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11th International conference on Control, Automation, Robotics and Vision (ICARCV 2010)

作者： Wu, Haiyan Lou, Lei Chen, Chih-Chung Hirche, Sandra Kuehnlenz, Kolja Tech Univ Munich Inst Automat Control Engn LSR D-8000 Munich Germany

ISBN: (纸本)9781424478132

In this paper, a networked visual servo control system with distributed computation is proposed to overcome the low sampling rate problem in vision-based control systems. A real-time image data transmission protocol based on Real-time Transport Protocol (RTP) is developed. The captured images are sent to different processing nodes connected over a communication network and processed in parallel. Thus, a high sampling rate of the visual feedback is achieved under a cloud image processing architecture. The varying image processing delay caused by the varying number of extracted features and the random transmission delay are modeled as a random process with Bernoulli distribution. By using the input-delay approach, the resulted networked visual servo control system is reformulated into a stochastic continuous-time system with time-varying delay. Experiments on two 1-DoF linear motor modules are carried out to validate the proposed approach. A visual servo control system without parallel distributed computation is implemented for comparison. The experimental results demonstrate significant performance improvement by the proposed approach.

关键词： Bernoulli distribution cloud image processing architecture communication network computer networks computer vision continuous time systems delay systems distributed computation feature extraction feature extraction image processing delay input-delay appro

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A Grid version of the Fusion code FAFNER

A Grid version of the Fusion code FAFNER

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18th Euromicro International conference on parallel, distributed and Network-Based processing (PDP)

作者： Rodriguez-Pascual, Manuel A. Guasp, Jose Castejon, Francisco Rubio-Montero, Antonio J. Llorente, Ignacio M. Mayo, Rafael CIEMAT Madrid Spain EURATOM CIEMAT Assoc Spanish Natl Lab Fus Madrid Spain Univ Complutense Madrid Distributed Syst Architecture Res Grp Madrid Spain

ISBN: (纸本)9780769539393

FAFNER is code developed by Lister which simulates by Monte Carlo methods the Neutral Beam Injection (NBI) technology, one of the most extended heating methods for fusion devices. To the date, FAFNER has been usually run at CIEMAT adapted to the TJ-II helical axis stellarator on shared memory Cray architecture machines. From this version, FAFNER has been ported to the Grid in the framework of the EGEE Project. At the same time, this work is the first step of a more ambitious target since the code can be now coupled to many others such as ion transport tools. In this paper, all these preliminary advances are described as well as the performance and portability gains obtained.

关键词： NBI heating Grid applications

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REAL-TIME distributed TRACKING WITH NON-OVERLAPPING CAMERAS

REAL-TIME DISTRIBUTED TRACKING WITH NON-OVERLAPPING CAMERAS

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IEEE International conference on image processing

作者： Wang, Youlu He, Li Velipasalar, Senem Univ Nebraska Dept Elect Engn Lincoln NE 68588 USA

ISBN: (纸本)9781424479948

We present a real-time distributed system for tracking with non-overlapping camera views. Each camera performs multi-object tracking, and cameras communicate with each other in a peer-to-peer manner for consistent labeling. To match objects across non-overlapping views, we employ multiple features, namely color histogram, height, travel time and speed. First, camera configuration and reference values of different features are learned in the training phase. Then, we combine multiple evidences by computing an overall similarity score, which is a weighted sum of the similarity scores of different features. Communication and frame processing run in parallel and share memory. Experimental results show the success of the presented system in real-time tracking with non-overlapping cameras and in handling merge cases.

关键词： Tracking real-time distributed multi-camera non-overlapping cameras

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parallel Iterative Linear Solvers on GPU: A Financial Engineering Case

Parallel Iterative Linear Solvers on GPU: A Financial Engine...

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18th Euromicro International conference on parallel, distributed and Network-Based processing (PDP)

作者： Gaikwad, Abhijeet Toke, Ioane Muni Ecole Cent Paris Lab MAS Chatenay Malabry France

ISBN: (纸本)9780769539393

In many numerical applications resulting from computational science and engineering problems, the solution of sparse linear systems is the most prohibitively compute intensive task. Consequently, the linear solvers need to be carefully chosen and efficiently implemented in order to harness the available computing resources. Krylov subspace based iterative solvers have been widely used for solving large systems of linear equations. In this paper, we focus on the design of such iterative solvers to take advantage of massive parallelism of general purpose Graphics processing Units (GPU)s. We will consider Stabilized BiConjugate Gradient (BiCGStab) and Conjugate Gradient Squared (CGS) methods for the solutions of sparse linear systems with unsymmetric coefficient matrices. We discuss data structures and efficient implementation of these solvers on the NVIDIA's CUDA platform. We evaluate scalability and performance of our implementations in the context of a financial engineering problem of solving multidimensional option pricing PDEs using sparse grid combination technique.

关键词： Sparse linear iterative solvers GPU parallel computing computational finance

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RESEARCH ON parallel CONE-BEAM CT image RECONSTRUCTION ON CUDA-ENABLED GPU

RESEARCH ON PARALLEL CONE-BEAM CT IMAGE RECONSTRUCTION ON CU...

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IEEE International conference on image processing

作者： Sun, Yigang Sun, Xiuyu Zhang, Hongying Civil Aviat Univ China Coll Aeronaut Automat Tianjin 300300 Peoples R China

ISBN: (纸本)9781424479948

Computed tomography (CT) image reconstruction algorithms via graphic processing unit (GPU) have recently attracted much public attention. These methods often adopt cached texture memory to reduce GPU's high memory latency. However, these texture-based methods still have low efficiency because of their low cache hit rates. By studying threads' execution model on GPU, this paper proposes an accelerating scheme based on the degree of streaming multiprocessor level parallelism. This parallel strategy could make simultaneously executing threads in each multiprocessor have closer localities of memory accesses to improve the utilization of cached texture memory. Experiment results indicate that our accelerating scheme could reduce the computing time by 20%-30% for both forward-and backward-projections on GPU.

关键词： computed tomography forward-projection backprojection CT reconstruction GPU texture cache optimization

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On distributing load in cloud computing: A real application for very-large image datasets

On distributing load in cloud computing: A real application ...

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International conference on Computational Science (ICCS)

作者： Alonso-Calvo, Raul Crespo, Jose Garcia-Remesal, Miguel Anguita, Alberto Maojo, Victor Univ Politecn Madrid GIB LIA DLSIIS DIA Boadilla De Monte 28660 Madrid Spain

Managing large image collections has become an important issue for information companies and institutions. We present a cloud computing service and its application for the storage and analysis of very-large images. This service has been implemented using multiple distributed and collaborative agents. For image storage and analysis, a region-oriented data structure is utilized, which allows storing and describing image regions using low-level descriptors. Different types of structural relationships between regions are also taken into account. A distinctive goal of this work is that data operations are adapted for working in a distributed mode. This allows that an input image can be divided into different sub-images that can be stored and processed separately by different agents in the system, facilitating processing very-large images in a parallel manner. A key aspect to decrease processing time for parallelized tasks is the use of an appropriate load balancer to distribute and assign tasks to agents with less workload. (C) 2010 Published by Elsevier Ltd.

关键词： Cloud computing image processing load balancing image region analysis data structures image storage digital libraries distributed databases parallel processing very-large datasets

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parallel Computing Rendering In Specific Remote Sensing image processing

Parallel Computing Rendering In Specific Remote Sensing Imag...

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conference on Optoelectronic Imaging and Multimedia Technology

作者： Mao Bingjing Xue Bo Chen Xiaomei Ni Guoqiang Beijing Inst Technol Sch Optoelect Beijing 100081 Peoples R China

ISBN: (纸本)9780819483805

Because of the more in-depth scientific research, remote sensing images often contain huge amounts of information. Therefore, remote sensing images always have features with multi-dimensions details and huge size. In order to obtain the ground information more accurately from the images, the remote sensing image processing would have several steps in the aim of better image restore and the image information refining. Frequently, processing for this type of images has faced to some difficult issues, such as calculating slowly or consuming huge in resources. For this reason, the parallel computing rendering in remote sensing image processing is essentially necessary. The parallel computing method approached in this paper does not require the original algorithm rewriting. Under a distributed framework, the method allocated the original algorithm efficiently to the multiple computing cores of the processing computer. Because this method has fully use the computing resources, so the calculating time would be reduced linearly with the number of computing threads. What's more, the method can also truly guarantee the integrity of the remote sensing image data. For the purpose of validating the feasibility of the method, this paper put the parallel computing method on application, in which the method rendering into a radiation simulation of remote sensing image processing. We conducted several experiments and got the statistical results. We integrated the parallel computing into the core of the original algorithm the wide huge size convolution. The experimental results showed that the computing efficiency improved linearly. The number of computer calculating core was proportionally related to the reduced rate of computing time. At the same time, the computing results were identical to the original results.

关键词： parallel Computing Multi-Threaded distributed Framework Remote Sensing processing WCF

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A Survey of Medical image Registration on Multicore and the GPU

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IEEE SIGNAL processing MAGAZINE 2010年第2期27卷 50-60页

作者： Shams, Ramtin Sadeghi, Parastoo Kennedy, Rodney A. Hartley, Richard I. College of Engineering and Computer Science at the Australian National University (ANU).

In this article, we look at early, recent, and state-of-the-art methods for registration of medical images using a range of high-performance computing (HPC) architectures including symmetric multiprocessing (SMP), massively multiprocessing (MMP), and architectures with distributed memory (DM), and nonuniform memory access (NUMA). The article is designed to be self-sufficient. We will take the time to define and describe concepts of interest, albeit briefly, in the context of image registration and HPC. We provide an overview of the registration problem and its main components in the section "Registration." Our main focus will be HPC-related aspects, and we will highlight relevant issues as we explore the problem domain. This approach presents a fresh angle on the subject than previously investigated by the more general and classic reviews in the literature [1]-[3]. The sections "Multi-CPU Implementations" and "Accelerator Implementations" are organized from the perspective of high-performance and parallel- computing with the registration problem embodied. This is meant to equip the reader with the knowledge to map a registration problem to a given computing architecture.

关键词： Biomedical imaging image registration Multicore processing Surges Computer architecture Cancer Ultrasonic imaging Computed tomography Pancreas Laparoscopes

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Local Acceleration in distributed Geographic Information processing with CUDA

Local Acceleration in Distributed Geographic Information Pro...

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18th International conference on Geoinformatics

作者： Zhao, Yong Huang, Zhou Chen, Bin Fang, Yu Yan, Menglong Yang, Zhenzhen Peking Univ Inst Remote Sensing & Geog Informat Syst Beijing 100871 Peoples R China

ISBN: (纸本)9781424473021

DGIP (distributed Geographic Information processing) has become a new tendency of GIS (Geographic Information System) recently. DGIP focuses on how to organize and process a series of geographic resources in distributed computing environment and now existing research is mainly carried out from a global point of view. But it is noticeable that each computing node in distributed computing environment will carry a heavy load with growth of data quantity. So this paper concentrates on how to make each computing node fulfill the subtask more quickly to achieve efficient local acceleration. The paper designs a prototype for distributed remote sensing image processing and achieves local acceleration in each computing node with CUDA (Compute Unified Device Architecture). Firstly, the paper introduces the distributed procedure of the prototype and overviews the architecture and programming model of CUDA. Then the paper takes Mean Filter as an example to design and implement the parallel program with CUDA to accelerate the procedure of remote sensing image processing in each node. To evaluate the performance of the local acceleration, the paper carries out a group of comparative tests between the parallel implementation with CUDA and the conventional implementation. The results demonstrate that the local acceleration with CUDA runs more than 20 times faster than conventional process.

关键词： local acceleration DGIP CUDA parellel programming

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A Scalable parallel Union-Find Algorithm for distributed Memory Computers

A Scalable Parallel Union-Find Algorithm for Distributed Mem...

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8th International conference on parallel processing and Applied Mathematics

作者： Manne, Fredrik Patwary, Md. Mostofa Ali Univ Bergen Dept Informat N-5020 Bergen Norway

ISBN: (纸本)9783642143892

The Union-Find algorithm is used for maintaining a number of non-overlapping sets from a finite universe of elements. The algorithm has applications in a number of areas including the computation of spanning trees, sparse linear algebra, and in image processing. Although the algorithm is inherently sequential there has been some previous efforts at constructing parallel implementations. These have mainly focused on shared memory computers. In this paper we present the first scalable parallel implementation of the Union-Find algorithm suitable for distributed memory computers. Our new parallel algorithm is based on an observation of how the Find part of the sequential algorithm can be executed more efficiently. We show the efficiency of our implementation through a series of tests to compute spanning forests of very large graphs.

关键词： disjoint sets parallel Union-Find distributed memory

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