High Performance Parallelism Pearls shows how to leverage parallelism on processors and coprocessors with the same programming - illustrating the most effective ways to better tap the computational potential of systems with Intel Xeon Phi coprocessors and Intel Xeon processors or other multicore processors. The book includes examples of successful programming efforts, drawn from across industries and domains such as chemistry, engineering, and environmental science. 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 these powerful systems, but also how to leverage parallelism across these heterogeneous systems.
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Table of Contents
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 PhiT Coprocessors11. Dynamic Load Balancing using OpenMP 4.012. Concurrent Kernel Offloading13. Heterogeneous Computing with MPI14. Power Analysis on the Intel� Xeon PhiT 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
