Using OpenMP - The Next Step. Affinity, Accelerators, Tasking, and SIMD
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- Nombre de pages365
- PrésentationBroché
- FormatGrand Format
- Poids0.68 kg
- Dimensions20,3 cm × 22,6 cm × 2,0 cm
- ISBN978-0-262-53478-9
- EAN9780262534789
- Date de parution20/10/2017
- CollectionScientific and Engineering Com
- ÉditeurMIT Press (The)
Résumé
This book offers an up-to-date, practical tutorial on advanced features in the widely used OpenMP parallel programming model. Building on the previous volume, Using OpenMP : Portable Shared Memory Parallel Programming (MIT Press), this book goes beyond the fundamentals to focus on what has been changed and added to OpenMP since the 2.5 specifications. It emphasizes four major and advanced areas : thread affinity (keeping threads close to their data), accelerators (special hardware to speed up certain operations), tasking (to parallelize algorithms with a less regular execution flow), and SIMD (hardware-assisted operations on vectors).
As in the earlier volume, the focus is on practical usage, with major new features primarily introduced by example. Examples are restricted to C and C++, but are straightforward enough to be understood by Fortran programmers. After a brief recap of OpenMP 2.5, the book reviews enhancements introduced since 2.5. It then discusses in detail tasking, a major functionality enhancement ; Non-Uniform Memory Access (NUMA) architectures, supported by OpenMP ; SIMD, or Single Instruction Multiple Data ; heterogeneous systems, a new parallel programming model to offload computation to accelerators ; and the expected further development of OpenMP.
As in the earlier volume, the focus is on practical usage, with major new features primarily introduced by example. Examples are restricted to C and C++, but are straightforward enough to be understood by Fortran programmers. After a brief recap of OpenMP 2.5, the book reviews enhancements introduced since 2.5. It then discusses in detail tasking, a major functionality enhancement ; Non-Uniform Memory Access (NUMA) architectures, supported by OpenMP ; SIMD, or Single Instruction Multiple Data ; heterogeneous systems, a new parallel programming model to offload computation to accelerators ; and the expected further development of OpenMP.
This book offers an up-to-date, practical tutorial on advanced features in the widely used OpenMP parallel programming model. Building on the previous volume, Using OpenMP : Portable Shared Memory Parallel Programming (MIT Press), this book goes beyond the fundamentals to focus on what has been changed and added to OpenMP since the 2.5 specifications. It emphasizes four major and advanced areas : thread affinity (keeping threads close to their data), accelerators (special hardware to speed up certain operations), tasking (to parallelize algorithms with a less regular execution flow), and SIMD (hardware-assisted operations on vectors).
As in the earlier volume, the focus is on practical usage, with major new features primarily introduced by example. Examples are restricted to C and C++, but are straightforward enough to be understood by Fortran programmers. After a brief recap of OpenMP 2.5, the book reviews enhancements introduced since 2.5. It then discusses in detail tasking, a major functionality enhancement ; Non-Uniform Memory Access (NUMA) architectures, supported by OpenMP ; SIMD, or Single Instruction Multiple Data ; heterogeneous systems, a new parallel programming model to offload computation to accelerators ; and the expected further development of OpenMP.
As in the earlier volume, the focus is on practical usage, with major new features primarily introduced by example. Examples are restricted to C and C++, but are straightforward enough to be understood by Fortran programmers. After a brief recap of OpenMP 2.5, the book reviews enhancements introduced since 2.5. It then discusses in detail tasking, a major functionality enhancement ; Non-Uniform Memory Access (NUMA) architectures, supported by OpenMP ; SIMD, or Single Instruction Multiple Data ; heterogeneous systems, a new parallel programming model to offload computation to accelerators ; and the expected further development of OpenMP.