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High Performance Computing

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Cover of 'High Performance Computing'

Table of Contents

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    Book Overview
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    Chapter 1 Evaluating Quality of Service Traffic Classes on the Megafly Network
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    Chapter 2 Densifying Assumed-Sparse Tensors
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    Chapter 3 Learning Neural Representations for Predicting GPU Performance
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    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
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    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
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    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
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    Chapter 14 End-to-End Resilience for HPC Applications
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    Chapter 15 Resilient Optimistic Termination Detection for the Async-Finish Model
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    Chapter 16 Global Task Data-Dependencies in PGAS Applications
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    Chapter 17 Finepoints: Partitioned Multithreaded MPI Communication
Attention for Chapter 2: Densifying Assumed-Sparse Tensors
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Chapter title
Densifying Assumed-Sparse Tensors
Chapter number 2
Book title
High Performance Computing
Published by
Springer, Cham, June 2019
DOI 10.1007/978-3-030-20656-7_2
Book ISBNs
978-3-03-020655-0, 978-3-03-020656-7
Authors

Derya Cavdar, Valeriu Codreanu, Can Karakus, John A. Lockman, Damian Podareanu, Vikram Saletore, Alexander Sergeev, Don D. Smith, Victor Suthichai, Quy Ta, Srinivas Varadharajan, Lucas A. Wilson, Rengan Xu, Pei Yang, Cavdar, Derya, Codreanu, Valeriu, Karakus, Can, Lockman, John A., Podareanu, Damian, Saletore, Vikram, Sergeev, Alexander, Smith, Don D., Suthichai, Victor, Ta, Quy, Varadharajan, Srinivas, Wilson, Lucas A., Xu, Rengan, Yang, Pei

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 33%
Researcher 2 33%
Other 1 17%
Unknown 1 17%
Readers by discipline Count As %
Computer Science 4 67%
Linguistics 1 17%
Unknown 1 17%