Multi-core Programmiong with Cuda and Opencl
Delmar Cengage Learning (Publisher)
Book
Hardback
304 pages
978-1-4354-5855-0 (ISBN)
Description
"Multi-Core Programming With Cuda And Opencl" covers multi-core GPU programming with some emphasis on CPU vs GPU hardware, with benchmark/performance tests to show improvements from purely a CPU approach to a GPU approach to processing data using several different libraries including CUDA, OpenCL, and DirectCompute. It includes topics such as differences and capabilities of hardware architectures, parallel API's and algorithm design for parallel machines. Emphasis will be on practical, applied use of these multi-core GPU libraries. In order for these new architectures to be utilized, programmers must educate themselves on the advantages, limitations, and algorithm design methods for parallel computing platforms. This is a very important but challenging subject that encompasses multi-threading, symmetric multi-processing (SMP), and parallel processing on the CPU side, and massively parallel processing on the GPU side. This book includes end-of-chapter exercises, discussions, and quizzes.
More details
Language
English
Place of publication
Clifton Park
United States
Publishing group
Cengage Learning, Inc
Target group
College/higher education
Professional and scholarly
Dimensions
Height: 246 mm
Width: 189 mm
Thickness: 20 mm
ISBN-13
978-1-4354-5855-0 (9781435458550)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Content
1. Introduction to Symmetric Multi-Processing (SMP). 2. Multi-Core Architectures: CPU vs GPU Hardware. 3. Getting Started with CUDA. 4. Crunching Numbers with CUDA. 5. Getting Started with OpenCL. 6. Using OpenCL. 7. ATI Stream SDK. 8. DirectCompute. 9. Advanced DirectCompute. 10. Case Study - Parallel Sorting. 11. Inter-Processor Communication Patterns. 12. Recognizing Parallelism. 13. Designing Parallel Algorithms. 14. Parallel Efficiency. 15. Benchmark Performance Comparisons. Appendix A: Compiler Configurations. Appendix B: Recommended Resources.