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A GPU-Based Statistical Framework for Moving Object Segmentation: Implementation, Analysis and applications 8th

A GPU-Based Statistical Framework for Moving Object Segmenta...

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8th Annual international conference on Internet and distributed Computing Systems (IDCS)

作者： Cuzzocrea, Alfredo Mumolo, Enzo Moro, Alessandro Umeda, Kazunori Univ Trieste DIA Dept Trieste Italy CNR ICAR Arcavacata Di Rende Italy Chuo Univ Tokyo 112 Japan

ISBN: (纸本)9783319232379;9783319232362

This paper describes a real-time implementation of a recently proposed background maintenance algorithm and reports the relative performances. Experimental results on dynamic scenes taken from a fixed camera show that the proposed parallel algorithm produces background images with an improved quality with respect to classical pixel-wise algorithms, obtaining a speedup of more than 35 times compared to CPU implementation. It is worth noting that we used both the GeForce 9 series (actually a 9800 GPU) available from the year 2008 and the GeForce 200 series (actually a 295 GPU) available from the year 2009. Finally, we show that this parallel implementation allows us to use it in real-time moving object detection application.

关键词： Graphics processing unit

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Towards Optimal Task Distribution on Computer Clusters with Intel MIC Coprocessors 17

Towards Optimal Task Distribution on Computer Clusters with ...

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2015 IEEE 17th international conference on High Performance Computing and Communications (HPCC)

作者： Lai, Chenggang Huang, Miaoqing Chen, Genlang Univ Arkansas Dept Comp Sci & Comp Engn Fayetteville AR 72701 USA Zhejiang Univ Ningbo Inst Technol Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781479989379

Computer clusters with coprocessors/accelerators are typically leveraged to parallelize applications for reducing computation time. Given N parallel tasks and M processing cores, the typical strategy is to statically distribute those N tasks among M cores so that each core receives N/M tasks. However, for many sophisticated applications, the processing times of N tasks may vary. In other words, some tasks will take longer time than others. The static distribution will cause the cores with light tasks to wait for the cores with the heavy tasks, resulting in an imbalance in task distribution and the nonminimal overall processing time for the application. In this work we apply dynamic task distribution. All the unfinished tasks form a task pool. Once a core finishes a task, it will request a new task from the task pool. Through this manner, all cores will be kept busy in the whole computation process. We apply two additional optimization techniques to further improve the performance of applications on clusters with Intel MIC coprocessors. First, we design hybrid implementations to distribute tasks to both the CPUs and MICs. Second, we apply multiple-level parallelism technique to realize the concurrency among the N tasks as well as the concurrency in each task. We use sparse coding as a case study to demonstrate the advantages of our approach. Sparse coding is a class of unsupervised methods for learning sets of over-complete bases to represent data efficiently. The aim of sparse coding is to find a set of basis vectors such that an input vector can be represented as a linear combination of these basis vectors. The results show that the dynamic task distribution can improve the performance by 25% compared with the static one. Further, the hybrid mode implementation involving both the host CPUs and the MICs can outperform the basic offload mode implementation by 40%.

关键词： Computers Coprocessors Dictionaries Encoding Instruction sets Microwave integrated circuits parallel processing

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Concept of User-PC Computing System 2

Concept of User-PC Computing System

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IEEE international conference on Consumer Electronics - Taiwan (ICCE-TW 2015)

作者： Funabiki, Nobuo Kim, Bongsu Aoyagi, Yuki Okayama Univ Okayama 7008530 Japan

ISBN: (纸本)9781479987450

The User-PC computing system (UPC) is a technology of realizing a parallel computing environment using idling computing resources (CPU cycles) in Personal Computers (PCs) of members in the same organization or group such as a laboratory in a university and a section in a company. UPC can resolve drawbacks in the registration of computing projects, the reliability of computing results, and the continuity of computations that may often happen in Volunteer Computing (VC) systems using voluntary PCs in the Internet. Thus, UPC can be easily and comfortably used by computing users. In this paper, we show the concept of UPC and preliminary evaluations.

关键词： Browsers Computational modeling Computer applications distributed processing Instruction sets Web servers

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Scalable distributed Stream Join processing 15

Scalable Distributed Stream Join Processing

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ACM SIGMOD international conference on Management of Data

作者： Lin, Qian Ooi, Beng Chin Wang, Zhengkui Yu, Cui Natl Univ Singapore Sch Comp Singapore Singapore Monmouth Univ Dept Comp Sci & Software Engn West Long Branch NJ USA

ISBN: (纸本)9781450327589

Efficient and scalable stream joins play an important role in performing real-time analytics for many cloud applications. However, like in conventional database processing, online theta-joins over data streams are computationally expensive and moreover, being memory-based processing, they impose high memory requirement on the system. In this paper, we propose a novel stream join model, called join-biclique, which organizes a large cluster as a complete bipartite graph. Join-biclique has several strengths over state-of-the-art techniques, including memory-efficiency, elasticity and scalability. These features are essential for building efficient and scalable streaming systems. Based on join-biclique, we develop a scalable distributed stream join system, BiStream, over a large-scale commodity cluster. Specifically, BiStream is designed to support efficient full-history joins, window-based joins and online data aggregation. BiStream also supports adaptive resource management to dynamically scale out and down the system according to its application workloads. We provide both theoretical cost analysis and extensive experimental evaluations to evaluate the efficiency, elasticity and scalability of BiStream.

关键词： stream join distributed system data streams

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A software tool to efficiently manage the energy consumption of HPC clusters

A software tool to efficiently manage the energy consumption...

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IEEE international conference on Fuzzy Systems (FUZZ-IEEE)

作者： Cocana-Fernandez, Alberto Sanchez, Luciano Ranilla, Jose Univ Oviedo Dept Informat Gijon Spain

ISBN: (纸本)9781467374286

Today, High Performance Computing clusters (HPC) are an essential tool owing to they are an excellent platform for solving a wide range of problems through parallel and distributed applications. Nonetheless, HPC clusters consume large amounts of energy, which combined with notably increasing electricity prices are having an important economical impact, forcing owners to reduce operation costs. In this work we propose a software, named EECluster, to reduce the high energy consumption of HPC clusters. EECluster works with both OGE/SGE and PBS/TORQUE resource management systems and automatically tunes its decision-making mechanism based on a machine learning approach. The quality of the obtained results using this software are evaluated by means of experiments made using actual workloads from the Scientific Modelling Cluster at Oviedo University and the academic-cluster used by the Oviedo University for teaching high performance computing subjects.

关键词： decision making learning (artificial intelligence) parallel processing power aware computing resource allocation software tools EECluster software tool HPC cluster energy consumption OGE-SGE resource management systems Oviedo University PBS-TORQUE resource management systems Scientific Modelling Cluster academic-cluster decision-making mechanism high performance computing clusters machine learning approach Computational modeling Computer architecture Decision making Fuzzy systems Genetics Software tools Software Tools High Performance Computing power aware computing Computer Architecture Fuzzy systems decision making Computational modeling parallel processing (COMPUTERS) Resource utilization energy consumption Genetics management of resources energy capacity

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Real World applications using parallel Computing techniques in Dynamic Traffic Assignment and Shortest Path Search 18

Real World Applications using Parallel Computing Techniques ...

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18th IEEE international conference on Intelligent Transportation Systems

作者： Attanasi, Alessandro Silvestri, Edmondo Meschini, Pietro Gentile, Guido Univ Roma La Sapienza DICEA Rome Italy PTV SISTeMA Rome Italy

ISBN: (纸本)9781467365963

As the range of applications for Intelligent Transport Systems (ITS) grows wider, the efficiency of the underlying tools for Big Data Analytics becomes of crucial importance. Smart Cities are able to monitor, forecast and (possibly) control the pulse of collective interactions involving networks and environment (such as traffic and pollution) by means of key performance indicators. Technology-guided solutions can proactively support the sustainable development and the optimal management of infrastructures and services, improving the quality of life for both city dwellers and commuters. This requires processing huge amounts of data, continuously streaming in from a variety of fixed sensors (e.g. loops, cameras) and mobile devices (GPS trajectories). In particular, Mobility Control Centres need effective software solutions and fast algorithms to deal with two major problems: Traffic Forecasting and Route Guidance. This paper presents real world examples of large scale applications where both tasks are addressed by implementing parallel computing algorithms, achieving high performances and allowing real time management operations and end-user services. The first test case examines the performance of a routing platform covering the entire Austria region, while the second concerns large instances of dynamic traffic assignment for real-time forecasting.

关键词： Intelligent Transport Systems Big Data Analytics parallel Computing Dynamic Traffic Assignment Dynamic Shortest Path

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An Efficient Algorithm for Tensor Principal Component Analysis via Proximal Linearized Alternating Direction Method of Multipliers

An Efficient Algorithm for Tensor Principal Component Analys...

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international conference on Advanced Cloud and Big Data (CBD)

作者： Linbo Qiao Bofeng Zhang Lei Zhuang Jinshu Su College of Computer National University of Defense Technology ChanzSha China National Laboratory for Parallel and Distributed Processing National University of Defense Technology ChangSha China Department of Statistics Shanghai University of Finance and Economics Shanghai China

ISBN: (纸本)9781509036783

In paper, we focus on the computation of the principal components for a general tensor, known as the tensor principal component analysis (PCA) problem. It has been proven that the general tensor PCA problem is reducible to its matricization form when the order is even. Usually it is considered as low-rank matrix completion problems theoretically. It is common to consider nuclear norm as a surrogate of the rank operator since it is the tightest convex lower bound of the rank operator under certain condition. However, most nuclear norm minimization based approaches involve numbers of singular value decomposition (SVD) operations. Given a matrix X ∈ R m × n , the time complexity of SVD operation is O(mn 2 ), which brings prohibitive computational burden to apply these methods in real applications. However, the problem is non-convex, and the proximal mapping associated with non-convex regularization is not easy to compute. It is always solved by the Linearized Alternating Direction Method of Multipliers (LADMM). Despite the success of LADMM in practice, it remains unknown if LADMM is convergent in solving such non-convex compositely regularized optimization. In this paper, we firstly present a detailed convergence analysis of the LADMM algorithm for solving non-convex compositely regularized optimization with a large class of non-convex penalties. Furthermore, we propose a new efficient and scalable algorithm for matrix principal component analysis called Proximal Linearized Alternating Direction Method of Multipliers for Principal Component Analysis(PLADMPCA). Different from traditional matrix factorization methods, PLADMPCA utilizes the linearization technique to formulate the matrix as an outer product of vectors, which greatly improves the computational efficacy compared to matrix factorization method. We empirically evaluate the proposed algorithm PLADMPCA on synthetic tensor data with different order. Results have shown that PLADMPCA have much better computational

关键词： Tensile stress Principal component analysis Algorithm design and analysis Convergence Matrix decomposition Optimization Computational efficiency

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Optimized and parallel query processing in similarity-based databases 12th

Optimized and parallel query processing in similarity-based ...

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12th international conference on Modeling Decisions for Artificial Intelligence, MDAI 2015

作者： Krajča, Petr Department of Computer Science Palacky University Olomouc 17. Listopadu 12 Olomouc77146 Czech Republic

ISBN: (纸本)9783319232393

We present a novel method of query execution in similarity based databases which adopts techniques commonly used in traditional programming language compilers. Our method is based on decomposition of relational algebra operators into a small set of simple operations which are subject of further optimizations. It shows up that with a small set of optimizations rules our system itself is able to infer efficient algorithms for data processing. Furthermore, operations we propose are compatible with the map/reduce approach to data processing, and thus, allows for implicitly parallel or distributed data processing. © Springer international Publishing Switzerland 2015.

关键词： Fuzzy logic

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An Efficient Pre-filter to Accelerate Regular Expression Matching 15th

An Efficient Pre-filter to Accelerate Regular Expression Mat...

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15th international conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Xu, Chengcheng Chen, Shuhui Wang, Xiaofeng Su, Jinshu Natl Univ Def Technol Coll Comp Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9783319271613;9783319271606

Regular expression matching is widely used in content-aware applications, such as NIDS and protocol identification. However, wire-speed processing for large scale patterns still remains a great challenge in practice. Considering low hit rates in NIDS, a compact and efficient pre-filter is firstly proposed to filter most normal traffics and leave few suspicious traffics for further pattern matching. Experiment results show that, the pre-filter achieves a big improvement in both space and time consumption with its compact and efficient structure.

关键词： Regular expression matching Pattern matching Deep packet inspection DPI Pre-filter

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Enterprise: Breadth-First Graph Traversal on GPUs 15

Enterprise: Breadth-First Graph Traversal on GPUs

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international conference for High Performance Computing, Networking, Storage and Analysis (SC)

作者： Liu, Hang Huang, H. Howie George Washington Univ Dept Elect & Comp Engn Washington DC 20052 USA

ISBN: (纸本)9781450337236

The Breadth-First Search (BFS) algorithm serves as the foundation for many graph-processing applications and analytics workloads. While Graphics processing Unit (GPU) offers massive parallelism, achieving high-performance BFS on GPUs entails efficient scheduling of a large number of GPU threads and effective utilization of GPU memory hierarchy. In this paper, we present Enterprise, a new GPU-based BFS system that combines three techniques to remove potential performance bottlenecks: (1) streamlined GPU threads scheduling through constructing a frontier queue without contention from concurrent threads, yet containing no duplicated frontiers and optimized for both top-down and bottomup BFS. (2) GPU workload balancing that classifies the frontiers based on different out-degrees to utilize the full spectrum of GPU parallel granularity, which significantly increases thread-level parallelism;and (3) GPU based BFS direction optimization quantifies the effect of hub vertices on direction-switching and selectively caches a small set of critical hub vertices in the limited GPU shared memory to reduce expensive random data accesses. We have evaluated Enterprise on a large variety of graphs with different GPU devices. Enterprise achieves up to 76 billion traversed edges per second (TEPS) on a single NVIDIA Kepler K40, and up to 122 billion TEPS on two GPUs that ranks No. 45 in the Graph 500 on November 2014. Enterprise is also very energy-efficient as No. 1 in the GreenGraph 500 (small data category), delivering 446 million TEPS per watt.

关键词： Graphics processing units Instruction sets Arrays Switches Optimization Hardware parallel processing Graphics processing Unit instruction sets GRAPPER PICK UP parallel processing (COMPUTERS) Switches Computer hardware THREADS arrays

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