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A Hierarchical distributed processing Framework for Big image Data

IEEE Transactions on Big Data 2016年第4期2卷 297-309页

作者： Dong, Le Lin, Zhiyu Liang, Yan He, Ling Zhang, Ning Chen, Qi Cao, Xiaochun Izquierdo, Ebroul University of Electronic Science and Technology of China Chengdu611731 China China Academy of Engineering Physics Mianyang Sichuan621900 China Cyberspace Administration of the People's Republic of China Beijing100010 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100864 China Queen Mary University of London LondonE1 4NS United Kingdom

This paper introduces an effective processing framework nominated image Cloud processing (ICP) to powerfully cope with the data explosion in image processing field. While most previous researches focus on optimizing the image processing algorithms to gain higher efficiency, our work dedicates to providing a general framework for those image processing algorithms, which can be implemented in parallel so as to achieve a boost in time efficiency without compromising the results performance along with the increasing image scale. The proposed ICP framework consists of two mechanisms, i.e., Static ICP (SICP) and Dynamic ICP (DICP). Specifically, SICP is aimed at processing the big image data pre-stored in the distributed system, while DICP is proposed for dynamic input. To accomplish SICP, two novel data representations named P-image and Big-image are designed to cooperate with MapReduce to achieve more optimized configuration and higher efficiency. DICP is implemented through a parallel processing procedure working with the traditional processing mechanism of the distributed system. Representative results of comprehensive experiments on the challenging imageNet dataset are selected to validate the capacity of our proposed ICP framework over the traditional state-of-The-Art methods, both in time efficiency and quality of results. © 2015 IEEE.

关键词： MapReduce

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ADVANCES IN SOFTWARE ENGINEERING AND THEIR RELATIONS TO PATTERN RECOGNITION AND image processing.

NATO Conference Series, (Series) 4: Marine Sciences

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NATO conference Series, (Series) 4: Marine Sciences 1980年 1卷 734-741页

作者： Tanimoto, Steven L.

In recent years ″software engineering″ has emerged as a discipline of programming. It includes the conceptualization, design, implementation, testing and modification of software systems. Related issues are languages, standards, distributed and parallel processing, and total programming environments. The fact that more than 80 percent of system development costs were in software rather than hardware helps one appreciate the importance of any effort to understand and enhance the software production process. Many pattern recognition projects involve fairly large software efforts. It makes sense not only for researchers to make use of the latest software tools and methodologies but also to anticipate future changes.

关键词： COMPUTER SOFTWARE

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Database integration and remote accessibility in a distributed vision-based surveillance system 11

Database integration and remote accessibility in a distribut...

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11th Euromicro conference on parallel, distributed and Network-Based processing

作者： Jorge, P Moreno, DL Sánchez, E Barro, S Univ Santiago de Compostela Dept Elect & Computac Fac Fis Grp Sistemas Intelixentes Santiago De Compostela 15706 Spain

ISBN: (纸本)0769518753

In this paper we present the integration of database and remote accessibility in a distributed surveillance system. We focus on the flexibility and scalability of the system, as well as the design and implementation possibilities of readily available free software components. What is more, we discuss the main issues encountered during the development stage and propose feasible solutions for them.

关键词： Computer architecture Connectors distributed computing distributed databases Humans image analysis image databases Motion detection Software architecture Surveillance

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image parallel processing by Using MapReduce

Image Parallel Processing by Using MapReduce

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2021 International conference on Information Science, parallel and distributed Systems, ISPDS 2021

作者： Chen, Lei Zhang, Xuebo Sun, Liting Spacing Engineering University School of Spacing Information Beijing China

ISBN: (纸本)9781665417389

In recent years, big data processing platform Hadoop and parallel computing model MapReduce have achieved good results in mass data processing. However, since Hadoop does not design the input data streams for image files and MapReduce does not define an interface to match the data types of image files, the technology that running MapReduce program on Hadoop cluster to process massive image files in parallel is not mature. In addition, as image characteristic data (such as pixel intensive) is often skewness, image parallel processing on heterogeneous Hadoop cluster triggers a large data transfer and load imbalance among nodes, so that reducing the efficiency of MapReduce job. To tackle aforementioned problems, this paper carried out the following work, (1) The Hadoop platform is optimized to efficiently process the massive image;(2) The MapReduce model is extended and the interface is designed to support parallel image processing. (3) A new partition, namely PIHA, is proposed to mitigate data skew in heterogeneous Hadoop 3.2.0. Experiments show that the method proposed in this paper can effectively improve the efficiency of parallel image processing using MapReduce on Hadoop. © 2021 IEEE.

关键词： MapReduce

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A distributed Eigensolver for Loosely Coupled Networks

A Distributed Eigensolver for Loosely Coupled Networks

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21st Euromicro International conference on parallel, distributed, and Network-Based processing (PDP)

作者： Strakova, Hana Gansterer, Wilfried N. Univ Vienna Res Grp Theory & Applicat Algorithms Vienna Austria

ISBN: (纸本)9780769549392;9781467353212

We introduce a new distributed eigensolver (dOI) for square matrices based on orthogonal iteration. In contrast to standard parallel eigensolvers, our approach performs only nearest neighbor communication and provides much more flexibility with respect to the properties of the hardware infrastructure on which the computation is performed. This is achieved by utilizing distributed summation methods with randomized communication schedules which do not require global synchronization across the nodes. Our algorithm is particularly attractive for loosely coupled distributed networks with arbitrary network topologies and potentially unreliable components. Our distributed eigensolver dOI is based on a novel distributed matrix-matrix multiplication algorithm and on an extension of a distributed QR factorization algorithm proposed earlier. We illustrate the advantages of dOI in terms of higher flexibility with respect to the underlying network and lower communication cost compared to a related distributed eigensolver by Kempe and McSherry. Moreover, we experimentally illustrate how the overall communication cost of dOI is further reduced by adapting the accuracy of each distributed summation during the orthogonal iteration process.

关键词： distributed eigensolver distributed orthogonal iteration gossip-based algorithm push-sum algorithm randomized communication schedule

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A Multi-staining Digital Pathology image Registration Method Based on Global and Local Computing 27

A Multi-staining Digital Pathology Image Registration Method...

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27th IEEE/ACIS International conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing (SNPD)

作者： Ding, Yi Zhang, Xin Lu, Cheng Commun Univ China Sch Comp & Cyber Sci Beijing Peoples R China Guangdong Prov Peoples Hosp Dept Radiol Guangzhou Peoples R China

ISBN: (纸本)9798350391961;9798350391954

To accurately evaluate the patient's condition, medical workers usually need to register multiple pathological images of the lesion site samples. Using computer technology to assist in registration work can effectively improve the efficiency of doctors analyzing pathological images. One of the most advanced methods currently is the Virtual Alignment of Pathology image Series method, which is a multi-staining digital pathology image registration method that combines global and local calculations. However, this method may encounter certain biases when processing images with significant angle differences. Through a detailed analysis of this method, this article proposes an improvement plan which optimizes the acquisition of non-rigid registration mask images, enabling the method to obtain mask images more reasonably and achieve better registration results for images with significant angle differences. This provides more accurate judgment basis and helps doctors diagnose and develop treatment plans more accurately.

关键词： digital pathological images global rigid registration local non-rigid registration feature point detection

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parallelization of subdomain methods with overlapping for linear and nonlinear convection-diffusion problems. 11

Parallelization of subdomain methods with overlapping for li...

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11th Euromicro conference on parallel, distributed and Network-Based processing

作者： Spiteri, P Guivarch, R El Baz, D Ming, C IRIT LIMA ENSEEIHT F-31071 Toulouse France

ISBN: (纸本)0769518753

Linear and nonlinear convection-diffusion problems are considered. The numerical solution of these problems via the Schwarz alternating method is studied A new class of parallel asynchronous iterative methods with flexible communication is applied. The implementation of parallel asynchronous and synchronous algorithms on distributed memory multiprocessors is described Experimental results obtained on an IBM SP2 by using PVM are presented and analyzed. The interest of asynchronous iterative methods with flexible communication is clearly shown.

关键词： convection-diffusion problems parallel asynchronous iterations subdomain methods

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SVM-based real-time hyperspectral image classifier on a manycore architecture

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JOURNAL OF SYSTEMS ARCHITECTURE 2017年 80卷 30-40页

作者： Madronal, D. Lazcano, R. Salvador, R. Fabelo, H. Ortega, S. Callico, G. M. Juarez, E. Sanz, C. Univ Politecn Madrid Ctr Software Technol & Multimedia Syst CITSEM Madrid Spain ULPGC Res Inst Appl Microelect IUMA Las Palmas Gran Canaria Spain

This paper presents a study of the design space of a Support Vector Machine (SVM) classifier with a linear kernel running on a manycore MPPA (Massively parallel Processor Array) platform. This architecture gathers 256 cores distributed in 16 clusters working in parallel. This study aims at implementing a real-time hyperspectral SVM classifier, where real-time is defined as the time required to capture a hyperspectral image. To do so, two aspects of the SVM classifier have been analyzed: the classification algorithm and the system parallelization. On the one hand, concerning the classification algorithm, first, the classification model has been optimized to fit into the MPPA structure and, secondly, a probability estimation stage has been included to refine the classification results. On the other hand, the system parallelization has been divided into two levels: first, the parallelism of the classification has been exploited taking advantage of the pixel-wise classification methodology supported by the SVM algorithm and, secondly, a double-buffer communication procedure has been implemented to parallelize the image transmission and the cluster classification stages. Experimenting with medical images, an average speedup of 9 has been obtained using a single-cluster and double-buffer implementation with 16 cores working in parallel. As a result, a system whose processing time linearly grows with the number of pixels composing the scene has been implemented. Specifically, only 3 mu s are required to process each pixel within the captured scene independently from the spatial resolution of the image. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Support Vector Machine Hyperspectral imaging Massively parallel processing Real-time processing Energy consumption awareness Embedded system

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Ultra-fast tomographic reconstruction with a highly optimized Weighted Back-Projection algorithm

Ultra-fast tomographic reconstruction with a highly optimize...

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

作者： Agulleiro, J. I. Garzon, E. M. Garcia, I. Fernandez, J. J. Univ Almeria Dept Comp Architecture & Elect Almeria 04120 Spain

ISBN: (纸本)9780769539393

Electron tomography (ET) allows elucidation of the three-dimensional (3D) structure of large complex biological specimens at molecular resolution. In order to achieve such resolution levels, large projection images have to be used to compute the 3D reconstructions. Tomographic reconstruction on this scale requires a tremendous use of computational resources and a considerable processing time. In this work, we present and evaluate a highly optimized implementation of the Weighted Back-Projection reconstruction algorithm. Briefly, optimizations made to the code comprise (1) vector processing with SSE (Streaming SIMD Extensions) instructions, (2) an efficient use of cache memory, (3) to take advantage of the inherent image symmetry, (4) to use the FFTW (Fastest Fourier Transform in the West) library for image filtering, (5) to use regions of interest and last, but not least, (6) a wide range of minor optimizations like some data pre-calculations or an instruction level parallelism improvement. We have evaluated the method on tomographic reconstructions of several datasets and on two computing platforms. The results show that our version speeds up the method by a factor around 14 or 16, depending on the platform.

关键词： 3D image reconstruction Back-Projection Code optimization Tomography Vectorization

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Towards a Scalable parallel Infomap Algorithm for Community Detection 32

Towards a Scalable Parallel Infomap Algorithm for Community ...

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32nd Euromicro International conference on parallel, distributed and Network-Based processing (PDP)

作者： Santos, Gabriel G. Lakhotia, Kartik De Rose, Cesar A. F. Pontificia Univ Catolica Rio Grande do Sul Sch Technol Porto Alegre Brazil Intel Labs Santa Clara CA USA

ISBN: (纸本)9798350363074;9798350363081

Identifying Community structures is a fundamental problem in graph analysis. To detect communities in massive contemporary graphs, researchers have extensively explored shared- and distributed-memory parallel algorithms for several methods including Louvain Modularity Optimization and Label Propagation. The widely used Infomap algorithm based on Map Equation Framework (MEF) is known to provide better quality results than other approaches. However, research on parallel community detection using MEF or Infomap is extremely sparse when compared to other methods. We present a comprehensive characterization of Infomap and some of its known parallel implementations to facilitate research into parallel algorithms based on MEF. Most implementations take simple parallelization approaches, leaving strategies used to parallelize similar algorithms such as Louvain untouched. We highlight the scalability limitations of current implementations and implement and evaluate optimizations for MEF based parallel community detection that achieved up to 119% improvement on the overall speedup across the tested datasets.

关键词： community detection graph algorithms parallel computing

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