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IEEE Conference on Computer Communications Workshops

作者： Wanxin Zhang Dongsheng Li Ying Xu Yiming Zhang National Lab for Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9781467399562

Locality Sensitive Hashing (LSH) is an important indexing technique for approximate similarity search in high-dimensional spaces. An obvious limitation of LSH approaches is the lack of capability and scalability to deal with massive data. This paper proposes a distributed variant of LSH called Spark-LSH, which is implemented on Apache Spark, a well-known distributed computing framework. We design a shuffle-efficient indexing scheme for the Spark-LSH, which can reduce the data shuffle and improve the network efficiency when constructing the hash table indices. Furthermore, we propose a location-aware querying scheme to improve the query performance. Experiments show that the Spark-LSH scheme can reduce the network shuffle overhead remarkably and accelerate the query significantly.

关键词： Locality Sensitive Hashing Spark Shuffle

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Multi-representation knowledge distillation for audio classification

arXiv

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arXiv 2020年

作者： Gao, Liang Xu, Kele Wang, Huaimin Peng, Yuxing National Key Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha410073 China

As an important component of multimedia analysis tasks, audio classification aims to discriminate between different audio signal types and has received intensive attention due to its wide applications. Generally speaking, the raw signal can be transformed into various representations (such as Short Time Fourier Transform and Mel Frequency Cepstral Coefficients), and information implied in different representations can be complementary. Ensembling the models trained on different representations can greatly boost the classification performance, however, making inference using a large number of models is cumbersome and computationally expensive. In this paper, we propose a novel end-to-end collaborative learning framework for the audio classification task. The framework takes multiple representations as the input to train the models in parallel. The complementary information provided by different representations is shared by knowledge distillation. Consequently, the performance of each model can be significantly promoted without increasing the computational overhead in the inference stage. Extensive experimental results demonstrate that the proposed approach can improve the classification performance and achieve state-of-the-art results on both acoustic scene classification tasks and general audio tagging tasks. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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High Performance Graph Analytics with Productivity on Hybrid CPU-GPU Platforms

High Performance Graph Analytics with Productivity on Hybrid...

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2018年第二届高性能编译、计算和通信国际会议

作者： Haoduo Yang Huayou Su Qiang Lan Mei Wen Chunyuan Zhang Department of Computer National University of Defense Technology National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology

In recent years, the rapid-growing scales of graphs have sparked a lot of parallel graph analysis frameworks to leverage the massive hardware resources on CPUs or GPUs. Existing CPU implementations are time-consuming, while GPU implementations are restricted by the memory space and the complexity of programming. In this paper, we present a high performance hybrid CPU-GPU parallel graph analytics framework with good productivity based on GraphMat. We map vertex programs to generalized sparse matrix vector multiplication on GPUs to deliver high performance, and propose a high-level abstraction for developers to implement various graph algorithms with relatively little efforts. Meanwhile, several optimizations have been adopted for reducing the communication cost and leveraging hardware resources, especially the memory hierarchy. We evaluate the proposed framework on three graph primitives(PageRank, BFS and SSSP) with large-scale graphs. The experimental results show that, our implementation achieves an average speedup of 7.0 X than GraphMat on two 6-core Intel Xeon CPUs. It also has the capability to process larger datasets but achieves comparable performance than MapGraph, a state-of-theart GPU-based framework.

关键词： parallel Computing Graph Analytics Hybrid CPU-GPU

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Combining FFT and Spectral-Pooling for Efficient Convolution Neural Network Model

Combining FFT and Spectral-Pooling for Efficient Convolution...

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2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE2016)

作者： Zelong Wang Qiang Lan Dafei Huang Mei Wen Department of Compute National University of Technology Defense National Key Laboratory of Parallel and Distributed Processing National University of Defense Technology

ISBN: (纸本)9781510835368

Convolution operation is the most important and time consuming step in a convolution neural network *** this work,we analyze the computing complexity of direct convolution and fast-Fourier-transform-based(FFT-based) *** creatively propose CS-unit,which is equivalent to a combination of a convolutional layer and a pooling layer but more *** computing complexity of and some other similar operation is demonstrated,revealing an advantage on computation of ***,practical experiments are also performed and the result shows that CS-unit holds a real superiority on run time.

关键词： computing complexity FFT-based convolution CS-unit

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An effective control scheme to ensure real-time causal order in large-scale distributed interactive simulations

An effective control scheme to ensure real-time causal order...

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2011 IEEE 3rd International Conference on Communication Software and Networks, ICCSN 2011

作者： Zhou, Hangjun Fu, Sha Department of Information Management Hunan University of Finance and Economics Chang Sha Hu Nan China Key Laboratory of Science and Technology for National Defence of Parallel and Distributed Processing School of Computer Science National University of Defense Technology Chang Sha Hu Nan China

ISBN: (纸本)9781612844855

When a large-scale distributed interactive simulation system is running on WAN, the sites usually disperse over a wide area in geography, which results in the simulation clock of each site is hardly to be accurately synchronized with that of other sites. The asynchronous clocks and large transmission latency on WAN bring on a problem for the large-scale simulations to preserve the real-time causal order delivery of received events at each site. In this article, we analyze the indirect way to compare the values of asynchronous simulation clocks at first, and then propose a novel scheme which can select the reconstructible causal control information for each message so as to ensure the causal ordering of events in real time. Experiments demonstrate that the scheme can weaken the effect of network latency, reduce the overhead of the transmission amount of control information and improve the causal order consistency in asynchronous distributed simulations. © 2011 IEEE.

关键词： Real time control

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A causality based dynamic message ordering for DVEs on Wide Area Network

A causality based dynamic message ordering for DVEs on Wide ...

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IEEE International Conference on Communication Software and Networks

ISBN: (纸本)9781612844855

Due to the large message transmission latency in distributed Virtual Environments(DVEs) on Wide Area Net-work(WAN), the effectiveness of causality consistency control of message ordering is determined by not only causal order of messages but also the real-timeness. If merely causal order is considered, the real-time property of DVEs may not be ensured because of the unlimited waiting time for the delayed messages. While if only real-timeness is emphasized, there may be too many delayed messages, which have to be discarded, to maintain the quality of causal message ordering. Therefore, a trade-off between the quality of causal order delivery and real-timeness is necessary for DVEs. In this article, a novel causality based message ordering approach is presented. In general, this new approach dynamically balances the demands of causal order delivery and real-timeness. Experiment results demonstrate the approach can enhance the quality of causality, while simultaneously keep the real-time property of DVEs. © 2011 IEEE.

关键词： Wide area networks

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A multi-layered emulation platform to verify the effectiveness of consistency control approaches about message ordering

A multi-layered emulation platform to verify the effectivene...

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IEEE International Conference on Communication Software and Networks

ISBN: (纸本)9781612844855

It is an important issue to preserve the consistent delivering order of messages at each site in distributed Virtual Environments(DVEs). Currently, the violations of message delivery order are inclined to happen in the large-scale and frequently interactive DVEs running on Wide Area Network(WAN). The inconsistent anomalies are characterized as unpredictable, unrepeatable and uncontrollable, which results in the severe problem for the development of the consistency control approaches. Meanwhile, it is also highly expensive to directly verify the proposed approaches on WAN. Therefore, in this paper, we design and establish an original multi-layered emulation platform in LAN, which can emulate the network latency of WAN and repeat the scenarios of violations. On the platform, the designers of consistency approaches don't have to spend efforts to realize the fundamental functions of the verification, but could pay more attention to the development and optimization of the control algorithms, which is very beneficial for the improvement of message ordering approaches. © 2011 IEEE.

关键词： Wide area networks

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Research of Context Situation Awareness Technology

Research of Context Situation Awareness Technology

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IEEE International Symposium on Object/Component/Service-Oriented Real-Time distributed Computing Workshops (ISORCW)

作者： Dianxi Shi Zhendong Wu Bo Ding Huining Yan National Key Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha China

Context situation, which means a snapshot of the status of the real world, is formed by integrating a large amount of contexts collected from various resources. How to get the context situation and use the situation to provide better services is a challenging issue. In this paper, we focused on this challenge on the basis of the mobile cloud computing architecture. An abstract model is proposed in this paper to uniformly collect the context and send them to cloud. A rule-based large-scale context aggregation algorithm is also proposed which utilizes the MapReduce computing paradigm. Finally, a large-scale context management framework based on the abstract model and the context aggregation algorithm is proposed, and a real-time traffic demo is implemented to verify the validity of the framework.

关键词： Context Mobile handsets Real time systems Sensors Servers Mobile communication Context modeling

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Crowd intelligence in AI 2.0 era

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Frontiers of Information Technology & Electronic Engineering 2017年第1期18卷 15-43页

作者： Wei LI Wen-jun WU Huai-min WANG Xue-qi CHENG Hua-jun CHEN Zhi-hua ZHOU Rong DING State Key Laboratory of Software Development Beihang University National Laboratory for Parallel and Distributed Processing College of ComputerNational University of Defense Technology Institute of Computing Technology Chinese Academy of Sciences College of Computer Science and Technology Zhejiang University National Key Laboratory for Novel Software Technology Nanjing University

The Internet based cyber-physical world has profoundly changed the information environment for the development of artificial intelligence(AI), bringing a new wave of AI research and promoting it into the new era of AI 2.0. As one of the most prominent characteristics of research in AI 2.0 era, crowd intelligence has attracted much attention from both industry and research communities. Specifically, crowd intelligence provides a novel problem-solving paradigm through gathering the intelligence of crowds to address challenges. In particular, due to the rapid development of the sharing economy, crowd intelligence not only becomes a new approach to solving scientific challenges, but has also been integrated into all kinds of application scenarios in daily life, e.g., online-tooffline(O2O) application, real-time traffic monitoring, and logistics management. In this paper, we survey existing studies of crowd intelligence. First, we describe the concept of crowd intelligence, and explain its relationship to the existing related concepts, e.g., crowdsourcing and human computation. Then, we introduce four categories of representative crowd intelligence platforms. We summarize three core research problems and the state-of-the-art techniques of crowd intelligence. Finally, we discuss promising future research directions of crowd intelligence.

关键词： Crowd intelligence Artificial intelligence 2.0 Crowdsourcing Human computation

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Device View Redundancy: an adaptive low-overhead fault tolerance mechanism for many-core system

Device View Redundancy: an adaptive low-overhead fault toler...

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International Workshop on Intelligent Communication and Social Networks

作者： Wentao Jia Chunyuan Zhang Jian Fu National Key Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology Institute for Informatics University of Amsterdam

ISBN: (纸本)9781479909735

Continued increasing of fault rate in integrate circuit makes processors more susceptible to errors, especially many-core processor. Meanwhile, most systems or applications do not need full fault coverage, which has excessive overhead. So on-demand fault tolerance is desired for these applications. In this paper, we propose an adaptive low-overhead fault tolerance mechanism for many-core system, called Device View Redundancy (DVR). It treats fault tolerance as a device that can be configured and used by application when high reliability is needed. Nevertheless, DVR exploits the idle resources for low-overhead fault tolerance, which is based on the observation that the utilization of many-core system is low due to lack of parallelism in application. Finally, the experiment shows that the performance overhead of DVR is reduced by 16% to 98% compared with full Dual Modular Redundancy (DMR).

关键词： On-demand redundancy Idle resource exploitation Dynamic coupling Low-overhead Many core system

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