CUDA Programming: A Developer's Guide to Parallel Computing with GPUs
Autor Shane Cooken Limba Engleză Paperback – 7 dec 2012
- 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
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Specificații
ISBN-13: 9780124159334
ISBN-10: 0124159338
Pagini: 592
Dimensiuni: 191 x 235 x 25 mm
Greutate: 1.01 kg
Editura: ELSEVIER SCIENCE
ISBN-10: 0124159338
Pagini: 592
Dimensiuni: 191 x 235 x 25 mm
Greutate: 1.01 kg
Editura: ELSEVIER SCIENCE
Public țintă
Software engineers, programmers, hardware engineers, students / advanced studentsCuprins
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
Recenzii
"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
"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