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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
  2. Altmetric Badge
    Chapter 1 Evaluating Quality of Service Traffic Classes on the Megafly Network
  3. Altmetric Badge
    Chapter 2 Densifying Assumed-Sparse Tensors
  4. Altmetric Badge
    Chapter 3 Learning Neural Representations for Predicting GPU Performance
  5. Altmetric Badge
    Chapter 4 SLOPE: Structural Locality-Aware Programming Model for Composing Array Data Analysis
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    Chapter 5 A Near-Data Processing Server Architecture and Its Impact on Data Center Applications
  7. Altmetric Badge
    Chapter 6 Comparing the Efficiency of In Situ Visualization Paradigms at Scale
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    Chapter 7 Layout-Aware Embedding for Quantum Annealing Processors
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    Chapter 8 Toward Efficient Architecture-Independent Algorithms for Dynamic Programs
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    Chapter 9 Petaflop Seismic Simulations in the Public Cloud
  11. Altmetric Badge
    Chapter 10 MaLTESE: Large-Scale Simulation-Driven Machine Learning for Transient Driving Cycles
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    Chapter 11 PerfMemPlus: A Tool for Automatic Discovery of Memory Performance Problems
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    Chapter 12 GPUMixer: Performance-Driven Floating-Point Tuning for GPU Scientific Applications
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    Chapter 13 Performance Exploration Through Optimistic Static Program Annotations
  15. Altmetric Badge
    Chapter 14 End-to-End Resilience for HPC Applications
  16. Altmetric Badge
    Chapter 15 Resilient Optimistic Termination Detection for the Async-Finish Model
  17. Altmetric Badge
    Chapter 16 Global Task Data-Dependencies in PGAS Applications
  18. Altmetric Badge
    Chapter 17 Finepoints: Partitioned Multithreaded MPI Communication
Attention for Chapter 10: MaLTESE: Large-Scale Simulation-Driven Machine Learning for Transient Driving Cycles
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

news
2 news outlets
blogs
1 blog
twitter
2 tweeters

Readers on

mendeley
4 Mendeley
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Chapter title
MaLTESE: Large-Scale Simulation-Driven Machine Learning for Transient Driving Cycles
Chapter number 10
Book title
High Performance Computing
Published in
arXiv, June 2019
DOI 10.1007/978-3-030-20656-7_10
Book ISBNs
978-3-03-020655-0, 978-3-03-020656-7
Authors

Shashi M. Aithal, Prasanna Balaprakash

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 75%
Researcher 1 25%
Readers by discipline Count As %
Computer Science 3 75%
Engineering 1 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 21. 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 24 September 2019.
All research outputs
#998,079
of 15,908,399 outputs
Outputs from arXiv
#15,872
of 630,010 outputs
Outputs of similar age
#27,722
of 269,778 outputs
Outputs of similar age from arXiv
#852
of 30,614 outputs
Altmetric has tracked 15,908,399 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 630,010 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done particularly well, scoring higher than 97% 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 269,778 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 30,614 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.