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4th International Conference on computer Engineering and Networks, CENet2014

作者： Li, Teng Dou, Yong Jiang, Jingfei Qiao, Peng Department of Computer Science National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha410073 China

ISBN: (纸本)9783319111032

Moving objects detection is important in traffic video analysis, and many algorithms are being increasingly applied to moving objects detection. Most of these algorithms are time-consuming and cannot satisfy real-time demand in traffic video analysis by using a conventional central processing unit (CPU) sequential method. The emergence of the graphics processing unit (GPU) and multi-core CPU provides a method to accelerate the aforementioned algorithms to meet demand. In this study, we provide a GPU-accelerated implementation of the background subtraction algorithm and the morphological operation algorithm. Then, the connected component labeling algorithm is parallelized on a multi-core CPU with Open Multi-Processing (OpenMP). Furthermore, parallel pipeline implementation on a heterogeneous platform is proposed by integrating the aforementioned algorithms. Experimental results show that the proposed implementation achieves a significant speedup of up to 5×, compared with sequential implementation on a CPU. © Springer International Publishing Switzerland 2015.

关键词： Program processors

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Tinman: Eliminating confidential mobile data exposure with security oriented offloading 15

Tinman: Eliminating confidential mobile data exposure with s...

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10th European Conference on computer systems, EuroSys 2015

作者： Xia, Yubin Liu, Yutao Tan, Cheng Ma, Mingyang Guan, Haibing Zang, Binyu Chen, Haibo Shanghai Key Laboratory of Scalable Computing and Systems China Institute of Parallel and Distributed Systems Shanghai Jiao Tong University China Department of Computer Science Shanghai Jiao Tong University China

ISBN: (纸本)9781450332385

The wide adoption of smart devices has stimulated a fast shift of security-critical data from desktop to mobile devices. However, recurrent device theft and loss expose mobile devices to various security threats and even physical attacks. This paper presents TinMan, a system that protects confidential data such as web site password and credit card number (we use the term cor to represent these data, which is short for Confidential Record) from being leaked or abused even under device theft. TinMan separates accesses of cor from the rest of the functionalities of an app, by introducing a trusted node to store cor and offloading any code from a mobile device to the trusted node to access cor. This completely eliminates the exposure of cor on the mobile devices. The key challenges to TinMan include deciding when and how to efficiently and transparently offload execution;Tin-Man addresses these challenges with security-oriented offloading with a low-overhead tainting scheme called asymmetric tainting to track accesses to cor to trigger offloading, as well as transparent SSL session injection and TCP payload replacement to offload accesses to cor. We have implemented a prototype of TinMan based on Android and demonstrated how TinMan protects the information of user's bank account and credit card number without modifying the apps. Evaluation results also show that TinMan incurs only a small amount of performance and power overhead. Copyright © 2015 ACM.

关键词： Crime

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SYNC or ASYNC: Time to fuse for distributed graph-parallel computation 2015

SYNC or ASYNC: Time to fuse for distributed graph-parallel c...

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20th ACM SIGPLAN Symposium on Principles and Practice of parallel Programming, PPoPP 2015

作者： Xie, Chenning Chen, Rong Guan, Haibing Zang, Binyu Chen, Haibo Shanghai Key Laboratory of Scalable Computing and Systems Institute of Parallel and Distributed Systems Shanghai Jiao Tong University China Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science Shanghai Jiao Tong University China

ISBN: (纸本)9781450332057

Large-scale graph-structured computation usually exhibits iterative and convergence-oriented computing nature, where input data is computed iteratively until a convergence condition is reached. Such features have led to the development of two different computation modes for graph-structured programs, namely synchronous (Sync) and asynchronous (Async) modes. Unfortunately, there is currently no in-depth study on their execution properties and thus programmers have to manually choose a mode, either requiring a deep understanding of underlying graph engines, or suffering from suboptimal performance. This paper makes the first comprehensive characterization on the performance of the two modes on a set of typical graph-parallel applications. Our study shows that the performance of the two modes varies significantly with different graph algorithms, partitioning methods, execution stages, input graphs and cluster scales, and no single mode consistently outperforms the other. To this end, this paper proposes Hsync, a hybrid graph computation mode that adaptively switches a graph-parallel program between the two modes for optimal performance. Hsync constantly collects execution statistics on-the-fly and leverages a set of heuristics to predict future performance and determine when a mode switch could be profitable. We have built online sampling and offline profiling approaches combined with a set of heuristics to accurately predicting future performance in the two modes. A prototype called PowerSwitch has been built based on PowerGraph, a state-of-the-art distributed graph-parallel system, to support adaptive execution of graph algorithms. On a 48-node EC2-like cluster, PowerSwitch consistently outperforms the best of both modes, with a speedup ranging from 9% to 73% due to timely switch between two modes. Copyright 2015 ACM.

关键词： Graphic methods

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Locality Protected Dynamic Cache Allocation Scheme on GPUs

Locality Protected Dynamic Cache Allocation Scheme on GPUs

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Yang Zhang Zuocheng Xing Li Zhou Chunsheng Zhu National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China School of Electronic Science and Engineering National University of Defense Technology Changsha China Department of Electrical and Computer Engineering University of British Columbia Vancouver BC Canada

ISBN: (纸本)9781509032068

As we are approaching the exascale era in supercomputing, designing a balanced computer system with powerful computing ability and low energy consumption becomes increasingly important. GPU is a widely used accelerator in most recently applied supercomputers. It adopts massive multithreads to hide long latency and has high energy efficiency. In contrast to its strong computing power, GPUs have few on-chip resources with several MB of fast on-chip memory storage per SM (Streaming Multiprocessors). GPU caches exhibit poor efficiency due to the mismatch of the throughput-oriented execution model and its cache hierarchy design. Since the severe deficiency in on-chip memory, the benefit of high computing capacity of GPUs is pulled down by the poor cache performance dramatically, which limits system performance and energy-efficiency. In this paper, we put forward a locality protected scheme to make full use of the data locality based on the fixed capacity. We present a Locality Protected method based on instruction PC (LPP) to promote GPU performance. Firstly, we use a PC-based collector to collect the reuse information of each cache line. After getting the dynamic reuse information of the cache line, we take an intelligent cache allocation unit (ICAU) which coordinates the reuse information with LRU (Least Recently Used) replacement policy to find out the cache line with the least locality for eviction. The results show that LPP provides an up to 17.8% speedup and an average of 5.5% improvement over the baseline method.

关键词： Graphics processing units System-on-chip computer architecture Instruction sets Resource management Optimization Electronic mail

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Preface

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CEUR Workshop Proceedings 2017年 1826卷

作者： Kopp, Oliver Lenhard, Jörg Pautasso, Cesare University of Stuttgart Institute for Parallel and Distributed Systems Universitätsstraße 38 StuttgartDE70569 Germany Karlstad University Department of Mathematics and Computer Science Universitetsgatan 2 KarlstadSE65188 Sweden USI Faculty of Informatics Architecture Design and Web Information Systems Engineering Research Group Via Buffi 13 LuganoCH6900 Switzerland

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A concurrent k-NN search algorithm for R-tree 15

A concurrent k-NN search algorithm for R-tree

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8th ACM COMPUTE INDIA Conference, Compute 2015

作者： Challa, Jagat Sesh Goyal, Poonam Nikhil, S. Balasubramaniam, Sundar Goyal, Navneet Advanced Data Analytics and Parallel Technologies Laboratory Department of Computer Science and Information Systems Birla Institute of Technology and Science - Pilani Pilani Campus Pilani India

ISBN: (纸本)9781450336505

K-nearest neighbor (k-NN) search is one of the commonly used query in database systems. It has its application in various domains like data mining, decision support systems, information retrieval, multimedia and spatial databases, etc. When k-NN search is performed over large data sets, spatial data indexing structures such as R-trees are commonly used to improve query efficiency. The best-first k-NN (BF-kNN) algorithm is the fastest known k-NN over R-trees. We present CBF-k NN, a concurrent BF-kNN for R-trees, which is the first concurrent version of k-NN we know of for R-trees. CBF-k NN uses one of the most efficient concurrent priority queues known as mound. CBF-k NN overcomes the concurrency limitations of priority queues by using a tree-parallel mode of execution. CBF-kNN has an estimated speedup of 0(p/k) for p threads. Experimental results on various real datasets show that the speedup in practice is close to this estimate. © 2015 ACM.

关键词： Data mining

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A Workload-Aware Energy Model for Virtual Machine Migration

A Workload-Aware Energy Model for Virtual Machine Migration

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IEEE International Conference on Cluster Computing

作者： Vincenzo De Maio Gabor Kecskemeti Radu Prodan Institute of Computer Science University of Innsbruck MTA SZTAKI Laboratory of Parallel and Distributed Systems

Energy consumption has become a significant issue for data centres. Assessing their consumption requires precise and detailed models. In the latter years, many models have been proposed, but most of them either do not consider energy consumption related to virtual machine migration or do not consider the variation of the workload on (1) the virtual machines (VM) and (2) the physical machines hosting the VMs. In this paper, we show that omitting migration and workload variation from the models could lead to misleading consumption estimates. Then, we propose a new model for data centre energy consumption that takes into account the previously omitted model parameters and provides accurate energy consumption predictions for paravirtualised virtual machines running on homogeneous hosts. The new model's accuracy is evaluated with a comprehensive set of operational scenarios. With the use of these scenarios we present a comparative analysis of our model with similar state-of-the-art models for energy consumption of VM Migration, showing an improvement up to 24% in accuracy of prediction.

关键词： Energy consumption Power demand Memory management Computational modeling Virtual machining Predictive models Hardware

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Streaming 4K/UHD video to the rest of us: Can bandwidth sharing help? 16

Streaming 4K/UHD video to the rest of us: Can bandwidth shar...

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16th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2015

作者： Montes, Isabel Chua, Jaymari Cruz, Michelangelo Remillano, Augusto Young, Joseph Darwin Ocampo, Roel Festin, Cedric Computer Networks Laboratory Electrical and Electronics Engineering Institute University of the Philippines Diliman Philippinese United States Networks and Distributed Systems Group Department of Computer Science University of the Philippines Diliman Philippinese United States

ISBN: (纸本)9781479984619

High quality video streaming for mobile users is difficult to achieve in some areas of the world due to poor broadband capacity and sparse network coverage. We propose a bandwidth-sharing scheme to allow users with limited uplink bandwidth to borrow idle bandwidth from nearby access points and provide these users the opportunity to experience higher quality video than what their own bandwidth could normally support. We developed a Linux kernel implementation of a multipath less-than-best-effort (MP-LBE) protocol for bandwidth scavenging and demonstrated, through testbed experiments, that video quality may be improved through bandwidth sharing. This may bring us closer to the 'video everywhere' vision even as incremental upgrades are made on broadband Internet infrastructure. © 2015 IEEE.

关键词： Video streaming

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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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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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