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OpenMP: Enabling Massive Node-Level Parallelism

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Cover of 'OpenMP: Enabling Massive Node-Level Parallelism'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Improving Speculative taskloop in Hardware Transactional Memory
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    Chapter 2 Vectorized Barrier and Reduction in LLVM OpenMP Runtime
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    Chapter 3 Enhancing OpenMP Tasking Model: Performance and Portability
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    Chapter 4 OpenMP Taskloop Dependences
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    Chapter 5 Outcomes of OpenMP Hackathon: OpenMP Application Experiences with the Offloading Model (Part I)
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    Chapter 6 Outcomes of OpenMP Hackathon: OpenMP Application Experiences with the Offloading Model (Part II)
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    Chapter 7 An Empirical Investigation of OpenMP Based Implementation of Simplex Algorithm
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    Chapter 8 Task Inefficiency Patterns for a Wave Equation Solver
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    Chapter 9 Comparing OpenMP Implementations with Applications Across A64FX Platforms
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    Chapter 10 A Case Study of LLVM-Based Analysis for Optimizing SIMD Code Generation
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    Chapter 11 Experience Report: Writing a Portable GPU Runtime with OpenMP 5.1
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    Chapter 12 FOTV: A Generic Device Offloading Framework for OpenMP
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    Chapter 13 Beyond Explicit Transfers: Shared and Managed Memory in OpenMP
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    Chapter 14 Communication-Aware Task Scheduling Strategy in Hybrid MPI+OpenMP Applications
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    Chapter 15 An OpenMP Free Agent Threads Implementation
Attention for Chapter 8: Task Inefficiency Patterns for a Wave Equation Solver
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Chapter title
Task Inefficiency Patterns for a Wave Equation Solver
Chapter number 8
Book title
OpenMP: Enabling Massive Node-Level Parallelism
Published by
Springer, Cham, September 2021
DOI 10.1007/978-3-030-85262-7_8
Book ISBNs
978-3-03-085261-0, 978-3-03-085262-7
Authors

Schulz, Holger, Gadeschi, Gonzalo Brito, Rudyy, Oleksandr, Weinzierl, Tobias

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 1 50%
Student > Master 1 50%
Readers by discipline Count As %
Computer Science 2 100%

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