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检索条件"任意字段=10th International Conference on Algorithms and Architectures for Parallel Processing"

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Scheduling the parallel Execution of Workflows in Cloud Computing Based on the Imperialist Competitive and Genetic algorithms 18

Scheduling the Parallel Execution of Workflows in Cloud Comp...

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2nd international conference on Cloud and Big Data Computing (ICCBDC) / 7th international conference on Intelligent Information processing (ICIIP)

作者： Paridar, Farzad Majma, Mohammadreza Maeen, Mehrdad Islamic Azad Univ Dept IT Engn Elect Branch Tehran Iran Islamic Azad Univ Pardis Branch Dept Comp Engn Pardis Iran Yadegar E Imam Khomeini RAH Shahre Rey Branch Dept Comp Engn Tehran Iran

ISBN: (纸本)9781450364744

Cloud computing can be regarded as one of the best solutions to information technology problems. Most of the proposed scheduling algorithms try to minimize the execution time without considering the cost of accessing resources and mainly target environments resembling a community network. However, workflows were executed parallely in this paper to improve scheduling in cloud computing. For this purpose, the imperialist competitive and genetic algorithms were used. According to the simulation, the proposed algorithm produced better results of cost minimization in comparison with other algorithms. Finally, by applying an efficient scheduling process to the virtual machines running on the cloud, a number of parameters including system efficiency, response time and number of migration more than 10% optimized.

关键词： Cloud Computing parallel Scheduling of Workflows Imperialist Competitive Algorithm Genetic Algorithm

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GPU Based Real-Time Enhancement of High Resolution Image 10

GPU Based Real-Time Enhancement of High Resolution Image

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10th international conference on Information Optics and Photonics

作者： Huang, Maosen Cheng, Kuanhong Zhou, Huixin Yu, Yue Zhang, Zhe Tan, Wei Xidian Univ Sch Phys & Optoelect Engn Xian 710071 Shaanxi Peoples R China

ISBN: (纸本)9781510625808

Curvature filter and gradient transform based image enhancement algorithm can effectively suppress noises and enhance image edges. However, it is very hard to be carried out in real time due to the large computing load. To address this problem, a GPU based parallel implementation is proposed in this paper. First, aiming at the characteristics of the algorithm, a numerical implementation method based on central-difference is proposed. then a domain decomposition scheme is utilized in parallel Gaussian curvature filter to remove the dependence of neighboring pixels and guarantee convergence. Finally, we make the multiprocessor wrap occupancy reach 100% by optimizing the thread grid and register usage. Experimental results demonstrate that our parallel method runs 200-300 times faster than CPU serial method with real time processing of 4096x4096 resolution image, which indicates a great potential for application.

关键词： Image enhancement Image filtering parallel computing Graphics processing Unit Real time processing

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Network-Aware Grouping in Distributed Stream processing Systems 18th

Network-Aware Grouping in Distributed Stream Processing Syst...

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18th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Chen, Fei Wu, Song Jin, Hai Huazhong Univ Sci & Technol Sch Comp Sci & Technol Serv Comp Technol & Syst Lab Cluster & Grid Comp Lab Wuhan 430074 Peoples R China

ISBN: (纸本)9783030050511;9783030050504

Distributed Stream processing (DSP) systems have recently attracted much attention because of their ability to process huge volumes of real-time stream data with very low latency on clusters of commodity hardware. Existing workload grouping strategies in a DSP system can be classified into four categories (i.e. raw and blind, data skewness, cluster heterogeneity, and dynamic load-aware). However, these traditional stream grouping strategies do not consider network distance between two communicating operators. In fact, the traffic from different network channels makes a significant impact on performance. How to grouping tuples according to network distances to improve performance has been a critical problem. In this paper, we propose a network-aware grouping framework called Squirrel to improve the performance under different network distances. Identifying the network location of two communicating operators, Squirrel sets a weight and priority for each network channel. It introduces Weight Grouping to assign different numbers of tuples to each network channel according to channel's weight and priority. In order to adapt to changes in network conditions, input load, resources and other factors, Squirrel uses Dynamic Weight Control to adjust network channel's weight and priority online by analyzing runtime information. Experimental results prove Squirrel's effectiveness and show that Squirrel can achieve 1.67x improvement in terms of throughput and reduce the latency by 47%.

关键词： Stream processing Load balancing Grouping Network distance

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Proceedings of 2018 10th international conference on Information Technology and Electrical Engineering: Smart Technology for Better Society, ICITEE 2018

Proceedings of 2018 10th International Conference on Informa...

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10th international conference on Information Technology and Electrical Engineering, ICITEE 2018

ISBN: (纸本)9781538647394

the proceedings contain 111 papers. the topics discussed include: body weight measurement using image processing based on body surface area and elliptical tube volume;a case study of mutual impedance effect in parallel transmission lines under maintenance condition;slot-less tubular linear permanent magnet generator using Halbach-array excitation;CNN based posture-free hand detection;technology web 2.0 as intervention media: technology organization environment and socio-technical system perspective;development of enterprise security framework in SKK Migas based on integration of ISO 27000 and SABSA model;detection of induction motor bearing damage with starting current analysis using wavelet discrete transform and artificial neural network;and design of LPG injection mechanism with PID control for 1 kVA generator set.

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Efficient implementation of Flux Reconstruction schemes for the simulation of compressible viscous flows on Graphics processing Units 10

Efficient implementation of Flux Reconstruction schemes for ...

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10th international conference on Computational Fluid Dynamics, ICCFD 2018

作者： Gisbert, F. Bolinches-Gisbert, M. Pueblas, J. Corral, R. ITP Aero Spain Universitat Rovira i Virgili Spain Universidad Politécnica de Madrid Spain

this paper describes the strategies followed to implement a nodal-based Discontinuous Galerkin Flux Reconstruction solver (DGFR) for the compressible Navier-Stokes equations on hybrid unstructured grids, that is going to be executed on multiple Graphics processing Units (GPUs). DGFR schemes exhibit some features, such as the strong data locality, that make them good candidates to be executed efficiently on a GPU. the solver has been implemented using a mixture of C++ and OpenCL. the implementation details of the most time consuming parts of the solver are presented. It is demonstrated that the use of the GPU local memory has a strong impact on the solver performance. When properly adjusted, it is shown that all the computationally relevant parts of the solver are memory bound for the GPUs used in this work. the parallel efficiency of the code is measured running first on a single GPU and then running on multiple GPUs of a cluster. In the first case, the efficiency of the GPU does not depend on the case size for large enough cases. However, when the number of mesh cells decreases below a certain threshold, the GPU performance decrease is substantial. When multiple GPUS are used in parallel, the communication between them is carried using MPI. the penalty due to the communication of data between GPUs has a large detrimental effect on the parallel speed-up, even in case the communications and computations overlap. the resulting solver is fast and precise enough to be used as an industrial tool for analysis for common turbomachinery problems. © ICCFD 2018.

关键词： Graphics processing unit

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Enhancing the Performance of an Image Steganalysis Approach Using Variable Batch Size-Based CNN on GPUs

Enhancing the Performance of an Image Steganalysis Approach ...

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IEEE international conference on Intelligent Data Acquisition and Advanced Computing Systems

作者： Eslam M. Mustafa Mohamed M. Fouad Mohamed A. Elshafey Military Technical College Cairo Egypt

Blind image steganalysis is defined as the binary classification problem of predicting whether or not an image contains an embedded message. With the development of steganography, extracting powerful features from the stego-images becomes a challenge. Recently, convolutional Neural Networks (CNNs) are presented as a promising solution for such a challenge. Unlike traditional steganalysis approaches, CNN-based steganalysis approaches have the ability of extracting features automatically from input images. With such an ability, there is no need to handcraft feature extractors like those used by traditional steganalysis approaches. Despite its long clinical success, CNN-based steganalysis approaches are time consuming. Training on those approaches may stand for days and sometimes for weeks. It is necessary to accelerate the training on CNN-based approaches to make them more usable in practice, especially for some real-time applications. the purpose of this paper is to implement an enhanced version of the improved Gaussian-Neuron CNN (IGNCNN) steganalysis approach on GPUs, and to profiteer the parallel power of GPUS. In this paper two approaches for parallelizing the CNN training process are proposed. the first is to apply the concept of data parallelism with the feature extraction module and the second is to apply model parallelism with the classification module. Besides the parallelization approaches, a variable batch size is implemented as an optimization approach. Using a big batch size in fully-connected layers leads to faster convergence to a better minima, but it may negatively affect the accuracy. the results of the proposed approach show that it outperforms the IGNCNN in terms of accuracy and performance metrics.

关键词： parallel processing Training Data models Graphics processing units Feature extraction Computational modeling Acceleration

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Proving the Preclusion of Data Manipulation Using parallel Data Acquisition in Chromatography 10th

Proving the Preclusion of Data Manipulation Using Parallel D...

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10th international conference on processing and Manufacturing of Advanced Materials processing, Fabrication, Properties, Applications (thERMEC)

作者： Marehn, David thomas Wilhelm, Detlef Pospisil, Heike Pizzoferrato, Roberto AnaTox GmbH & Co KG D-15517 Fuerstenwalde Germany TUAS Wildau High Performance Comp Life Sci D-15745 Wildau Germany Univ Roma Tor Vergata I-00133 Rome Italy

ISBN: (纸本)9783035712087

Traceability has an enormous value for companies, but especially for those working in the regulated environment. It plays a special role in the field of pharmacy with respect to manufacturing, controlling and distributing batches of drugs. through the guidance of Good Manufacturing Practice (GMP) traceability should be ensured. An increasing number of pharmaceutical companies are member of one of the global pharmacopoeias (United States Pharmacopeia, European Pharmacopeia and Japanese Pharmacopeia). the specifications of these pharmacopoeias describe the best practice in documentation, control, qualification and risk management. But however, the pharmacopoeias are written very generally and do not distinguish between the vendors of the analytical instruments. Here, we analyze how chromatographic analyses and data acquisition rely on a specific vendor of the device and the chromatography data system (CDS), the controlling software. We present a way to compare the data acquisition of different CDSs communicating with HPLC instruments. A newly developed software called Data Collector allows the acquisition of data from a HPLC detector parallel to the controlling CDS in the same run. Two HPLC systems and two different CDSs using a well defined sample standard have been tested. the direct comparison of the acquired data precludes unexpected data manipulations of both tested CDSs and shows that there are primarily deviations between the CDSs due to time variations only which depend on the sampling rate. All in all the Data Collector can be used for the traceability of data acquisition.

关键词： Chromatography Data Acquisition parallelism Pharmacopoeia

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Proceedings of the 44th international conference on Very Large Data Bases, VLDB January 2018

Proceedings of the 44th International Conference on Very Lar...

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44th international conference on Very Large Data Bases, VLDB 2018

this proceedings contains 10 papers. the Proceedings of the VLDB Endowment (PVLDB) provides a high-quality publication service to the data management research community. Issue 5 contains a variety of topics ranging from transactional databases and parallel architectures for traditional data processing and for machine learning, advanced indexing including range indices and R-trees, storage, and privacy. the key terms of this proceedings include practical differential privacy, structured storage, non-volatile memory, latch-free range index, parameter server architecture, hierarchical data, flexible parallelism control, throughput Optimizing Adaptive Index (TOAIN), machine learning workloads, contention-aware lock scheduling. © 2018 VLDB Endowment 21508097/18/01.

关键词： parallel architectures

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Container-Based Complex Programming Skills Training Platform

Container-Based Complex Programming Skills Training Platform

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IEEE international conference on Software Engineering and Service Sciences (ICSESS)

作者： Wei Wang Tao Wang Gang Yin National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha Hunan Province China

ISBN: (数字)9781728109459

ISBN: (纸本)9781728109466

In recent years, online programming education and evaluation have developed rapidly. the traditional online education platform focuses on the teaching of knowledge, lacking a good evaluation platform to assess the learning outcomes, while the existing online evaluation platform only focuses on the evaluation of algorithms or languages. Until now, no platform can support the training of engineering-level complex technologies. In this paper, we present our solution - Educoder. Educoder is an one-stop service platform that integrates “teaching”, “learning” and “use”. It not only supports simple programming language training type, but also supports the training of complex technology such as Hadoop, Spring, Machine Learning, Docker, etc. the code environment for student learning and evaluation is managed by Gitlab, and the runtime environment is provided by Kubernetes pod, which guarantees good isolation. For complex types of training, we provide the Docker Web Terminal module for SSH connection to enter the pod for operation and use docker in docker technology to provide support for training that requires multiple services or multiple nodes. In Educoder, we use scripts to serialize the training evaluation steps, and we formed a private evaluation script template library for training teacher to reference, which greatly reduced the time cost of writing a new training course.

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parallel k-means Clustering of Geospatial Data Sets Using Manycore CPU architectures 18

Parallel <i>k</i>-means Clustering of Geospatial Data Sets U...

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18th IEEE international conference on Data Mining Workshops (ICDMW)

作者： Mills, Richard Tran Sripathi, Vamsi Kumar, Jitendra Sreepathi, Sarat Hoffman, Forrest M. Hargrove, William W. Argonne Natl Lab Math & Comp Sci Div Lemont IL 60439 USA Intel Corp Data Ctr Grp Hillsboro OR USA Oak Ridge Natl Lab Div Environm Sci POB 2008 Oak Ridge TN 37831 USA Oak Ridge Natl Lab Comp Sci & Math Div Oak Ridge TN USA Oak Ridge Natl Lab Computat Sci & Engn Div Oak Ridge TN USA US Forest Serv So Res Stn USDA Asheville NC USA

ISBN: (纸本)9781538692882

the increasing availability of high-resolution geospatiotemporal data sets from sources such as observatory networks, remote sensing platforms, and computational Earth system models has opened new possibilities for knowledge discovery and mining of weather, climate, ecological, and other geoscientific data sets fused from disparate sources. Many of the standard tools used on individual workstations are impractical for the analysis and synthesis of data sets of this size;however, new algorithmic approaches that can effectively utilize the complex memory hierarchies and the extremely high levels of parallelism available in state-of-the-art high-performance computing platforms can enable such analysis. Here, we describe pKluster, an open-source tool we have developed for accelerated k-means clustering of geospatial and geospatiotemporal data, and discuss algorithmic modifications and code optimizations we have made to enable it to effectively use parallel machines based on novel CPU architectures-such as the Intel Knights Landing Xeon Phi and Skylake Xeon processors-with many cores and hardware threads, and employing significant single instruction, multiple data (SIMD) parallelism. We outline some applications of the code in ecology and climate science contexts and present a detailed discussion of the performance of the code for one such application, LiDAR-derived vertical vegetation structure classification.

关键词： Instruction sets parallel processing Acceleration Computer architecture Clustering algorithms Geoscience

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