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High Performance Computing

Overview of attention for book
Cover of 'High Performance Computing'

Table of Contents

  1. Altmetric Badge
    Book Overview
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    Chapter 1 Compiler-Assisted Type-Safe Checkpointing
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    Chapter 2 Static Analysis to Enhance Programmability and Performance in OmpSs-2
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    Chapter 3 Automatic Detection of MPI Assertions
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    Chapter 4 Automatic Code Motion to Extend MPI Nonblocking Overlap Window
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    Chapter 5 Complete Deep Computer-Vision Methodology for Investigating Hydrodynamic Instabilities
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    Chapter 6 Prediction of Acoustic Fields Using a Lattice-Boltzmann Method and Deep Learning
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    Chapter 7 Unsupervised Learning of Particle Image Velocimetry
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    Chapter 8 Reduced Order Modeling of Dynamical Systems Using Artificial Neural Networks Applied to Water Circulation
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    Chapter 9 Parameter Identification of RANS Turbulence Model Using Physics-Embedded Neural Network
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    Chapter 10 Investigating the Overhead of the REST Protocol When Using Cloud Services for HPC Storage
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    Chapter 11 Characterizing I/O Optimization Effect Through Holistic Log Data Analysis of Parallel File Systems and Interconnects
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    Chapter 12 The Importance of Temporal Behavior When Classifying Job IO Patterns Using Machine Learning Techniques
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    Chapter 13 GOPHER, an HPC Framework for Large Scale Graph Exploration and Inference
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    Chapter 14 Ensembles of Networks Produced from Neural Architecture Search
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    Chapter 15 SmartPred: Unsupervised Hard Disk Failure Detection
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    Chapter 16 Application IO Analysis with Lustre Monitoring Using LASSi for ARCHER
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    Chapter 17 AI-Driven Holistic Approach to Energy Efficient HPC
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    Chapter 18 Characterizing HPC Performance Variation with Monitoring and Unsupervised Learning
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    Chapter 19 Service Function Chaining Based on Segment Routing Using P4 and SR-IOV (P4-SFC)
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    Chapter 20 Seamlessly Managing HPC Workloads Through Kubernetes
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    Chapter 21 Interference-Aware Orchestration in Kubernetes
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    Chapter 22 RustyHermit: A Scalable, Rust-Based Virtual Execution Environment
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    Chapter 23 Rootless Containers with Podman for HPC
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    Chapter 24 Bioinformatics Application with Kubeflow for Batch Processing in Clouds
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    Chapter 25 Converging HPC, Big Data and Cloud Technologies for Precision Agriculture Data Analytics on Supercomputers
Overall attention for this book and its chapters
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

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8 X users


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High Performance Computing
Published by
Lecture notes in computer science, January 2020
DOI 10.1007/978-3-030-59851-8
978-3-03-059850-1, 978-3-03-059851-8

Heike Jagode, Hartwig Anzt, Guido Juckeland, Hatem Ltaief

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The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 March 2021.
All research outputs
of 24,226,848 outputs
Outputs from Lecture notes in computer science
of 8,180 outputs
Outputs of similar age
of 464,871 outputs
Outputs of similar age from Lecture notes in computer science
of 25 outputs
Altmetric has tracked 24,226,848 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,180 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done well, scoring higher than 78% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 464,871 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.