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检索条件"机构=Parallel and Distributed Processing Center"
91 条 记 录,以下是41-50 订阅
排序:
PCGraph: Accelerating GNN Inference on Large Graphs via Partition Caching
PCGraph: Accelerating GNN Inference on Large Graphs via Part...
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IEEE International Conference on Big Data and Cloud Computing (BdCloud)
作者: Lizhi Zhang Zhiquan Lai Yu Tang Dongsheng Li Feng Liu Xiaochun Luo National Laboratory for Parallel and Distributed Processing (PDL) Computer College National University of Defence Technology Changsha China PLA News Media Center Beijing China
Graph neural networks (GNNs) have been emerging as powerful learning tools for unstructured data and successfully applied to many graph-based application domains. Sampling-based GNN inference is commonly adopted in ex... 详细信息
来源: 评论
A chronological history-based execution time estimation model for embarrassingly parallel applications on grids
A chronological history-based execution time estimation mode...
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3rd International Symposium on parallel and distributed processing and Applications, ISPA 2005
作者: Yang, Chao-Tung Shih, Po-Chi Lin, Cheng-Fang Hsu, Ching-Hsien Li, Kuan-Ching High Performance Computing Laboratory Department of Computer Science and Information Engineering Tunghai University Taichung 40704 Taiwan Department of Computer Science and Information Engineering Chung Hua University Hsinchu 300 Taiwan Parallel and Distributed Processing Center Department of Computer Science and Information Management Providence University Taichung 43301 Taiwan
In order to identify and schedule jobs that are suitable for determined resources, an execution time estimation model is required. In this paper, it is described a Chronological history-based execution time estimation... 详细信息
来源: 评论
Performance analysis of applying replica selection technology for Data Grid environments
Performance analysis of applying replica selection technolog...
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8th International Conference on parallel Computing Technologies, PaCT 2005
作者: Yang, Chao-Tung Chen, Chun-Hsiang Li, Kuan-Ching Hsu, Ching-Hsien High-Performance Computing Laboratory Department of Computer Science and Information Engineering Tunghai University Taichung 40704 Taiwan Parallel and Distributed Processing Center Department of Computer Science and Information Management Providence University Taichung 43301 Taiwan Department of Computer Science and Information Engineering Chung Hua University Hsinchu 300 Taiwan
The Data Grid enables the sharing, selection, and connection of a wide variety of geographically distributed computational and storage resources for solving large-scale data intensive scientific applications. Such tec... 详细信息
来源: 评论
Visuel: A novel performance monitoring and analysis toolkit for cluster and grid environments
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6th International Conference on Algorithms and Architectures for parallel processing, ICA3PP
作者: Li, Kuan-Ching Cheng, Hsiang-Yao Yang, Chao-Tung Hsu, Ching-Hsien Wang, Hsiao-Hsi Hsu, Chia-Wen Hung, Sheng-Shiang Chang, Chia-Fu Liu, Chun-Chieh Pan, Yu-Hwa Parallel and Distributed Processing Center Department of Computer Science and Information Management Providence University Taichung 43301 Taiwan High Performance Computing Laboratory Department of Computer Science and Information Engineering Tunghai University Taichung 40704 Taiwan Department of Computer Science and Information Engineering Chung Hua University Hsinchu 300 Taiwan
The computing power provided by high performance low-cost PC-based Cluster and Grid platforms are attractive, and they are equal or superior to supercomputers and mainframes widely available. In this research paper, w... 详细信息
来源: 评论
A Heterogeneous Processor Design for CNN-Based AI Applications on IoT Devices  8
A Heterogeneous Processor Design for CNN-Based AI Applicatio...
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8th International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2019
作者: Liu, Zhiqiang Jiang, Jingfei Lei, Guoqing Chen, Kai Qin, Buyue Zhao, Xiaoqiang Artificial Intelligence Research Center National Innovation Institute of Defense Technology Beijing China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China College of System Engineering National University of Defense Technology Changsha China
This paper proposes a heterogeneous processor design for CNN-based AI applications on IoT devices. The heterogeneous processor contains an embedded RISC-V CPU that works as a general processor and an efficient CNN-acc... 详细信息
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A Dynamic Replication Mechanism to Reduce Response-Time of I/O Operations in High Performance Computing Clusters
A Dynamic Replication Mechanism to Reduce Response-Time of I...
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IEEE International Conference on Social Computing (SocialCom)
作者: Ehsan Mousavi Khaneghah Seyedeh Leili Mirtaheri Lucio Grandinetti Amir Saman Memaripour Mohsen Sharifi Center of High Performance Computing for Parallel and Distributed Processing University of Calabria Rende Italy School of Computer Engineering Iran University of Science and Technology Tehran Iran
Extraordinary large datasets of high performance computing applications require improvement in existing storage and retrieval mechanisms. Moreover, enlargement of the gap between data processing and I/O operations'... 详细信息
来源: 评论
Controllable Template Generation for Document-level Event Extraction
Controllable Template Generation for Document-level Event Ex...
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Neural Networks, Information and Communication Engineering (NNICE), International Conference on
作者: Quntian Fang Feng Liu Zhen Huang Zhenliang Guo Changjian Wang Dongsheng Li Minghao Hu National Key Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha China Information Research Center Academy of Military Sciences Beijing China
Document-level event extraction task has achieved significant progress based on template generation methods. However, there is no reasonable regulation and restriction in the existing template-based generation methods...
来源: 评论
A mathematical model for empowerment of Beowulf clusters for exascale computing
A mathematical model for empowerment of Beowulf clusters for...
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International Conference on High Performance Computing & Simulation (HPCS)
作者: Seyedeh Leili Mirtaheri Ehsan Mousavi Khaneghah Lucio Grandinetti Mohsen Sharifi Center of High Performance Computing for Parallel and Distributed Processing University of Calabria Rende Italy School of Computer Engineering Iran University of Science and Technology Tehran Iran
High-performance computing (HPC) clusters are currently faced with two major challenges - namely, the dynamic nature of new generation of applications and the heterogeneity of platforms - if they are going to be usefu... 详细信息
来源: 评论
A Wallet-Size Cluster for H.264 Encoding
A Wallet-Size Cluster for H.264 Encoding
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International Conference on Information Technology and Applications, ICITA
作者: Liang-Teh Lee Chia-Ying Tseng Kang-Yuan Liu Kuan-Ching Li Department of Computer Science and Engineering Tatung University Taipei Taiwan Parallel and Distributed Processing Center Department of Computer Science and Information Management Providence University Taichung Taiwan
A scalable wallet-size cluster computing system has been proposed to obtain low cost, low power consumption and small sized digital home processing center. In this paper, we apply the wallet-size cluster for H.264 enc... 详细信息
来源: 评论
Breaking up is hard to do - Dispersion: from design to implementation
Breaking up is hard to do - Dispersion: from design to imple...
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IEEE International Conference on Robotics and Automation (ICRA)
作者: M. Isaacs M. Anderson S. Ashworth J. Blackburn-Lynch B. Bynum J. Pearce C. Pemberton Department of Mathematics and Computer Science Berea College Mobile Robotics and Parallel Processing Laboratory Berea KY USA Center for Distributed Robotics University of Minnesota Minneapolis MN USA
This article assesses the performance of a virtual pheromone-based sectored dispersion algorithm which is implemented on a low-cost miniature mobile robotic platform using sonar sensing. The approach is a distributed ... 详细信息
来源: 评论