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共 511 条记录，以下是341-350 订阅

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A GA-Based Energy Aware Virtual Machine Placement Algorithm for Cloud Data Centers

A GA-Based Energy Aware Virtual Machine Placement Algorithm ...

引用

international symposium on parallel architectures, algorithms and programming (PAAP)

作者： Xiaodong Wu School of Mathmatics and Computer Science Quanzhou Normal University Quanzhou China Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University Quanzhou China

ISBN: (纸本)9781665496407

Virtual machine placement (VMP) is among the important techniques to improve the energy efficiency of green cloud data centers. To address this issue, bionic optimization algorithms have been widely used. One of the most effective methods is to use the genetic algorithm (GA) to find the best or near-optimal solution for the VMP problem. In this paper, a GGA-based algorithm is proposed to solve the energy-aware VMP problem. The algorithm regards the VMP problem as BPP and strives to reduce the number of active hosts in order to reduce the system energy consumption. Further, since it is important to find the solution of VMP in a short time especially in the online scenario, several optimization strategies are developed and applied in the proposed algorithm to improve the convergence speed of the algorithm and reduce the execution time of the algorithm. The experimental results demonstrate that compared with the traditional genetic algorithm, our method can effectively decrease the number of iterations and improve the speed of seeking optimal solutions.

关键词： Data centers Cloud computing Energy consumption Heuristic algorithms Simulation programming Virtual machining

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Efficient 1D Barcode Localization Method for Imagery Shipping Label Using Deep Learning Models

Efficient 1D Barcode Localization Method for Imagery Shippin...

引用

international symposium on parallel architectures, algorithms and programming (PAAP)

作者： Lang-chao Qiao Jin-lu Wang Bao-hong Gao Xin-gang Yang Wen-tao Feng Yu-xiao Zhang Yan Wang Hai Liu Wei Liu Division of Monopoly Administratin Shaanxi Branch China National Tobacco Corporation Xi'an China Gaoxin District Branch Xi'an Tobacco Monopoly Administration Xi'an China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (纸本)9781665496407

In this paper, an efficient approach for imagery shipping label 1D barcode localization integrated with two deep learning models is proposed. In the first stage, a Faster R-CNN model is adopted to detect the barcode region existed in the imagery shipping label automatically. Then orientation of parallel lines of 1D barcode can be computed by using Line Segment Detector algorithm. Next, the tilt angle of the image will be corrected using the direction information for rotation correction. Finally, in order to decode barcode accurately in the subsequent stage after barcode localization, a Resnet-34 model was used as a multi-class classifier to determinate the orientation of the shipping label and calibrate the whole image. Using a dataset that contains a total of 1830 imagery shipping labels including slanted ones or ones with complex background, our method can yield a barcode localization accuracy of 0.9866, which demonstrates its encouraging performance.

关键词： Location awareness Deep learning Image segmentation Computational modeling Detectors programming parallel architectures

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Optimal loop scheduling with register constraints using flow graphs

Optimal loop scheduling with register constraints using flow...

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international symposium on parallel architectures, algorithms and Networks (ISPAN)

作者： J. Muller D. Fimmel R. Merker Department of Electrical Engineering Dresden University of Technology Dresden Germany

We present a novel loop scheduling approach using a generalized flow graph model of the resource constraints. From this model we derive a new flow graph to incorporate register constraints. Our linear programming implementation produces an optimum loop schedule, respecting the constraints on functional units and registers in a single optimization problem. Moreover, the iteration interval is treated as a rational number, and the approach supports heterogeneous processor architectures and pipelined functional units. Compared to earlier approaches, the solution can reduce the problem complexity and solution time, and provide faster loop schedules.

关键词： Flow graphs Registers Resource management Intelligent networks parallel architectures Digital filters Delay Adders Writing Pipelines

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A Heuristic SAT Problems Solving Method based on LSTM Network

A Heuristic SAT Problems Solving Method based on LSTM Networ...

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international symposium on parallel architectures, algorithms and programming (PAAP)

作者： Yichuan Wang Xiaolong Liang Xinhong Hei Lei Zhu Wenjiang Ji College of Computer Science and Engineering Xi'an University of Technology Xi'an China

ISBN: (纸本)9781665496407

Boolean Satisfiability Problem(SAT) is a well known determination Problem. *** proved in 1971 that SAT Problem is the first NP-Complete (NPC) Problem in the world, namely, any non-deterministic Polynomial (NP) Problem can be specified with the SAT Problem for solving in Polynomial time. Therefore, Boolean satisfiability problem (SAT) can be extended to many practical applications, such as integrated circuit design optimization, biological genes, artificial intelligence, Internet, etc. And there are a lot of NP problems in cryptography. Therefore, the study of SAT Problem is of great significance to cryptography. In this paper, we design a heuristic SAT solving algorithm based on LSTM (Long Short-Term Memory) recurrent neural networks. First of all, based on LSTM, we train the model as an end-to-end SAT Solver, which can output 0 or 1 to represent the satisfiability of each SAT problem. Then we extract the high-dimensional output of the LSTM network and carry out k-means clustering analysis. Finally, we assign the obtained results as the initial value of the variables of the SAT problem, and inspire two types of classical algorithms (complete algorithm and incomplete algorithm) to solve the SAT problem respectively, so as to accelerate the solving process. In the experiment, we demonstrated the test results of SAT problems with different sizes, and The results show that the LSTM-WalkSAT and LSTM-DPLL based heuristic SAT solving methods can significantly improve the solving efficiency of the original complete or incomplete SAT solving algorithms.

关键词： Integrated circuit synthesis Recurrent neural networks Heuristic algorithms Clustering algorithms programming parallel architectures Internet

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Automatic software generation in the OORHS distributed computing framework

Automatic software generation in the OORHS distributed compu...

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international symposium on parallel architectures, algorithms and Networks (ISPAN)

作者： D.S. Yeung A.K.Y. Wong Department of Computing Hong Kong Polytechnic University Hong Kong China

Software generation in the OORHS (object-oriented reciprocative hypercomputing system) is user-transparent. It addresses the issue of ease of use by minimizing the number of steps leading to a programming solution. The OORHS requires from the user only a high level APPL program, which is, in effect, a specification. For every APPL program, the system automatically performs all the necessary distributed computing steps. The precompiler, based on the object-oriented paradigm, instantiates the encapsulated program objects embedded in an APPL program. These program objects are distributed at the source level. They are compiled and then executed at the allocated sites. This unique approach, known as local compilation, eliminates the need to store the compilers used by other machines locally. It enhances the compatibility between the compiled program and the host processor. The precompiler generates a program objects dictionary for every APPL program. The contents in the dictionary facilitates program visualization.

关键词： Distributed computing Dictionaries Software libraries Load management parallel programming Visualization Embryo Fault tolerance Global communication parallel processing

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seventh international Conference on parallel and Distributed Computing, Applications and Technologies-Title

Seventh International Conference on Parallel and Distributed...

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IEEE international Conference on parallel and Distributed Computing, Applications and Technologies (PDCAT)

The following topics are dealt with: parallel computing; distributed computing; interconnection networks; communication and telecommunication; image processing and computer graphics; access control and authorization; wireless networks and mobile computing; database applications and data mining; parallelizing compilers; parallel/distributed architectures; intelligent computing and neural networks; Internet computing; network routing and communication algorithms; Web technologies; ubiquitous computing systems; task mapping and job scheduling; security protocols; computer networks; formal methods and programming languages; intrusion detection and survivability; grid computing systems; applied cryptography; P2P and ad hoc networks; and reliability and fault-tolerance

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Semantic Embedding Graph Convolutional Networks for Multi-label Video Segment Classification

Semantic Embedding Graph Convolutional Networks for Multi-la...

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international symposium on parallel architectures, algorithms and programming (PAAP)

作者： Zhitao Li Jianzong Wang Ning Cheng Jing Xiao Ping An Technology (Shenzhen) Co. Ltd.

ISBN: (纸本)9781665496407

Video classification is a challenging problem, video segment labels are sparse and expensive to get, and it is important to leverage as much available information as possible from labeled datasets. There have been several ways to capture video frame information but none of them have utilized the information hidden in labels correlation to increase classification accuracy. This work proposed a framework called Graph Convolution Semantic Network of aggregated descriptors (GCSN) which can extract neighboring information of related segment labels to increase video segmentation classification accuracy. Label relation graph was built by thresholding on cosine similarity computed from mutual embedding similarities, word embeddings were generated by Deep Bidirectional Transformers. The testing accuracy on Youtube-8m video segments classification dataset shows that our proposed GCSN outperforms NeXtVLAD baseline by considering additional labels relation information.

关键词： Training Convolution Semantics Neural networks Natural languages programming Transformers

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Optimizing communication for array operations on message-passing multicomputers

Optimizing communication for array operations on message-pas...

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international symposium on parallel architectures, algorithms and Networks (ISPAN)

作者： A. Eberhart Jingke Li Department of Computer Science Portland State University Portland OR USA

Whole array operations and array section operations are important features of many data-parallel languages. Efficient implementation of these operations on distributed-memory multicomputers is critical to the scalability and high-performance of data-parallel programs. We present an approach for analyzing communication patterns induced by array operations and for scheduling message flow based on the information. Our scheduling algorithm guarantees contention-free data transfer and utilizes network resources optimally. It incurs little overhead and is suitable to be used in compilers and in runtime libraries. We also present simulation results that demonstrate the algorithm's superiority to the asynchronous transfer mode that is commonly used for this type of communication.

关键词： Scheduling algorithm Pattern analysis Runtime library Computer science Scalability Information analysis Asynchronous transfer mode parallel processing parallel algorithms programming profession

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parallel Reconstruction for High Resolution Multi-frame Solar Speckle Images 1

引用

11th international symposium on parallel architectures, algorithms and programming, PAAP 2020

作者： Zhu, Lingxiao Jiang, Murong Fu, Pengming Chui, Wenhao School of Information Science and Engineering Yunnan University Kunming China

ISBN: (数字)9789811600104

ISBN: (纸本)9789811600098

The high resolution reconstruction of solar speckle image is an important research issue in the field of astronomical observation. Due to the influence of atmospheric turbulence, the astronomical images obtained by ground-based optical telescope will be blurred or degraded seriously, which needs to be reconstructed by image restoration method. Most of the existing regularization methods deal with a single frame image. The more image features are, the better the reconstruction effect is. However, the poor quality of the reconstructed image is caused by the insufficient features of a single solar speckle image. In this paper, we combined the complementary relationship between multi-frame speckle images to establish a multi-frame blind restoration model suitable for solar speckle image reconstruction, used genetic algorithm to select regularization parameters and realized multi-frame block reconstruction in parallel. The experimental results show that the proposed method can restore the solar speckle image well, and the reconstruction speed is fast, which can meet the requirements of astronomical observation. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Image reconstruction

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Data parallel programming Model for Many-Core architectures

Data Parallel Programming Model for Many-Core Architectures

引用

IEEE international symposium on parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Yongpeng Zhang North Carolina State University USA

Emerging accelerating architectures, such as GPUs, have proved successful in providing significant performance gains to various application domains. This is done by exploiting data parallelism in existing algorithms. However, programming in a data-parallel fashion imposes extra burdens to programmers, who are used to writing sequential programs. New programming models and frameworks are needed to reach a balance between programmability, portability and performance. We start from stream processing domain and propose GStream, a general-purpose, scalable data streaming framework on GPUs. The contributions of GStream are as follows: (1) We provide powerful, yet concise language abstractions suitable to describe conventional algorithms as streaming problems. (2) We project these abstractions onto GPUs to fully exploit their inherent massive data-parallelism. (3) We demonstrate the viability of streaming on accelerators. Experiments show that the proposed framework provides flexibility, programmability and performance gains for various benchmarks from a collection of domains, including but not limited to data streaming, data parallel problems, numerical codes and text search. This work lays a foundation to our future work to develop more general data parallel programming models for many-core architectures.

关键词： Graphics processing unit parallel processing Benchmark testing Libraries Kernel Computer architecture

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