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.
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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.Review:
"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
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