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BL
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| Record Number
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862202
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| Main Entry
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ISC High Performance (Conference)(34th :2019 :, Frankfurt am Main, Germany)
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| Title & Author
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High performance computing : : 34th International Conference, ISC High Performance 2019, Frankfurt/Main, Germany, June 16-20, 2019, Proceedings /\ edited by Mich?le Weiland, Guido Juckeland, Carsten Trinitis, Ponnuswamy Sadayappan.
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| Publication Statement
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Cham, Switzerland :: Springer,, 2019.
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| Series Statement
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Lecture notes in computer science ;; 11501
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LNCS sublibrary. SL 1, Theoretical computer science and general issues
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| Page. NO
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1 online resource (xvi, 352 pages) :: illustrations (some color)
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| ISBN
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3030206564
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: 9783030206567
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9783030206550
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| Notes
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Includes author index.
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International conference proceedings.
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| Contents
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Intro; Preface; Organization; Contents; Architectures, Networks and Infrastructure; Evaluating Quality of Service Traffic Classes on the Megafly Network; 1 Introduction; 2 Exploring Quality of Service on HPC Networks; 3 Evaluation Methodology; 3.1 HPC Simulation Environment; 3.2 Topology and Routing Description; 3.3 Network Configurations; 3.4 Workloads; 3.5 Rank-to-Node Mappings; 4 Quantifying Interference on 1-D Dragonfly and Megafly Networks; 5 Evaluating Quality of Service on Megafly Networks; 5.1 QoS Mechanism I: Prioritizing Entire Applications
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5.1 Evaluation Using Synthetic Data Analysis5.2 Evaluation for SLOPE and Spark Using Real Applications; 6 Related Work; 7 Conclusions and Future Work; References; A Near-Data Processing Server Architecture and Its Impact on Data Center Applications; 1 Introduction; 2 Related Work; 3 NDP Server Architecture; 3.1 The Architecture of a Conventional Server; 3.2 The New NDP Server Architecture; 4 Implementations; 4.1 Implementation Methodology; 4.2 Implementation of SANS and SFNS; 4.3 Implementation of the Applications; 5 Evaluation; 5.1 Evaluation of FFT & LC & HE
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5.2 QoS Mechanism II: Prioritizing and Guaranteeing Bandwidth to Latency-Sensitive Operations5.3 Applying QoS Mechanisms to Multiple Application Workloads in Parallel; 6 Related Work; 7 Discussion and Conclusion; References; Artificial Intelligence and Machine Learning; Densifying Assumed-Sparse Tensors; 1 Introduction; 2 Background; 3 Issues with Scaling the Transformer Model; 4 Densifying Assumed-Sparse Tensors; 5 Experimental Results; 5.1 Weak Scaling Performance; 5.2 Strong Scaling; 5.3 Model Accuracy; 6 Discussion; 7 Future Work and Conclusion; References
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Data, Storage and VisualizationSLOPE: Structural Locality-Aware Programming Model for Composing Array Data Analysis; 1 Introduction; 2 Preliminaries; 2.1 Multidimensional Array; 2.2 User-Defined Function and Programming Model; 3 SLOPE Programming Model; 3.1 Abstract Data Type-Stencil; 3.2 SLOPE Programming Model; 3.3 Example Data Analysis Using SLOPE; 4 Parallel Execution Engine; 4.1 Overview of Parallel Execution Engine; 4.2 Data Partitioning and Halo Layer; 4.3 Data and Computing Scheduling; 4.4 Output Array Dimension; 4.5 Advanced Features; 4.6 Implementation of SLOPE; 5 Evaluation
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Learning Neural Representations for Predicting GPU Performance1 Introduction; 2 Background and Motivation; 2.1 Related Work; 2.2 Explicit Features; 2.3 Representation Learning; 2.4 Collaborative Filtering; 3 Prediction Model; 3.1 Multi-layer Perceptron Model; 3.2 Multiple Training Objectives; 3.3 Automated Architecture Search; 4 Experiment Setup; 4.1 Machine Specification; 4.2 Benchmarks; 4.3 Methodology; 5 Results and Discussions; 5.1 Performance of Matrix Factorization (R1); 5.2 Performance of Multi-layer Perceptron (R2); 5.3 Training with Additional Metrics (R3); 6 Conclusions; References
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| Abstract
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This book constitutes the refereed proceedings of the 34th International Conference on High Performance Computing, ISC High Performance 2019, held in Frankfurt/Main, Germany, in June 2019. The 17 revised full papers presented were carefully reviewed and selected from 70 submissions. The papers cover a broad range of topics such as next-generation high performance components; exascale systems; extreme-scale applications; HPC and advanced environmental engineering projects; parallel ray tracing - visualization at its best; blockchain technology and cryptocurrency; parallel processing in life science; quantum computers/computing; what's new with cloud computing for HPC; parallel programming models for extreme-scale computing; workflow management; machine learning and big data analytics; and deep learning and HPC. --
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| Subject
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High performance computing, Congresses.
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| Subject
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Supercomputers, Congresses.
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| Subject
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High performance computing.
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| Subject
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Supercomputers.
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| Dewey Classification
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004.1/1
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| LC Classification
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QA76.88.I83 2019eb
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| Added Entry
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Juckeland, Guido
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Sadayappan, P.
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Trinitis, Carsten
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Weiland, Michèle
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| Parallel Title
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ISC High Performance 2019
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