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A High-Performance Click-based Packet Classifier on GPU 5

A High-Performance Click-based Packet Classifier on GPU

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5th international conference on Computer and Knowledge Engineering (lCCKE)

作者： Gholami, Maisam Sookhtezari, Younes Haghighi, Milad BahramBeigy, Bahram Ahmadi, Mahmood Islamic Azad Univ Kermanshah Informat Technol Dept Kermanshah Iran Razi Univ Dept Comp Engn Kermanshah Iran

ISBN: (纸本)9781467392808

In this paper, design and configuration of a high-performance Click-based packet classifier on GPU is presented. In this regard, Bloom filter-based classification algorithm is used in order to design and implement the Click-based packet classifier elements (processors) on Graphics processing Unit (GPU). On one hand, utilization of Bloom filter data structure leads to memory usage reduction of the router. On the other hand, the parallel nature of Bloom filter and GPU architecture bring higher throughput of the software router. the analysis results indicate approximately five times speedup of the proposed Click-based classifier on GPU compared with the standard Click-based classifier.

关键词： Bloom filter Click router Graphics processing Unit Packet classification

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An Efficient Spatial Query processing Algorithm in Multi-sink Directional Sensor Network 15th

An Efficient Spatial Query Processing Algorithm in Multi-sin...

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15th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Ma, Zheng Zheng, Jin Jia, Weijia Wang, Guojun Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Hunan Univ Sch Informat Sci & Engn Changsha 410082 Hunan Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China Guangzhou Univ Sch Comp Sci & Educ Software Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9783319271613;9783319271606

In order to address the problem of energy- and time-efficient execution of spatial queries in directional sensor networks, an efficient hybrid spatial query processing algorithm called SQPDSN is proposed in this paper. In the majority of studies on query processing using wireless sensor networks, sensors are assumed to have an isotropic sensing and transmission model. However, in certain applications the sensors have directional sensing and directional transmission model. SQPDSN only requires each node within the query region send data message once which reduces the data messages. For achieving minimal energy consumption and minimal response time, our query processing model ensures that only the relevant nodes for the correct execution of a query are involved in the query execution. Each sector has a node which collects the sensory data in it, aggregates the data to derive partial query result and send it to the next sector. Compared with other techniques, the experimental results demonstrated an improvement of the proposed technique in terms of energy efficient query cover with lower communication cost.

关键词： Multi-Sink wireless sensor networks Directional transmission Spatial query parallel processing

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Evaluation of Global Synchronization for Iterative Algebra algorithms on Many-Core 16

Evaluation of Global Synchronization for Iterative Algebra A...

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16th IEEE/ACIS international conference on Software Engineering, Artificial Intelligence, Networking and parallel/Distributed Computing (SNPD)

作者： Khan, Ayaz ul Hasan Al-Mouhamed, Mayez Firdaus, Lutfi A. KFUPM Dept Comp Engn Coll Comp Sci & Engn Dhahran 31261 Saudi Arabia

ISBN: (纸本)9781479986767

Massively parallel computing is applied extensively in various scientific and engineering domains. With the growing interest in many-core architectures and due to the lack of explicit support for inter-block synchronization specifically in GPUs, synchronization becomes necessary to minimize inter-block communication time. In this paper, we have proposed two new inter-block synchronization techniques: 1) Relaxed Synchronization, and 2) Block-Query Synchronization. these schemes are used in implementing numerical iterative solvers where computation/communication overlapping is one used optimization to enhance application performance. We have evaluated and analyzed the performance of the proposed synchronization techniques using Jacobi Iterative Solver in comparison to the state of the art inter-block lock-free synchronization techniques. We have achieved about 1-8% performance improvement in terms of execution time over lock-free synchronization depending on the problem size and the number of thread blocks. We have also evaluated the proposed algorithm on GPU and MIC architectures and obtained about 8-26% performance improvement over the barrier synchronization available in OpenMP programming environment depending on the problem size and number of cores used.

关键词： CUDA OpenMP GPU Xeon Phi Inter-Block Synchronization Jacobi Iterative Method Graphics processing Unit (GPU)

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Microblog data parallel monitoring algorithm on compute unified device Architecture 4th

Microblog data parallel monitoring algorithm on compute unif...

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4th international conference on Computer Engineering and Networks, CENet2014

作者： Cao, Yunpeng Wang, Haifeng College of Information Linyi University LinyiShandong276005 China Institute of LinYi University of Shandong Provincial Key Laboratory of Network Based Intelligent Computing LinyiShandong276005 China

ISBN: (纸本)9783319111032

there is a large-scale information data in microblog systems to be processed in real time. processing large-scalemicroblog data needs high-performance computing architectures and parallel algorithms. Graphic processing units are adaptable to process data-intensive computing tasks. Using parallel algorithm of GPUs is an effectivemethod to deal with real-time microblog data. Dynamic thread configuration scheme was proposed to decompose the microblog data. the key optimization technique for accessing data on GPUs is solved. Finally, the experimental results show that the proposed parallel algorithm can achieve the average speedup ratio of about 15.3–17.9 and process more than 487 MB per second in practice. © Springer international Publishing Switzerland 2015.

关键词： parallel algorithms

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Scheduling Stochastic Tasks with Precedence Constrain on Cluster Systems with Heterogenous Communication Architecture 15th

Scheduling Stochastic Tasks with Precedence Constrain on Clu...

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15th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Liao, Qun Jiang, Shuangshuang Hei, Qiaoxiang Li, Tao Yang, Yulu Nankai Univ Coll Comp & Control Engn Tianjin 300353 Peoples R China

ISBN: (纸本)9783319271613;9783319271606

Scheduling precedence constrained stochastic tasks on heterogenous cluster systems is an important issue which impact the performance of clusters significantly. Different with deterministic tasks, stochastic task model assumes that the workload of task and quantity of data transmission between tasks are stochastic variables, which is more realistic than other task models. Scheduling model and algorithms of precedence constrained stochastic tasks attract a large number of researchers' attention recently. An algorithm SDLS (Stochastic Dynamic Level Scheduling) has been proved performing well in scheduling stochastic tasks on heterogenous clusters. However, the assumption about communication time between tasks in SDLS is much simpler than its assumptions about task computing time, which makes it cannot depict the communication cost among heterogenous links well. In this paper, it is assumed that the quantity of data communication between tasks is a stochastic variable of normal distribution, instead of assuming communication time among heterogenous links a same stochastic variable immediately. Moreover, a modified scheduling model and algorithm SDLS-HC (Stochastic Dynamic Level Scheduling on Heterogenous Communication links) are proposed. Work in this paper focus on considering much more detailed communication cost in task scheduling based on SDLS. Evaluation on many random generated tasks experiments demonstrates that SDLS-HC achieves better performance than SDLS on cluster systems with heterogenous links.

关键词： Stochastic tasks scheduling Directed acyclic graph Heterogenous clusters parallel and distributed processing Distributed system

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parallel framework for efficient k-means clustering 15

Parallel framework for efficient k-means clustering

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8th ACM COMPUTE INDIA conference, Compute 2015

作者： Kumari, Sonal Maheshwari, Anil Goyal, Poonam Goyal, Navneet ADAPT Laboratory CSIS Department BITS-Pilani Pilani Campus 333031 India Computational Geometry Laboratory School of Computer Science Carleton University Ottawa Canada

ISBN: (纸本)9781450336505

Handling and processing of larger volume of data requires efficient data mining algorithms. k-means is a very popular clustering algorithm for data mining, but its performance suffers because of initial seeding problem. the computation time of k-means algorithm is directly proportional to the number of datapoints, number of dimensions, and number of iterations, therefore, it is very expensive to process large data-points sequentially. We proposed an efficient parallel framework which includes dimensionality-reduction as well as data-size reduction techniques to improve k-means processing time and initial seeding problem. Our proposed parallel framework leverages the multi-node and multi-core architectures of a typical commodity cluster. We have validated our proposed approaches with real and synthetic datasets in parallel environment setup. the experimental results clearly show the significant improvements in k-means performance. © 2015 ACM.

关键词： Clustering algorithms

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GPU technology as a platform for accelerating physiological systems modeling based on Laguerre-Volterra networks 37

GPU technology as a platform for accelerating physiological ...

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37th Annual international conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Papadopoulos, Agathoklis Kostoglou, Kyriaki Mitsis, Georgios D. theocharides, theocharis Univ Cyprus Dept Elect & Comp Engn KIOS Res Ctr CY-1678 Nicosia Cyprus Univ Cyprus Dept Elect & Comp Engn Biosignal Proc & Quantitat Syst Physiol Lab CY-1678 Nicosia Cyprus

ISBN: (纸本)9781424492701

the use of a GPGPU programming paradigm (running CUDA-enabled algorithms on GPU cards) in biomedical engineering and biology-related applications have shown promising results. GPU acceleration can be used to speedup computation-intensive models, such as the mathematical modeling of biological systems, which often requires the use of nonlinear modeling approaches with a large number of free parameters. In this context, we developed a CUDA-enabled version of a model which implements a nonlinear identification approach that combines basis expansions and polynomial-type networks, termed Laguerre-Volterra networks and can be used in diverse biological applications. the proposed software implementation uses the GPGPU programming paradigm to take advantage of the inherent parallel characteristics of the aforementioned modeling approach to execute the calculations on the GPU card of the host computer system. the initial results of the GPU-based model presented in this work, show performance improvements over the original MATLAB model.

关键词： biomedical engineering graphics processing units mathematics computing medical computing parallel architectures polynomial approximation CUDA-enabled version GPGPU programming paradigm GPU acceleration GPU cards GPU technology Laguerre-Volterra networks accelerating physiological systems modeling basis expansions biological systems biology-related applications diverse biological applications host computer system inherent parallel characteristics mathematical modeling nonlinear identification approach nonlinear modeling approaches original MATLAB model polynomial-type networks running CUDA-enabled algorithms software implementation speedup computation-intensive models Biological system modeling Biomedical engineering Computational modeling Graphics processing units Kernel MATLAB Programming

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Segmentation of Tumor Vessels based on parallel Double Snakes Including Region Information 5

Segmentation of Tumor Vessels based on Parallel Double Snake...

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international conference on Image processing theory Tools and Applications

作者： Glanc, Pierre Rossant, Florence Rouffiac, Valerie Laplace-Builhe, Corinne Leguerney, Ingrid Pitre-Champagnat, Stephanie Inst Super Elect Paris 10 Rue Vanves F-92130 Issy Les Moulineaux France Univ Paris Sud Gustave Roussy Imaging & Cytometry Platform UMS AMMICACNRS F-94805 Villejuif France Univ Paris Sud Lab IR4M CNRS F-94805 Villejuif France Univ Paris Sud Gustave Roussy CNRS F-94805 Villejuif France

ISBN: (纸本)9781479986378

In this paper, we address the problem of the segmentation of vessels in images of mouse tumors, with an efficient algorithm that minimizes the user's intervention. For each vessel, two points delimiting its extremities have to be selected. then, a line inside the vessel is automatically determined based on a Dijkstra-type algorithm. Finally, an original active contour model combining both parallel double snakes and region criteria aims at finding the borders of the vessel. Our segmentation algorithm provides numerical models of tumor vessels, suitable for the simulation of blood and contrast agent flow.

关键词： Image segmentation Tumor vessels Active contour models parallel Double Snakes Region criteria

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A Mobile Agent Team Works Model for HPC Big Data Analysis: Fuzzy Logic Application 5

A Mobile Agent Team Works Model for HPC Big Data Analysis: F...

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5th international conference on Information and Communication Technology and Accessbility (ICTA)

作者： Benchara, Fatema Zahra Youssfi, Mohamed Bouattane, Omar Ouajji, Hassan Hassan II Univ Casablanca ENSET Mohammedia Lab SSDIA Mohammadia Morocco

ISBN: (纸本)9781467387491

the aim of this paper is to present a distributed implementation of the Type-2 Fuzzy Logic (T2FL) algorithm in a mobile agent based distributed computing platform. the proposed algorithm is assigned to be implemented on an SPMD (Single Program Multiple Data) architecture which is based on a cooperative mobile agent model. It is constituted by a set of mobile agents as AVPEs (Agent Virtual processing Elements) in order to improve the processing resources needed for performing the big data image segmentation. In this work we focused on the application of this algorithm to process the big data MRI (Magnetic Resonance Images) image. the input image is splitted into elementary images by the Mobile Team leader Agent and encapsulated one per AVPE. Each AVPE perform and exchange the segmentation results and maintain asynchronous communication with their Team leader agent until the convergence of this algorithm. the obtained experimental results in terms of accuracy and efficiency analysis of the proposed distributed implementation are achieved thanks to the mobile agents several interesting skills introduced in this distributed computational model.

关键词： Distributed Type-2 Fuzzy algorithm Image processing Mobile Agents parallel and Distributed Computing

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A Data-oriented Method for Scheduling Dependent Tasks on High-density Multi-GPU Systems 17

A Data-oriented Method for Scheduling Dependent Tasks on Hig...

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

作者： Zhang, Peng Gao, Yuxiang Qiu, Meikang SUNY Stony Brook Dept Biomed Engn Stony Brook NY 11794 USA Cray Inc Cluster Solut Dept San Jose CA USA Pace Univ Dept Comp Sci New York NY 10038 USA

ISBN: (纸本)9781479989379

the rapidly-changing computer architectures, though improving the performance of computers, have been challenging the programming environments for efficiently harnessing the potential of novel architectures. In this area, though the high-density multi-GPU architecture enabled unparalleled performance advantage of dense GPUs in a single server, it has increased the difficulty for scheduling diversified and dependent tasks. We therefore propose a data-oriented method for scheduling dependent tasks for this architecture while providing its implementation. In our method, we model a parallel program as a collection of data-dependent tasks for which data dependencies are managed by an expressive matrix. Accordingly, we develop a hierarchical scheduler infrastructure for our model. In this, a top scheduler is built for querying the data-dependency matrix;three downstream schedulers for queuing computation tasks that are exclusively assigned to processor, accelerator or either;and a multitude of bottom schedulers each for providing a processing element with assigned tasks. We experiment our scheduler for examples of Strassen matrix multiplication and Cholesky matrix inversion algorithms on a computer that has 8 Tesla K40 GPUs. the results show that our method is capable of offering the efficient task parallelism while fulfilling the complex task dependencies. When advanced task-oriented schedulers have been widely designed for distributed systems, a lightweight data-driven scheduler could be an alternative and handy approach that can handle the dependent yet diversified tasks of data-intensive applications for the novel high-density multi-accelerator system.

关键词： data scheduling task scheduling parallel computing heterogeneous multi-GPU systems

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