi Ding, Bo Wang, Huaimin National Key Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha410073 China National Key Laboratory of Big Data Management and Analysis Northeastern University Shenyang110000 China

The aim of multi-agent reinforcement learning systems is to provide interacting agents with the ability to collaboratively learn and adapt to the behavior of other agents. In many real-world applications, the agents can only acquire a partial view of the world. However, in realistic settings, one or more agents that show arbitrarily faulty or malicious behavior may suffice to let the current coordination mechanisms fail. In this paper, we study a practical scenario considering the security issues in the presence of agents with arbitrarily faulty or malicious behavior. Under these circumstances, learning an optimal policy becomes particularly challenging, even in the unrealistic case that an agent's policy can be made conditional upon all other agents' observations. To overcome these difficulties, we present an Attention-based Fault-Tolerant (FT-Attn) algorithm which selects correct and relevant information for each agent at every time-step. The multi-head attention mechanism enables the agents to learn effective communication policies through experience concurrently to the action policies. Empirical results have shown that FT-Attn beats previous state-of-the-art methods in some complex environments and can adapt to various kinds of noisy environments without tuning the complexity of the algorithm. Furthermore, FT-Attn can effectively deal with the complex situation where an agent needs to reach multiple agents' correct observation at the same time. Copyright © 2019, The Authors. All rights reserved.

关键词： Reinforcement learning

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Optimizing Batched Small Matrix Multiplication on Multi-core DSP Architecture

Optimizing Batched Small Matrix Multiplication on Multi-core...

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International Symposium on parallel and distributed Processing with Applications, ISPA

作者： Xiaohan Zuo Chunhua Xiao Qinglin Wang Chen Shi College of Computer Science Chongqing University China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

General Matrix Multiplication (GEMM) is a critical computational operation in scientific computing and machine learning domains. While traditional GEMM performs well on large matrices, it is inefficient in terms of data transfer and computation for small matrices. Many High-Performance computing (HPC) tasks can be decomposed into large batches of small matrix multiplication operations. Multi-core Digital Signal Processors (DSPs) are commonly used to accelerate high-performance computing. We present a design for batched fusion small matrix multiplication (BFMM) tailored for multi-core DSP architecture. To address the inefficiencies and redundancy in storage and computational operations associated with batch small matrix multiplications, we designed several strategies. We design a matrix fusion concatenation strategy, an access coordination mechanism, and a mechanism for fragment aggregation. BFMM supports an efficient K-dimension multi-core parallelization strategy. The parameter constraint model makes BFMM highly portable. BFMM also includes a performance evaluation model that facilitates assessment and verification. Experimental results demonstrate that, compared to traditional GEMM (TGEMM) on multi-core DSP and traditional GEMM with concatenated data access (TGEMM Op), BFMM exhibits superior performance. For large batches of small matrices, our design achieves 1.21x to 18x higher performance than TGEMM Op on single-core DSP, while on multi-core DSP, it outperforms TGEMM Op by 1.14x to 18.1x.

关键词： Performance evaluation Scientific computing High performance computing Digital signal processors Redundancy Batch production systems Signal processing algorithms computer architecture Machine learning Matrix decomposition

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Towards a uniform architecture for the efficient implementation of 2D and 3D deconvolutional neural networks on FPGAS

arXiv

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arXiv 2019年

作者： Wang, Deguang Shen, Junzhong Wen, Mei Zhang, Chunyuan College of Computer National University of Defense Technology Changsha410073 China National Key Laboratory for Parallel Distributed ProcessingNational University of Defense Technology Changsha410073 China

Three-dimensional deconvolution is widely used in many computer vision applications. However, most previous works have only focused on accelerating 2D deconvolutional neural networks (DCNNs) on FPGAs, while the acceleration of 3D DCNNs has not been studied in depth as they have higher computational complexity and sparsity than 2D DCNNs. In this paper, we focus on the acceleration of both 2D and 3D DCNNs on FPGAs by proposing efficient schemes for mapping 2D and 3D DCNNs on a uniform architecture. By implementing our design on the Xilinx VC709 platform for four real-life 2D and 3D DCNNs, we can achieve up to 3.0 TOPS with high hardware efficiency. Comparisons with CPU and GPU solutions demonstrate that we can achieve an improvement of up to 63.3× in throughput relative to a CPU solution and an improvement of up to 8.3× in energy efficiency compared to a GPU solution. Copyright © 2019, The Authors. All rights reserved.

关键词： Field programmable gate arrays (FPGA)

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Server-uncertain spectrum trading in cognitive radio networks: A queueing-theoretic modeling approach

Server-uncertain spectrum trading in cognitive radio network...

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International Conference on Cognitive Radio Oriented Wireless Networks and Communications

作者： Lixia Liu Gang Hu Ming Xu Yuxing Peng National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Department of Network Engineering College of Computer National University of Defense Technology Changsha China

Spectrum trading is the promising method to improve spectrum efficiency from the perspective of economics. In this paper we propose a queueing-theory based spectrum trading model, where the primary user plays the server role providing spectrum to the secondary user who acts as the customer. The most significant challenge is how to optimize the spectrum trading model considering the server uncertainty which includes service state, service time, service area, service content and service price. We design a STACP queueing model according to the server attributes, so that the secondary user can choose the right queue quickly and reasonably according to its demand. Moreover, we further analyze the optimizing strategies for STACP model which can maximize the profit of the primary user and minimize the service cost of the secondary user. The simulation results demonstrate the analysis results.

关键词： Servers Uncertainty Loss measurement Queueing analysis Economics Analytical models Business

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RAFlow: Read Ahead Accelerated I/O Flow through Multiple Virtual Layers

RAFlow: Read Ahead Accelerated I/O Flow through Multiple Vir...

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International Conference on Networking, Architecture, and Storage (NAS)

作者： Zhaoning Zhang Kui Wu Huiba Li Jinghua Feng Yuxing Peng Xicheng Lu National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology China Computer Science Department University of Victoria B.C. Canada National Super Computing Center Tianjin China

Virtualization is the foundation for cloud computing, and the virtualization can not be achieved without software defined, elastic, flexible and scalable virtual layers. Unfortunately, if multiple virtual storage devices are chained together, the system may be subject to severe performance degradation. While the read-ahead (RA) mechanism in storage devices plays a very important role to improve I/O performance, RA may not be effective as expected for multiple virtualization layers, since it is originally designed for one layer only. When I/O requests are passed through a long I/O path, they may trigger a chain reaction and lead to unnecessary data transmission and thus bandwidth waste. In this paper, we study the dynamic behavior of RA through multiple I/O layers and demonstrate that if controlled well, RA can greatly accelerate I/O speed. We present RAFlow, a RA control mechanism, to effectively improve I/O performance by strategically expanding RA window at each layer. Our real-world experiments show that it can achieve 20% to 50% performance improvement in I/O paths with up to 8 virtualized storage devices.

关键词： Radio access networks Acceleration distributed databases Performance evaluation Synchronization Benchmark testing Degradation

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Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection

arXiv

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arXiv 2022年

作者： Xu, Hongzuo Wang, Yijie Jian, Songlei Liao, Qing Wang, Yongjun Pang, Guansong Beijing100091 China The National Key Laboratory of Parallel and Distributed Computing College of Computer National University of Defense Technology Hunan410073 China The College of Computer National University of Defense Technology Hunan410073 China Guangdong518055 China The School of Computing and Information Systems Singapore Management University Singapore178902 Singapore

Time series anomaly detection is instrumental in maintaining system availability in various domains. Current work in this research line mainly focuses on learning data normality deeply and comprehensively by devising advanced neural network structures and new reconstruction/prediction learning objectives. However, their one-class learning process can be misled by latent anomalies in training data (i.e., anomaly contamination) under the unsupervised paradigm. Their learning process also lacks knowledge about the anomalies. Consequently, they often learn a biased, inaccurate normality boundary. To tackle these problems, this paper proposes calibrated one-class classification for anomaly detection, realizing contamination-tolerant, anomaly-informed learning of data normality via uncertainty modeling-based calibration and native anomaly-based calibration. Specifically, our approach adaptively penalizes uncertain predictions to restrain irregular samples in anomaly contamination during optimization, while simultaneously encouraging confident predictions on regular samples to ensure effective normality learning. This largely alleviates the negative impact of anomaly contamination. Our approach also creates native anomaly examples via perturbation to simulate time series abnormal behaviors. Through discriminating these dummy anomalies, our one-class learning is further calibrated to form a more precise normality boundary. Extensive experiments on ten real-world datasets show that our model achieves substantial improvement over sixteen state-of-the-art contenders. © 2022, CC BY.

关键词： Contamination

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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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Routing with uncertainty in wireless mesh networks

Routing with uncertainty in wireless mesh networks

引用

International Workshop on Quality of Service

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

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). 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： Uncertainty Wireless mesh networks Delay Routing protocols Physical layer Statistics Stochastic processes Relays computer networks Concurrent computing

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Improved algorithm for reconstructing singular connection in multi-block CFD applications

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Transactions of Nanjing University of Aeronautics and Astronautics 2013年第SUPPL.1期30卷 51-57页

作者： Wang, Yongxian Zhang, Lilun Che, Yonggang Xu, Chuanfu Liu, Wei Liu, Huayong Wang, Zhenghua College of Computer National University of Defense Technology Changsha 410073 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha 410073 China State Key Laboratory of Aerodynamics Mianyang 621000 China

An improved algorithm is proposed for the reconstruction of singular connectivity from the available pairwise connections during preprocessing phase. To evaluate the performance of the algorithm, an in-house computational fluid dynamics (CFD) code is used in which high-order finite-difference method for spatial discretization running on the Tianhe-1A supercomputer is employed. Test cases with a varied amount of mesh points are chosen, and the test results indicate that the improved singular connection reconstruction algorithm can achieve a speedup of 2000× at least when compared with the naive search method adopted in the former version of our code. Moreover, the parallel efficiency can benefit from the strategy of local communication based on the algorithm.

关键词： Finite difference method

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RoSAS: Deep Semi-supervised Anomaly Detection with Contamination-resilient Continuous Supervision

arXiv

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arXiv 2023年

作者： Xu, Hongzuo Wang, Yijie Pang, Guansong Jian, Songlei Liu, Ning Wang, Yongjun National Key Laboratory of Parallel and Distributed Computing China College of Computer National University of Defense Technology Hunan Changsha410073 China School of Computing and Information Systems Singapore Management University Singapore178902 Singapore College of Information and Communications National University of Defense Technology Hubei Wuhan430010 China

Semi-supervised anomaly detection methods leverage a few anomaly examples to yield drastically improved performance compared to unsupervised models. However, they still suffer from two limitations: 1) unlabeled anomalies (i.e., anomaly contamination) may mislead the learning process when all the unlabeled data are employed as inliers for model training;2) only discrete supervision information (such as binary or ordinal data labels) is exploited, which leads to suboptimal learning of anomaly scores that essentially take on a continuous distribution. Therefore, this paper proposes a novel semi-supervised anomaly detection method, which devises contamination-resilient continuous supervisory signals. Specifically, we propose a mass interpolation method to diffuse the abnormality of labeled anomalies, thereby creating new data samples labeled with continuous abnormal degrees. Meanwhile, the contaminated area can be covered by new data samples generated via combinations of data with correct labels. A feature learning-based objective is added to serve as an optimization constraint to regularize the network and further enhance the robustness w.r.t. anomaly contamination. Extensive experiments on 11 real-world datasets show that our approach significantly outperforms state-of-the-art competitors by 20%-30% in AUC-PR and obtains more robust and superior performance in settings with different anomaly contamination levels and varying numbers of labeled anomalies. The source code is available at https://***/xuhongzuo/rosas/. © 2023, CC BY.

关键词： Contamination

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