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High Performance Computing

Overview of attention for book
High Performance Computing
Springer International Publishing

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
Attention for Chapter 6: Prediction of Acoustic Fields Using a Lattice-Boltzmann Method and Deep Learning
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Chapter title
Prediction of Acoustic Fields Using a Lattice-Boltzmann Method and Deep Learning
Chapter number 6
Book title
High Performance Computing
Published by
Springer, Cham, October 2020
DOI 10.1007/978-3-030-59851-8_6
Book ISBNs
978-3-03-059850-1, 978-3-03-059851-8
Authors

Mario Rüttgers, Seong-Ryong Koh, Jenia Jitsev, Wolfgang Schröder, Andreas Lintermann, Rüttgers, Mario, Koh, Seong-Ryong, Jitsev, Jenia, Schröder, Wolfgang, Lintermann, Andreas

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X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 27%
Other 1 9%
Student > Doctoral Student 1 9%
Researcher 1 9%
Professor > Associate Professor 1 9%
Other 1 9%
Unknown 3 27%
Readers by discipline Count As %
Engineering 4 36%
Computer Science 2 18%
Energy 1 9%
Biochemistry, Genetics and Molecular Biology 1 9%
Unknown 3 27%

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