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An insider's guide to CUDA Fortran from key members of the development team.

Table of Contents

I CUDA Fortran Programming 1. Introduction 2. Performance Measurement and Metrics 3. Optimization 4. Multi-GPU ProgrammingII Case Studies 5. Monte Carlo Method 6. Finite Difference Method 7. Applications of Fast Fourier TransformIII Appendices A. Tesla Specifications B. System and Environment Management C. Calling CUDA C from CUDA Fortran D. Source Code

About the Author

Greg Ruetsch is a Senior Applied Engineer at NVIDIA, where he works on CUDA Fortran and performance optimization of HPC codes. He holds a Bachelor’s degree in mechanical and aerospace engineering from Rutgers University and a Ph.D. in applied mathematics from Brown University. Prior to joining NVIDIA he has held research positions at Stanford University’s Center for Turbulence Research and Sun Microsystems Laboratories. Massimiliano Fatica is the manager of the Tesla HPC Group at NVIDIA where he works in the area of GPU computing (high-performance computing and clusters). He holds a laurea in Aeronautical Engineering and a Phd in Theoretical and Applied Mechanics from the University of Rome “La Sapienza”. Prior to joining NVIDIA, he was a research staff member at Stanford University where he worked at the Center for Turbulence Research and Center for Integrated Turbulent Simulations on applications for the Stanford Streaming Supercomputer.

Reviews

"This book is written for the Fortran programmer who wants to do real work on GPUs, not just stunts or demonstrations. The book has many examples, and includes introductory material on GPU programming as well as advanced topics such as data optimization, instruction optimization and multiple GPU programming. Placing the performance measurement chapter before performance optimization is key, since measurement drives the tuning and optimization process. All Fortran programmers interested in GPU programming should read this book." --Michael Wolfe, PGI Compiler Engineer