Openmp Enabling Massive Node Level Parallelism
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OpenMP: Enabling Massive Node-Level Parallelism
Author: Simon McIntosh-Smith
language: en
Publisher: Springer Nature
Release Date: 2021-09-08
This book constitutes the proceedings of the 17th International Workshop on OpenMP, IWOMP 2021, held virtually in September 2021 and hosted by the High Performance Computing research group at the University of Bristol, UK. The 15 full papers presented in this volume were carefully reviewed and selected for inclusion in this book. The papers are organized in topical sections named: synchronization and data; tasking expansions; applications; case studies; and heterogenous computing and memory. Chapter ‘FOTV: A Generic Device Offloading Framework for OpenMP’ is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
OpenMP: Enabling Massive Node-Level Parallelism
This book constitutes the proceedings of the 17th International Workshop on OpenMP, IWOMP 2021, held virtually in September 2021 and hosted by the High Performance Computing research group at the University of Bristol, UK. The 15 full papers presented in this volume were carefully reviewed and selected for inclusion in this book. The papers are organized in topical sections named: synchronization and data; tasking expansions; applications; case studies; and heterogenous computing and memory. Chapter 'FOTV: A Generic Device Offloading Framework for OpenMP' is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Beyond Loop Level Parallelism in OpenMP: Accelerators, Tasking and More
Author: Mitsuhisa Sato
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-06
This book constitutes the refereed proceedings of the 6th International Workshop on OpenMP, IWOMP 2010, held in Tsukuba City, Japan, in June 2010. The papers are organized in topical sections on Runtime and Optimization, Proposed Extensions to OpenMP, Scheduling and Performance, as well as Hybrid Programming and Accelerators with OpenMP.