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

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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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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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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Two-dimensional euler PCA for face recognition 21

Two-dimensional euler PCA for face recognition

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21st International Conference on MultiMedia Modeling, MMM 2015

作者： Tan, Huibin Zhang, Xiang Guan, Naiyang Tao, Dacheng Huang, Xuhui Luo, Zhigang Science and Technology on Parallel Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha Hunan410073 China Department of Computer Science and Technology College of Computer National University of Defense Technology Changsha Hunan410073 China Centre for Quantum Computation and Intelligent Systems and the Faculty of Engineering and Information Technology University of Technology Sydney 235 Jones Street UltimoNSW2007 Australia

ISBN: (纸本)9783319144412

Principal component analysis (PCA) projects data on the directions with maximal variances. Since PCA is quite effective in dimension reduction, it has been widely used in computer vision. However, conventional PCA suffers from following deficiencies: 1) it spends much computational costs to handle high-dimensional data, and 2) it cannot reveal the nonlinear relationship among different features of data. To overcome these deficiencies, this paper proposes an efficient two-dimensional Euler PCA (2D-ePCA) algorithm. Particularly, 2D-ePCA learns projection matrix on the 2D pixel matrix of each image without reshaping it into 1D long vector, and uncovers nonlinear relationships among features by mapping data onto complex representation. Since such 2D complex representation induces much smaller kernel matrix and principal subspaces, 2D-ePCA costs much less computational overheads than Euler PCA on large-scale dataset. Experimental results on popular face datasets show that 2D-ePCA outperforms the representative algorithms in terms of accuracy, computational overhead, and robustness. © Springer International Publishing Switzerland 2015.

关键词： Principal component analysis

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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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Non-negative low-rank and group-sparse matrix factorization 21

Non-negative low-rank and group-sparse matrix factorization

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21st International Conference on MultiMedia Modeling, MMM 2015

作者： Wu, Shuyi Zhang, Xiang Guan, Naiyang Tao, Dacheng Huang, Xuhui Luo, Zhigang Science and Technology on Parallel Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha Hunan410073 China Department of Computer Science and Technology College of Computer National University of Defense Technology Changsha Hunan410073 China Centre for Quantum Computation & Intelligent Systems and the Faculty of Engineering and Information Technology University of Technology Sydney 235 Jones Street UltimoNSW2007 Australia

ISBN: (纸本)9783319144412

Non-negative matrix factorization (NMF) has been a popular data analysis tool and has been widely applied in computer vision. However, conventional NMF methods cannot adaptively learn grouping structure froma *** paper proposes a non-negative low-rank and group-sparse matrix factorization (NLRGS) method to overcome this deficiency. Particularly, NLRGS captures the relationships among examples by constraining rank of the coefficients meanwhile identifies the grouping structure via group sparsity regularization. By both constraints, NLRGS boosts NMF in both classification and clustering. However, NLRGS is difficult to be optimized because it needs to deal with the low-rank constraint. To relax such hard constraint, we approximate the low-rank constraint with the nuclear norm and then develop an optimization algorithm for NLRGS in the frame of augmented Lagrangian method(ALM). Experimental results of both face recognition and clustering on four popular face datasets demonstrate the effectiveness of NLRGS in quantities. © Springer International Publishing Switzerland 2015.

关键词： Non-negative matrix factorization

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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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Implementation of an Accurate and Efficient Compensated DGEMM for 64-bit ARMv8 Multi-Core Processors

Implementation of an Accurate and Efficient Compensated DGEM...

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International Conference on parallel and distributed Systems (ICPADS)

作者： Hao Jiang Feng Wang Kuan Li Canqun Yang Kejia Zhao Chun Huang College of Computer Science 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)9781467386692

This paper presents an implementation of an accurate and efficient compensated Double-precision General Matrix Multiplication (DGEMM) based on OpenBLAS for 64-bit ARMv8 multi-core processors. Due to cancellation phenomena in floating point arithmetic, the results of DGEMM may not be as accurate as expected. In order to increase the accuracy of DGEMM, we compensate the error introduced by its dot product kernel (GEBP) by applying an error-free transformation to rewrite the kernel in assembly language. We optimize the computations in the inner kernel through exploiting loop unrolling, instruction scheduling and software-implemented register rotation to exploit instruction level parallelism (ILP). We also conduct a priori error analysis of the derived CompDGEMM. Our compensated DGEMM is as accurate as the existing quadruple precision GEMM using MBLAS, but is up to 6.4x faster. Our parallel implementation achieves good performance and scalability under varying thread counts across a range of matrix sizes evaluated.

关键词： Multicore processing Kernel Registers Libraries Error analysis Algorithm design and analysis

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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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A Novel Run-time Load Balancing Method for MapReduce

A Novel Run-time Load Balancing Method for MapReduce

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International Conference on computer Science and Network Technology

作者： Zhihong Liu Yaping Liu Baosheng Wang Zhenghu Gong College of Computer National University of Defense Technology Changsha Hunan China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9781467381741

In recent years, many companies are embracing the Hadoop MapReduce system for large-data processing with completion time constrains. However, exiting Hadoop schedulers still suffer from the reducer load imbalancing problem. In this paper, we present a novel run-time load balancing method for MapReduce. Our approach predicts the workload of each reduce task at run-time, and assigns the reduce tasks to specified machines based on the estimated workload of reduce tasks dynamically. Therefore, our approach can achieve load balance among machines. The experimental results show that our approach achieves high accuracy while predicting the workload of reduce tasks, and improves the job completion time by up to 23.15%.

关键词： MapReduce load balancing task scheduling Load balancing TASK SCHEDULING Completion time Runtime Workload Religious Missions

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