- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- Preface
- Acknowledgements
- Chapter 1. Introduction
- Chapter 2. Data parallel computing
- Chapter 3. Scalable parallel execution
- Chapter 4. Memory and data locality
- Chapter 5. Performance considerations
- Chapter 6. Numerical considerations
- Chapter 7. Parallel patterns: convolution: An introduction to stencil computation
- Chapter 8. Parallel patterns: prefix sum: An introduction to work efficiency in parallel algorithms
- Abstract
- 8.1 Background
- 8.2 A Simple Parallel Scan
- 8.3 Speed and Work Efficiency
- 8.4 A More Work-Efficient Parallel Scan
- 8.5 An Even More Work-Efficient Parallel Scan
- 8.6 Hierarchical Parallel Scan for Arbitrary-Length Inputs
- 8.7 Single-Pass Scan for Memory Access Efficiency
- 8.8 Summary
- 8.9 Exercises
- References
- Chapter 9. Parallel patterns—parallel histogram computation: An introduction to atomic operations and privatization
- Chapter 10. Parallel patterns: sparse matrix computation: An introduction to data compression and regularization
- Chapter 11. Parallel patterns: merge sort: An introduction to tiling with dynamic input data identification
- Chapter 12. Parallel patterns: graph search
- Chapter 13. CUDA dynamic parallelism
- Chapter 14. Application case study—non-Cartesian magnetic resonance imaging: An introduction to statistical estimation methods
- Chapter 15. Application case study—molecular visualization and analysis
- Chapter 16. Application case study—machine learning
- Chapter 17. Parallel programming and computational thinking
- Abstract
- 17.1 Goals of Parallel Computing
- 17.2 Problem Decomposition
- 17.3 Algorithm Selection
- 17.4 Computational Thinking
- 17.5 Single Program, Multiple Data, Shared Memory and Locality
- 17.6 Strategies for Computational Thinking
- 17.7 A Hypothetical Example: Sodium Map of the Brain
- 17.8 Summary
- References
- Chapter 18. Programming a heterogeneous computing cluster
- Abstract
- 18.1 Background
- 18.2 A Running Example
- 18.3 Message Passing Interface Basics
- 18.4 Message Passing Interface Point-to-Point Communication
- 18.5 Overlapping Computation and Communication
- 18.6 Message Passing Interface Collective Communication
- 18.7 CUDA-Aware Message Passing Interface
- 18.8 Summary
- Reference
- Chapter 19. Parallel programming with OpenACC
- Chapter 20. More on CUDA and graphics processing unit computing
- Chapter 21. Conclusion and outlook
- Appendix A. An introduction to OpenCL
- Appendix B. THRUST: a productivity-oriented library for CUDA
- Appendix C. CUDA Fortran
- C.1 CUDA Fortran and CUDA C Differences
- C.2 A First CUDA Fortran Program
- C.3 Multidimensional Array in CUDA Fortran
- C.4 Overloading Host/Device Routines with Generic Interfaces
- C.5 Calling CUDA C via ISO_C_Binding
- C.6 Kernel Loop Directives and Reduction Operations
- C.7 Dynamic Shared Memory
- C.8 Asynchronous Data Transfers
- C.9 Compilation and Profiling
- C.10 Calling Thrust from CUDA Fortran
- Appendix D. An introduction to C++ AMP
- Index
Programming Massively Parallel Processors
A Hands-on Approach
Third Edition
David B. Kirk
Wen-mei W. Hwu
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