Buch, Englisch, 592 Seiten, Format (B × H): 184 mm x 231 mm, Gewicht: 1118 g
Multicore and Many-core Programming Approaches
Buch, Englisch, 592 Seiten, Format (B × H): 184 mm x 231 mm, Gewicht: 1118 g
ISBN: 978-0-12-803819-2
Verlag: Elsevier Science & Technology
High Performance Parallelism Pearls Volume 2 offers another set of examples that demonstrate how to leverage parallelism. Similar to Volume 1, the techniques included here explain how to use processors and coprocessors with the same programming - illustrating the most effective ways to combine Xeon Phi coprocessors with Xeon and other multicore processors. The book includes examples of successful programming efforts, drawn from across industries and domains such as biomed, genetics, finance, manufacturing, imaging, and more. Each chapter in this edited work includes detailed explanations of the programming techniques used, while showing high performance results on both Intel Xeon Phi coprocessors and multicore processors. Learn from dozens of new examples and case studies illustrating "success stories" demonstrating not just the features of Xeon-powered systems, but also how to leverage parallelism across these heterogeneous systems.
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Weitere Infos & Material
- Introduction
- Numerical Weather Prediction Optimization
- WRF Goddard Microphysics Scheme Optimization
- Pairwise DNA Sequence Alignment Optimization
- Accelerated Structural Bioinformatics for Drug Discovery
- Amber PME Molecular Dynamics Optimization
- Low-Latency Solutions for Financial Services Applications
- Parallel Numerical Methods in Finance
- Wilson Dslash Kernel from Lattice QCD Optimization
- Cosmic Microwave Background Analysis: Nested Parallelism
- Visual Search Optimization
- Radio Frequency Ray Tracing
- Exploring Use of the Reserved Core
- High Performance Python Offloading
- Fast Matrix Computations on Heterogeneous Streams
- MPI-3 Shared Memory Programming Introduction
- Coarse-Grained OpenMP for Scalable Hybrid Parallelism
- Exploiting Multilevel Parallelism in Quantum Simulations
- OpenCL: There and Back Again
- OpenMP Versus OpenCL: Difference in Performance?
- Prefetch Tuning Optimizations
- SIMD Functions Via OpenMP
- Vectorization Advice
- Portable Explicit Vectorization Intrinsics
- Power Analysis for Applications and Data Centers