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International Conference on Communication Technology (ICCT)

作者： Xiang Zhang Naiyang Guan Zhigang Luo College of Computer National University of Defense Technology Changsha China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (纸本)9781467370066

Sparse coding has shown its great potential in learning image feature representation. Recent developed methods such as group sparse coding prefer discovering the group relationships among examples and have achieved the state-of-the-art results in image classification. However, they suffer from poor robustness shortcomings in practice. This paper proposes a robust weighted supervised sparse coding method (RWSSC) to address this deficiency. Particularly, RWSSC distinguishes different classes' contributions to the sparse coding by a novel weighting strategy meanwhile removes the out liers by imposing l1-regularization over the noisy entries. Benefitting from these strategies, RWSSC can effectively boost performance of sparse coding in image classification. Besides, we developed the block coordinate descent algorithm to optimize it, and proved its convergence. Experimental results of image classification on two popular datasets show that RWSSC outperforms the representative sparse coding methods in quantities.

关键词： Support vector machines Robustness Classification algorithms Dictionaries Manganese

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A low-latency fine-grained dynamic shared cache management scheme for chip multi-processor

A low-latency fine-grained dynamic shared cache management s...

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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Jinbo Xu Weixia Xu Zhengbin Pang College of Computer National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

In order to utilize the shared last-level cache (LLC) in chip multi-processors (CMP) more efficiently, the partitioning of LLC resources among all cores should have the characteristics of low-latency for access, fine granularity for migration and simple hardware complexity for implementation. This paper proposes a dynamic LLC management scheme to achieve these goals. The proposed scheme migrates cache resources among different cores at the granularity of cache blocks, instead of ways. The quantity of victim cache blocks that each victim core can migrate to other target cores are related to an eviction probability, which are calculated according to the performance goal. Then the victim cache blocks for a target core is chosen from the nearest victim core who has non-zero eviction probability by introducing innovate E-Table structure in CMP. The eviction probabilities are updated periodically. With the help of E-Tables, the proposal achieves low-latency accesses by always keeping the required cache blocks near to the target cores. And fine granularity is guaranteed by maintaining an eviction probability for each core. In addition, only little additional hardware changes to traditional cache structure is required. Simulation results suggest significant performance improvements from 6.8% to 22.7% over related works.

关键词： Hardware Resource management Probability distribution Proposals Complexity theory Simulation

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High-energy-density electron beam from interaction of two successive laser pulses with subcritical-density plasma

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Physical Review Accelerators and Beams 2016年第2期19卷 021301-021301页

作者： J. W. Wang W. Yu M. Y. Yu H. Xu J. J. Ju S. X. Luan M. Murakami M. Zepf S. Rykovanov Helmholtz Institute Jena Jena 07743 Germany State Key Laboratory of High Field Laser Physics Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai 201800 China Institute for Fusion Theory and Simulation and the Department of Physics Zhejiang University Hangzhou 310027 China Institute for Theoretical Physics I Ruhr University Bochum D-44780 Germany National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha 410073 China Institute of Laser Engineering Osaka University Osaka 565-0871 Japan Centre for Plasma Physics School of Mathematics and Physics Queen’s University Belfast Belfast BT7 1NN United Kingdom

It is shown by particle-in-cell simulations that a narrow electron beam with high energy and charge density can be generated in a subcritical-density plasma by two consecutive laser pulses. Although the first laser pulse dissipates rapidly, the second pulse can propagate for a long distance in the thin wake channel created by the first pulse and can further accelerate the preaccelerated electrons therein. Given that the second pulse also self-focuses, the resulting electron beam has a narrow waist and high charge and energy densities. Such beams are useful for enhancing the target-back space-charge field in target normal sheath acceleration of ions and bremsstrahlung sources, among others.

关键词： Plasma acceleration & new acceleration techniques

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Poster: Segmentation Based Online Performance Problem Diagnosis

Poster: Segmentation Based Online Performance Problem Diagno...

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International Conference on Software Engineering (ICSE)

作者： Jingwen Zhou Zhenbang Chen Ji Wang College of Computer National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781479919352

Currently, the performance problems of software systems gets more and more attentions. Among various diagnosis methods based on system traces, principal component analysis (PCA) based methods are widely used due to the high accuracy of the diagnosis results and requiring no specific domain knowledge. However, according to our experiments, we have validated several shortcomings existed in PCA-based methods, including requiring traces with a same call sequence, inefficiency when the traces are long, and missing performance problems. To cope with these issues, we introduce a segmentation based online diagnosis method in this poster.

关键词： Principal component analysis Software systems Accuracy Measurement Monitoring Computer architecture Conferences

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Comparison of heavy-ion induced SEU for D- and TMR-flip-flop designs in 65-nm bulk CMOS technology

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Science China(Information Sciences) 2014年第10期57卷 223-229页

作者： HE YiBai CHEN ShuMing School of Computer Science National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

Heavy ion experiments were performed on D flip-flop(DFF) and TMR flip-flop(TMRFF) fabricated in a 65-nm bulk CMOS process. The experiment results show that TMRFF has about 92% decrease in SEU crosssection compared to the standard DFF design in static test mode. In dynamic test mode, TMRFF shows much stronger frequency dependency than the DFF design, which reduces its advantage over DFF at higher operation frequency. At 160 MHz, the TMRFF is only 3.2× harder than the standard DFF. Such small improvement in the SEU performance of the TMR design may warrant reconsideration for its use in hardening design.

关键词： SEU flip-flop TMR heavy-ion frequency

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Transductive Convolutive Nonnegative Matrix Factorization for Speech Separation

Transductive Convolutive Nonnegative Matrix Factorization fo...

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International Conference on Computer Science and Network Technology

作者： Yaodan Mai Long Lan Naiyang Guan Xiang Zhang Zhigang Luo College of Computer National University of Defense Technology Changsha 410073 China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

ISBN: (纸本)9781467381741

Nonnegative matrix factorization (NMF) is an effective speech separation approach of extracting discriminative components of different speaker. However, traditional NMF focuses only on the additive combination of the components and ignores the dependencies of speeches. Convolutive NMF (CNMF) captures the dependencies of speeches by overlapping components and achieves better separation performance. NMF and CNMF learn dictionaries for speakers in the absence of mixture, and thus they are unable to get enough information to learn dictionaries accurately when testing speeches are available. To handle this problem, transductive NMF (TNMF) is proposed which simultaneously utilizes speech of each speaker and mixture to learn more meaningful features of speakers, and significantly boost speech separation. CNMF addresses the dependencies of speech signals while it ignores the positive effect of mixtures in learning dictionaries. TNMF emphasizes the transductive learning of dictionaries while it fails to consider dependencies of speeches. This paper proposes transductive convolutive NMF (TCNMF) to overcome the deficiencies of both CNMF and TNMF. Experimental results show that our method makes significant improvement compared to aforementioned NMFbased methods.

关键词： Nonnegative matrix factorization single-channel speech separation transductive convolutive dictionary learning speech separation Speech matrix decomposition Loudspeakers Dictionaries as Topic NMF protocol Nonnegative mixtures DEPENDENCE

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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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Classification of Tiangong-1 hyperspectral remote sensing image via contextual sparse coding

Classification of Tiangong-1 hyperspectral remote sensing im...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Qi Lv Yong Dou Xin Niu Jiaqing Xu Jinbo Xu School of Computer National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781467372220

The hyperspectral remote sensing is one of the frontier techniques in the remote sensing research fields. Applying the sparse coding model to the hyperspectral remote sensing image processing is a hot topic in hyperspectral information processing. To improve the accuracy of hyperspectral image classification, we propose a classification method based on the spatial-spectral join-t contextual sparse coding. Firstly, a dictionary is obtained by training using samples selected from the ground-truth reference data. Then, the sparse coefficients of each pixel are calculated based on the learned dictionary. Afterward, the sparse coefficients are input to the classifier and the final classification result is obtained. The visible and near-infrared hyperspectral remote sensing image collected by Tiangong-1 in Chaoyang District of Beijing is used to evaluate the performance of the proposed approach. Experimental results show that the proposed method yields the best classification performance with the overall accuracy of 95.74% and the Kappa coefficient of 0.9476 in comparison with other classification methods.

关键词： Hyperspectral image Remote sensing Sparse coding Sparse coding Classification Methods remote sensing hyperspectral remote sensing hyperspectral imagery Image classification

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Constrained Projective Non-negative Matrix Factorization for Semi-supervised Multi-label Learning

Constrained Projective Non-negative Matrix Factorization for...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Xiang Zhang Naiyang Guan Zhigang Luo Xuejun Yang College of Computer National University of Defense Technology Changsha China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China The State key Laboratory of High Performance Computing National University of Defense Technology Changsha China

This paper formulates multi-label learning as a constrained projective non-negative matrix factorization (CPNMF) problem which concentrates on a variant of the original projective NMF (PNMF) and explicitly introduces an auxiliary basis to learn the semantic subspace and boosts its discriminating ability by exploiting labeled and unlabeled examples together. Particularly, it propagates labels of the labeled examples to the unlabeled ones by enforcing coefficients of examples sharing identical semantic contents to be identical based on a hard constraint, i.e., embedding the class indicator of labeled examples into their coefficients. CPNMF preserves the geometrical structure of dataset via manifold regularization meanwhile captures the inherent structure of labels by using label correlations. We developed a multiplicative update rule (MUR) based algorithm to optimize CPNMF and proved its convergence. Experiments of image annotation on Corel dataset, text categorization on Rcv1v2 dataset, and text clustering on two popular text corpuses suggest the effectiveness of CPNMF.

关键词： Conferences

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MPISE: Symbolic Execution of MPI Programs

MPISE: Symbolic Execution of MPI Programs

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IEEE International Symposim on High Assurance Systems Engineering

作者： Xianjin Fu Zhenbang Chen Yufeng Zhang Chun Huang Wei Dong Ji Wang College of Computer National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781479981120

Message Passing Interfaces (MPI) plays an important role in parallel computing. Many parallel applications are implemented as MPI programs. The existing methods of bug detection for MPI programs have the shortage of providing both input and non-determinism coverage, leading to missed bugs. In this paper, we employ symbolic execution to ensure the input coverage, and propose an on-the-fly schedule algorithm to reduce the interleaving explorations for non-determinism coverage, while ensuring the soundness and completeness. We have implemented our approach as a tool, called MPISE, which can automatically detect the deadlock and runtime bugs in MPI programs. The results of the experiments on benchmark programs and real world MPI programs indicate that MPISE finds bugs effectively and efficiently. In addition, our tool also provides diagnostic information and replay mechanism to help understand bugs.

关键词： Computer bugs System recovery Switches Synchronization Runtime Testing Scheduling

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