Cuda Programming - Shane Cook
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Présentation Cuda Programming de Shane Cook Format Broché
- Livre Littérature Générale
Résumé :
A comprehensive introduction to parallel programming with CUDA, for readers new to both.
Biographie:
Shane Cook is Technical Director at CUDA Developer, a consultancy company that helps companies exploit the power of GPUs by re-engineering code to make the optimal use of the hardware available. He formed CUDA Developer upon realizing the potential of heterogeneous systems and CUDA to disrupt existing serial and parallel programming technologies. He has a degree in Applied Software Engineering, specializing in the embedded software field. He has worked in senior roles with blue chip companies over the past twenty years, always seeking to help to develop the engineers in his team. He has worked on C programming standards including the MISRA Safer C used by widely in the automotive software community, and previously developed code for companies in the Germany automotive and defense contracting industries as well as Nortel and Ford Motor Company....
Sommaire: 1. A Short History of Supercomputing2. Understanding Parallelism with GPUs3. CUDA Hardware Overview4. Setting Up Cuda5. Grids, Blocks, and Threads6. Memory Handling with CUDA7. Using CUDA in Practice8. Multi-CPU and Multi-GPU Solutions9. Optimizing Your Application10. Libraries and SDK11. Designing GPU-Based Systems12. Common Problems, Causes, and Solutions
I must mention chapters 7, which deals with the practicalities of using the SDK, and 9, which offers advice and a detailed breakdown of areas that can limit the performance of a CUDA application. Together, these chapters transform this good book into the kind of excellent text that all CUDA developers can find useful, regardless of their relative experience.--ComputingReviews.com, July 12, 2013 This book is one of the most comprehensive on the subject published to date.it will guide those acquainted with GPU/CUDA from other books or from NVIDIA product documentation through the optimization maze to efficient CUDA/GPU coding.--ComputingReviews.com, April 25, 2013