High Performance Parallelism Pearls Volume One: Multicore and Many-core Programming Approaches
Autor James Reinders, James Jeffersen Limba Engleză Paperback – 6 noi 2014
- Promotes consistent standards-based programming, showing in detail how to code for high performance on multicore processors and Intel® Xeon Phi™
- Examples from multiple vertical domains illustrating parallel optimizations to modernize real-world codes
- Source code available for download to facilitate further exploration
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Specificații
ISBN-13: 9780128021187
ISBN-10: 0128021187
Pagini: 600
Dimensiuni: 191 x 235 x 25 mm
Greutate: 1.07 kg
Editura: ELSEVIER SCIENCE
ISBN-10: 0128021187
Pagini: 600
Dimensiuni: 191 x 235 x 25 mm
Greutate: 1.07 kg
Editura: ELSEVIER SCIENCE
Cuprins
1. Introduction2. Towards an efficient Godunov's scheme on Phi3. Better Concurrency and SIMD on HBM4. Case Study: Analyzing and Optimizing Concurrency5. Plesiochronous Phasing Barriers6. Parallel Evaluation of Fault Tree Expressions7. Deep-learning and Numerical Optimization8. Optimizing Gather/Scatter Patterns9. A many core implementation of the direct N-body problem10. N-body Methods on Intel® Xeon Phi™ Coprocessors11. Dynamic Load Balancing using OpenMP 4.012. Concurrent Kernel Offloading13. Heterogeneous Computing with MPI14. Power Analysis on the Intel® Xeon Phi™ Coprocessor15. Integrating Intel Xeon Phis into a Cluster16. Native File systems17. NWChem: Quantum Chemistry Simulations at Scale18. Efficient nested parallelism on large scale system19. Performance optimization of Black-Scholes pricing20. Host and Coprocessor Data Transfer through the COI21. High Performance Ray Tracing with Embree22. Portable and Perform with OpenCL23. Characterization and Auto-tuning of 3DFD.24. Profiling-guided optimization of cache performance25. Heterogeneous MPI optimization with ITAC26. Scalable Out-of-core Solvers on a Cluster27. Sparse matrix-vector multiplication: parallelization and vectorization28. Morton Order Improves Performance
Recenzii
"This book will make it much easier in general to exploit high levels of parallelism including programming optimally for the Intel Xeon Phi products. The common programming methodology between the Xeon and Xeon Phi families is good news for the entire scientific and engineering community; the same programming can realize parallel scaling and vectorization for both multicore and many-core." –-from the Foreword by Sverre Jarp, CERN Openlab CTO