CUDA Programming: A Developer's Guide to Parallel Computing with GPUs

by Shane Cook

Overview

If you need to learn CUDA but don't have experience with parallel computing, CUDA Programming: A Developer's Introduction offers a detailed guide to CUDA with a grounding in parallel fundamentals. It starts by introducing CUDA and bringing you up to speed on GPU parallelism and hardware, then delving into CUDA installation. Chapters on core concepts including threads, blocks, grids, and memory focus on both parallel and CUDA-specific issues. Later, the book demonstrates CUDA in practice for optimizing applications, adjusting to new hardware, and solving common problems. - Comprehensive introduction to parallel programming with CUDA, for readers new to both - Detailed instructions help readers optimize the CUDA software development kit - Practical techniques illustrate working with memory, threads, algorithms, resources, and more - Covers CUDA on multiple hardware platforms: Mac, Linux and Windows with several NVIDIA chipsets - Each chapter includes exercises to test reader knowledge

Product Details

ISBN-13:	9780124159884
Publisher:	Morgan Kaufmann Publishers
Publication date:	12/28/2012
Series:	Applications of GPU Computing Series
Sold by:	Barnes & Noble
Format:	eBook
Pages:	600
File size:	8 MB

About the Author

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.

Table of Contents

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

What People are Saying About This

From the Publisher

Provides a solid foundation for developers learning parallel programming with CUDA

Editorial Reviews

"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

From the Publisher