CUDA Fortran for Scientists and Engineers
Best Practices for Efficient CUDA Fortran Programming
- 2nd Edition - July 11, 2024
- Latest edition
- Authors: Gregory Ruetsch, Massimiliano Fatica
- Language: English
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 u… Read more
World Book Day celebration
Where learning shapes lives
Up to 25% off trusted resources that support research, study, and discovery.
Description
Description
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.
Key features
Key features
- Presents optimization strategies for current hardware, including Hopper generation GPUs
- Includes discussions of new language and hardware features, including managed memory, tensor cores, shuffle instructions, new multi-GPU paradigms
- Offers resources and strategies for porting large codes to GPUs, including language features as well as library use
Readership
Readership
Scientists and engineers who want to use GPU computing as a tool in their respective research fields rather than from a pure computational science perspective. No previous experience with parallel computing is required, only knowledge of Fortran 90. Anyone interested in writing parallel codes in Fortran, from financial applications to climate and weather modeling
Table of contents
Table of contents
1. Introduction
2. Correctness, Accuracy, and Debugging
3. Performance Measurements and Metrics
4. Synchronization
5. Optimization
6. Multi-GPU Programming
7. Porting Tips and Techniques
8. Interfacing with CUDA C, OpenACC, and CUDA Libraries
PART II Case Studies
9. Monte Carlo Method
10. Finite Difference Method
11. Applications of the Fast Fourier TransformRay Tracing
Product details
Product details
- Edition: 2
- Latest edition
- Published: July 16, 2024
- Language: English
About the authors
About the authors
GR
Gregory Ruetsch
MF