Programming Massively Parallel Processors: A Hands-on Approach, Third Edition shows both student and professional alike the basic concepts of parallel programming and GPU architecture, exploring, in detail, various techniques for constructing parallel programs.
Case studies demonstrate the development process, detailing computational thinking and ending with effective and efficient parallel programs. Topics of performance, floating-point format, parallel patterns, and dynamic parallelism are covered in-depth.
For this new edition, the authors have updated their coverage of CUDA, including coverage of newer libraries, such as CuDNN, moved content that has become less important to appendices, added two new chapters on parallel patterns, and updated case studies to reflect current industry practices.
1. Chapter 1. Introduction - p. 1 2. Chapter 2. Data parallel computing - p. 19 3. Chapter 3. Scalable parallel execution - p. 43 4. Chapter 4. Memory and data locality - p. 71 5. Chapter 5. Performance considerations - p. 103 6. Chapter 6. Numerical considerations - p. 131 7. Chapter 7. Parallel patterns: convolution: An introduction to stencil computation - p. 149 8. Chapter 8. Parallel patterns: prefix sum: An introduction to work efficiency in parallel algorithms - p. 175 9. Chapter 9. Parallel patterns—parallel histogram computation: An introduction to atomic operations and privatization - p. 199 10. Chapter 10. Parallel patterns: sparse matrix computation: An introduction to data compression and regularization - p. 215 11. Chapter 11. Parallel patterns: merge sort: An introduction to tiling with dynamic input data identification - p. 231 12. Chapter 12. Parallel patterns: graph search - p. 257 13. Chapter 13. CUDA dynamic parallelism - p. 275 14. Chapter 14. Application case study—non-Cartesian magnetic resonance imaging: An introduction to statistical estimation methods - p. 305 15. Chapter 15. Application case study—molecular visualization and analysis - p. 331 16. Chapter 16. Application case study—machine learning - p. 345 17. Chapter 17. Parallel programming and computational thinking - p. 369 18. Chapter 18. Programming a heterogeneous computing cluster - p. 387 19. Chapter 19. Parallel programming with OpenACC - p. 413 20. Chapter 20. More on CUDA and graphics processing unit computing - p. 443 21. Chapter 21. Conclusion and outlook - p. 457 22. Appendix A. An introduction to OpenCL - p. 461 23. Appendix B. THRUST: a productivity-oriented library for CUDA - p. 475 24. Appendix C. CUDA Fortran - p. 493 25. Appendix D. An introduction to C++ AMP - p. 515 26. Index - p. 535
Programming Massively Parallel Processors: A Hands-on Approach, Third Edition shows both student and professional alike the basic concepts of parallel programming and GPU architecture, exploring, in detail, various techniques for constructing parallel programs.
Case studies demonstrate the development process, detailing computational thinking and ending with effective and efficient parallel programs. Topics of performance, floating-point format, parallel patterns, and dynamic parallelism are covered in-depth.
For this new edition, the authors have updated their coverage of CUDA, including coverage of newer libraries, such as CuDNN, moved content that has become less important to appendices, added two new chapters on parallel patterns, and updated case studies to reflect current industry practices.
1. Chapter 1. Introduction - p. 1 2. Chapter 2. Data parallel computing - p. 19 3. Chapter 3. Scalable parallel execution - p. 43 4. Chapter 4. Memory and data locality - p. 71 5. Chapter 5. Performance considerations - p. 103 6. Chapter 6. Numerical considerations - p. 131 7. Chapter 7. Parallel patterns: convolution: An introduction to stencil computation - p. 149 8. Chapter 8. Parallel patterns: prefix sum: An introduction to work efficiency in parallel algorithms - p. 175 9. Chapter 9. Parallel patterns—parallel histogram computation: An introduction to atomic operations and privatization - p. 199 10. Chapter 10. Parallel patterns: sparse matrix computation: An introduction to data compression and regularization - p. 215 11. Chapter 11. Parallel patterns: merge sort: An introduction to tiling with dynamic input data identification - p. 231 12. Chapter 12. Parallel patterns: graph search - p. 257 13. Chapter 13. CUDA dynamic parallelism - p. 275 14. Chapter 14. Application case study—non-Cartesian magnetic resonance imaging: An introduction to statistical estimation methods - p. 305 15. Chapter 15. Application case study—molecular visualization and analysis - p. 331 16. Chapter 16. Application case study—machine learning - p. 345 17. Chapter 17. Parallel programming and computational thinking - p. 369 18. Chapter 18. Programming a heterogeneous computing cluster - p. 387 19. Chapter 19. Parallel programming with OpenACC - p. 413 20. Chapter 20. More on CUDA and graphics processing unit computing - p. 443 21. Chapter 21. Conclusion and outlook - p. 457 22. Appendix A. An introduction to OpenCL - p. 461 23. Appendix B. THRUST: a productivity-oriented library for CUDA - p. 475 24. Appendix C. CUDA Fortran - p. 493 25. Appendix D. An introduction to C++ AMP - p. 515 26. Index - p. 535
