Programming Massively Parallel Processors: A Hands-on Approach

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.

Teaches computational thinking and problem-solving techniques that facilitate high-performance parallel computingUtilizes CUDA version 7.5, NVIDIA's software development tool created specifically for massively parallel environmentsContains new and updated case studiesIncludes coverage of newer libraries, such as CuDNN for Deep Learning

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 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 Chapter 18. Programming a heterogeneous computing cluster Chapter 19. Parallel programming with OpenACC Chapter 20. More on CUDA and graphics processing unit computing Chapter 21. Conclusion and outlook