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Titlebook: Network and Parallel Computing; 15th IFIP WG 10.3 In Feng Zhang,Jidong Zhai,Mateo Valero Conference proceedings 2018 IFIP International Fed

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書目名稱Network and Parallel Computing
副標題15th IFIP WG 10.3 In
編輯Feng Zhang,Jidong Zhai,Mateo Valero
視頻videohttp://file.papertrans.cn/663/662872/662872.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Network and Parallel Computing; 15th IFIP WG 10.3 In Feng Zhang,Jidong Zhai,Mateo Valero Conference proceedings 2018 IFIP International Fed
描述.This book constitutes the proceedings of the 15th IFIP International Conference on Network and Parallel Computing, NPC 2018, held in Muroran, Japan, in November/December 2018. ..The 22 full and 12 short papers presented in this volume were carefully reviewed and selected from 72 submissions. The papers cover traditional areas of network and parallel computing, including parallel applications, distributed algorithms, parallel architectures, software environments, and distributed tools..
出版日期Conference proceedings 2018
關(guān)鍵詞artificial intelligence; cloud computing; clustering; computer architecture; computer networks; computer
版次1
doihttps://doi.org/10.1007/978-3-030-05677-3
isbn_softcover978-3-030-05676-6
isbn_ebook978-3-030-05677-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightIFIP International Federation for Information Processing 2018
The information of publication is updating

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CNLoc: Channel State Information Assisted Indoor WLAN Localization Using Nomadic Access Points,ncertainty of nomadic APs. Our implementation and evaluation show that CNLoc can improve the accuracy with unknown location information of nomadic APs. We also discuss some open issues and new possibilities in future nomadic AP based indoor localization.
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ALOR: Adaptive Layout Optimization of Raft Groups for Heterogeneous Distributed Key-Value Stores,. and .. We conducted experiments on a practical heterogeneous cluster, and the results indicate that, on average, ALOR improves throughput by 36.89%, reduces latency and 99th percentile tail latency by 24.54% and 21.32%, respectively.
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GPU-Accelerated Clique Tree Propagation for Pouch Latent Tree Models,each model structure during PLTM training. Our experiments with real-world data sets show that the GPU-accelerated implementation procedure can achieve up?to 52x speedup over the sequential implementation running on CPUs. The experiment results signify promising potential for further improvement on the full training of PLTMs with GPUs.
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Data Fine-Pruning: A Simple Way to Accelerate Neural Network Training,g approach. Extensive experiments with different neural networks are conducted to verify the effectiveness of our method. The experimental results show that applying the data fine-pruning approach can reduce the training time by around 14.29% while maintaining the accuracy of the neural network.
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