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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 Asynchronous Iterative Algorithm for Computing Incomplete Factorizations on GPUs
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    Chapter 2 Matrix Multiplication on High-Density Multi-GPU Architectures: Theoretical and Experimental Investigations
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    Chapter 3 A Framework for Batched and GPU-Resident Factorization Algorithms Applied to Block Householder Transformations
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    Chapter 4 Parallel Efficient Sparse Matrix-Matrix Multiplication on Multicore Platforms
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    Chapter 5 On the Design, Development, and Analysis of Optimized Matrix-Vector Multiplication Routines for Coprocessors
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    Chapter 6 Large-Scale Neo-Heterogeneous Programming and Optimization of SNP Detection on Tianhe-2
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    Chapter 7 ACCOLADES: A Scalable Workflow Framework for Large-Scale Simulation and Analyses of Automotive Engines
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    Chapter 8 Accelerating LBM and LQCD Application Kernels by In-Memory Processing
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    Chapter 9 On Quantum Chemistry Code Adaptation for RSC PetaStream Architecture
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    Chapter 10 Dtree: Dynamic Task Scheduling at Petascale
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    Chapter 11 Feasibility Study of Porting a Particle Transport Code to FPGA
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    Chapter 12 High Performance Computing
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    Chapter 13 BWTCP: A Parallel Method for Constructing BWT in Large Collection of Genomic Reads
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    Chapter 14 Lattice-CSC: Optimizing and Building an Efficient Supercomputer for Lattice-QCD and to Achieve First Place in Green500
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    Chapter 15 An Efficient Clique-Based Algorithm of Compute Nodes Allocation for In-memory Checkpoint System
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    Chapter 16 A Scalable Algorithm for Radiative Heat Transfer Using Reverse Monte Carlo Ray Tracing
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    Chapter 17 Optimizing Processes Mapping for Tasks with Non-uniform Data Exchange Run on Cluster with Different Interconnects
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    Chapter 18 Dynamically Adaptable I/O Semantics for High Performance Computing
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    Chapter 19 Predicting Performance of Non-contiguous I/O with Machine Learning
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    Chapter 20 A Best Practice Analysis of HDF $$5$$ 5 and NetCDF- $$4$$ 4 Using Lustre
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    Chapter 21 Striping Layout Aware Data Aggregation for High Performance I/O on a Lustre File System
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    Chapter 22 Hop: Elastic Consistency for Exascale Data Stores
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    Chapter 23 Energy-Efficient Data Processing Through Data Sparsing with Artifacts
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    Chapter 24 Updating the Energy Model for Future Exascale Systems
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    Chapter 25 High-Order ADER-DG Minimizes Energy- and Time-to-Solution of SeisSol
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    Chapter 26 Modeling the Productivity of HPC Systems on a Computing Center Scale
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    Chapter 27 Taking Advantage of Node Power Variation in Homogenous HPC Systems to Save Energy
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    Chapter 28 A Run-Time System for Power-Constrained HPC Applications
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    Chapter 29 A Machine Learning Approach for a Scalable, Energy-Efficient Utility-Based Cache Partitioning
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    Chapter 30 A Case Study - Cost of Preemption for Urgent Computing on SuperMUC
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    Chapter 31 Designing Non-blocking Personalized Collectives with Near Perfect Overlap for RDMA-Enabled Clusters
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    Chapter 32 Design Methodology for Optimizing Optical Interconnection Networks in High Performance Systems
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    Chapter 33 Quantifying Communication in Graph Analytics
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    Chapter 34 Formal Metrics for Large-Scale Parallel Performance
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    Chapter 35 Hunting Down Load Imbalance: A Moving Target
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    Chapter 36 Orchestrating Docker Containers in the HPC Environment
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    Chapter 37 Performance and Scaling of WRF on Three Different Parallel 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
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

news
1 news outlet
twitter
8 X users

Citations

dimensions_citation
4 Dimensions

Readers on

mendeley
8 Mendeley
Summary News X Dimensions citations
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
High Performance Computing
Published by
Lecture notes in computer science, January 2015
DOI 10.1007/978-3-319-20119-1
ISBNs
978-3-31-920118-4, 978-3-31-920119-1
Authors

Julian M. Kunkel, Thomas Ludwig

Editors

Ludwig, Thomas, Kunkel, Julian M.

View on publisher site
X Demographics

X Demographics

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.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 25%
Professor > Associate Professor 2 25%
Student > Master 2 25%
Student > Bachelor 1 13%
Student > Doctoral Student 1 13%
Other 0 0%
Readers by discipline Count As %
Computer Science 4 50%
Engineering 2 25%
Unknown 2 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 March 2017.
All research outputs
#1,956,960
of 24,226,848 outputs
Outputs from Lecture notes in computer science
#329
of 8,180 outputs
Outputs of similar age
#27,443
of 361,495 outputs
Outputs of similar age from Lecture notes in computer science
#27
of 258 outputs
Altmetric has tracked 24,226,848 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% 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 particularly well, scoring higher than 95% 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 361,495 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 258 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.

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