High Performance Parallelism Pearls Volume Two
Multicore and Many-core Programming Approaches

Second collection of success stories leveraging parallelism in heterogeneous systems

Jim Jeffers (Author), James Reinders (Author)

9780128038192, Elsevier Science

Paperback, published 24 July 2015

592 pages
23.4 x 19 x 3.6 cm, 1.13 kg

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.

1. Introduction
2. Numerical Weather Prediction Optimization
3. WRF Goddard Microphysics Scheme Optimization
4. Pairwise DNA Sequence Alignment Optimization
5. Accelerated Structural Bioinformatics for Drug Discovery
6. Amber PME Molecular Dynamics Optimization
7. Low-Latency Solutions for Financial Services Applications
8. Parallel Numerical Methods in Finance
9. Wilson Dslash Kernel from Lattice QCD Optimization
10. Cosmic Microwave Background Analysis: Nested Parallelism
11. Visual Search Optimization
12. Radio Frequency Ray Tracing
13. Exploring Use of the Reserved Core
14. High Performance Python Offloading
15. Fast Matrix Computations on Heterogeneous Streams
16. MPI-3 Shared Memory Programming Introduction
17. Coarse-Grained OpenMP for Scalable Hybrid Parallelism
18. Exploiting Multilevel Parallelism in Quantum Simulations
19. OpenCL: There and Back Again
20. OpenMP Versus OpenCL: Difference in Performance?
21. Prefetch Tuning Optimizations
22. SIMD Functions Via OpenMP
23. Vectorization Advice
24. Portable Explicit Vectorization Intrinsics
25. Power Analysis for Applications and Data Centers

Subject Areas: Computer architecture & logic design [UYF], Distributed systems [UTR], Computer programming / software development [UM], Grid & parallel computing [UKG]