CUDA Application Design and Development

Informationen zum Autor Rob Farber has served as a scientist in Europe at the Irish Center for High-End Computing as well as U.S. national labs in Los Alamos, Berkeley, and the Pacific Northwest. He has also been on the external faculty at the Santa Fe Institute, consultant to fortune 100 companies, and co-founder of two computational startups that achieved liquidity events. He is the author of CUDA Application Design and Development? as well as numerous articles and tutorials that have appeared in Dr. Dobb's Journal and Scientific Computing, The Code Project and others. Klappentext As the computer industry retools to leverage massively parallel graphics processing units (GPUs)! this book is designed to meet the needs of working software developers who need to understand GPU programming with CUDA and increase efficiency in their projects. It starts with an introduction to parallel computing concepts. Zusammenfassung As the computer industry retools to leverage massively parallel graphics processing units (GPUs)! this book is designed to meet the needs of working software developers who need to understand GPU programming with CUDA and increase efficiency in their projects. It starts with an introduction to parallel computing concepts.

Autorentext
Rob Farber has served as a scientist in Europe at the Irish Center for High-End Computing as well as U.S. national labs in Los Alamos, Berkeley, and the Pacific Northwest. He has also been on the external faculty at the Santa Fe Institute, consultant to fortune 100 companies, and co-founder of two computational startups that achieved liquidity events. He is the author of CUDA Application Design and Development” as well as numerous articles and tutorials that have appeared in Dr. Dobb's Journal and Scientific Computing, The Code Project and others.

Klappentext

As the computer industry retools to leverage massively parallel graphics processing units (GPUs), this book is designed to meet the needs of working software developers who need to understand GPU programming with CUDA and increase efficiency in their projects. It starts with an introduction to parallel computing concepts.

Inhalt

1. First Programs and How to Think in CUDA

2. CUDA for Machine Learning and Optimization

3. The CUDA Tool Suite: Profiling a PCA/NLPCA Functor

4. The CUDA Execution Model

5. CUDA Memory

6. Efficiently Using GPU Memory

7. Techniques to Increase Parallelism

8. CUDA for All GPU and CPU Applications

9. Mixing CUDA and Rendering

10. CUDA in a Cloud and Cluster Environments

11. CUDA for Real Problems: Monte Carlo, Modeling, and More

12. Application Focus on Live Streaming Video