CUDA Fortran for Scientists and Engineers: Best Practices for Efficient CUDA Fortran Programming shows how high-performance application developers can leverage the power of GPUs using Fortran, the familiar language of scientific computing and supercomputer performance benchmarking. The authors presume no prior parallel computing experience, and cover the basics along with best practices for efficient GPU computing using CUDA Fortran. In order to add CUDA Fortran to existing Fortran codes, they explain how to understand the target GPU architecture, identify computationally-intensive parts of the code, and modify the code to manage the data and parallelism and optimize performance – all in Fortran, without having to rewrite in another language.
Each concept is illustrated with actual examples so you can immediately evaluate the performance of your code in comparison.
This second edition provides much needed updates on how to efficiently program GPUs in CUDA Fortran. It can be used either as a tutorial on GPU programming in CUDA Fortran as well as a reference text.
1. Chapter 1 : Introduction - p. Page xi 2. Chapter 2 : Correctness, accuracy, and debugging - p. Page 39 3. Chapter 3 : Performance measurement and metrics - p. Page 55 4. Chapter 4 : Synchronization - p. Page 75 5. Chapter 5 : Optimization - p. Page 115 6. Chapter 6 : Porting tips and techniques - p. Page 165 7. Chapter 7 : Interfacing with CUDA C code and CUDA libraries - p. Page 195 8. Chapter 8 : Multi-GPU programming - p. Page 227 9. Chapter 9 : Monte Carlo method - p. Page 259 10. Chapter 10 : Finite difference method - p. Page 298 11. Chapter 11 : Applications of the fast Fourier transform - p. Page 316 12. Chapter 12 : Ray tracing - p. Page 339 13. Appendix A : System and environment management - p. Page 400 14. References - p. Page 409 15. Index - p. Page 409
CUDA Fortran for Scientists and Engineers: Best Practices for Efficient CUDA Fortran Programming shows how high-performance application developers can leverage the power of GPUs using Fortran, the familiar language of scientific computing and supercomputer performance benchmarking. The authors presume no prior parallel computing experience, and cover the basics along with best practices for efficient GPU computing using CUDA Fortran. In order to add CUDA Fortran to existing Fortran codes, they explain how to understand the target GPU architecture, identify computationally-intensive parts of the code, and modify the code to manage the data and parallelism and optimize performance – all in Fortran, without having to rewrite in another language.
Each concept is illustrated with actual examples so you can immediately evaluate the performance of your code in comparison.
This second edition provides much needed updates on how to efficiently program GPUs in CUDA Fortran. It can be used either as a tutorial on GPU programming in CUDA Fortran as well as a reference text.
1. Chapter 1 : Introduction - p. Page xi 2. Chapter 2 : Correctness, accuracy, and debugging - p. Page 39 3. Chapter 3 : Performance measurement and metrics - p. Page 55 4. Chapter 4 : Synchronization - p. Page 75 5. Chapter 5 : Optimization - p. Page 115 6. Chapter 6 : Porting tips and techniques - p. Page 165 7. Chapter 7 : Interfacing with CUDA C code and CUDA libraries - p. Page 195 8. Chapter 8 : Multi-GPU programming - p. Page 227 9. Chapter 9 : Monte Carlo method - p. Page 259 10. Chapter 10 : Finite difference method - p. Page 298 11. Chapter 11 : Applications of the fast Fourier transform - p. Page 316 12. Chapter 12 : Ray tracing - p. Page 339 13. Appendix A : System and environment management - p. Page 400 14. References - p. Page 409 15. Index - p. Page 409
