PDF Ebook Professional CUDA C Programming
PDF Ebook Professional CUDA C Programming
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Professional CUDA C Programming
PDF Ebook Professional CUDA C Programming
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From the Back Cover
Break into the powerful world of parallel computing Focused on the essential aspects of CUDA, Professional CUDA C Programming offers down-to-earth coverage of parallel computing. Packed with examples and exercises that help you see code, real-world applications, and try out new skills, this resource makes the complex concepts of parallel computing accessible and easy to understand. Each chapter is organized around one central topic, and includes workable examples that demonstrate the development process, allowing you to measure significant performance gains while exploring all aspects of GPU programming. Professional CUDA C Programming: Focuses on GPU programming skills and best practices that deliver outstanding performance Shows you how to think in parallel Turns complex subjects into easy-to-understand concepts Makes information accessible across multiple industrial sectors Features helpful examples and exercises in each chapter Covers the essentials for those who are not experts in C programming wrox.com Programmer Forums Join our Programmer to Programmer forums to ask and answer programming questions about this book, join discussions on the hottest topics in the industry, and connect with fellow programmers from around the world. Code Downloads Take advantage of free code samples from this book, as well as code samples from hundreds of other books, all ready to use. Read More Find articles, e-books, sample chapters, and tables of contents for hundreds of books, and more reference resources on programming topics that matter to you.
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About the Author
John Cheng, PHD, is a Research Scientist at BGP International in Houston. He has developed seismic imaging products with GPU technology and many high-performance parallel production applications on heterogeneous computing-platforms. Max Grossman is an expert in GPU computing with experience applying CUDA to problems in medical imaging, machine learning, geophysics, and more. Ty McKercher has been helping customers adopt GPU acceleration technologies while he has been employed at NVIDIA since 2008.
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Product details
Paperback: 528 pages
Publisher: Wrox; 1 edition (September 9, 2014)
Language: English
ISBN-10: 1118739329
ISBN-13: 978-1118739327
Product Dimensions:
7.2 x 1.2 x 9.2 inches
Shipping Weight: 2 pounds (View shipping rates and policies)
Average Customer Review:
4.8 out of 5 stars
18 customer reviews
Amazon Best Sellers Rank:
#367,748 in Books (See Top 100 in Books)
Before getting into this book, you might want to have at least some high-level overview of CUDA.The authors explain not only how to program in CUDA but also how to get the maximum performance out of the hardware. Issues such as branch divergence, uncoalesced memory, etc. are beautifully explained by means of coding examples. The authors also explain how to use NVIDIA profiling tools to assess the performance of your programs.On the other hand, I was somewhat surprised that the book goes deep into the Fermi (2.x) and Kepler (3.x) architectures, but has no mention of the latest Maxwell (5.x) architecture. I guess that's because the authors use Tesla GPUs (not the usual GeForce GT/GTX card), but anyway the code is portable across devices.I used a GeForce GTX 750 Ti and this required some minor adaptations. Since my card had only 2GB of memory, in those examples where the authors allocate 3 vectors of 1GB each, I simply ran out of memory. The fix was to use a smaller vector size, and things ran smoothly again. Also, since my card was a Maxwell, I used nvcc -arch=sm_50 to compile the code samples. Some minor warnings were easy to fix.Recommended for the serious/advanced programmer.
Very pleased with this. I use it along with the book by Sanders & Kandrot. I get to write working code for all the concepts. The diagrams are very helpful. The instructions for how to set up your system are so good that even though the book is for Linux I had no trouble using Windows instead. If you are a Windows user I would encourage you to still buy this book. Once you get into the subject the OS makes no difference. I appreciate how they provide code to verify and test what you are doing. There is a lot of discussion about optimization and you learn a lot experimenting. It is becoming one of those books that I'm marking with tons of notes and looking like an old shoe.
Before I buy a book I usually read the Introduction so I can figure out what audience the book is made for and whether it might be a good fit for me. In the introduction of this book, it says (among other possible audiences) that the book does not assume 'copious amounts of experience in C programming'. I'd say that was stretching it a little bit since it begins using pointers and bitwise operations in the second chapter. That said, as a relative newcomer to C programming (having used PHP, Java, and Python previously), I did not understand what those things were so it took a bit of learning on the side. I found that what worked for me was to grab a C Programming book, read a couple chapters into it to get a basic understanding, and then reference the appropriate chapters when I came across something new. Then I simply commented what I learned into the code so that I could look at it later and quickly be reminded what worked for me.All of that said - as a review for the book, I love it. It explains why some things are better than others, often down to the hardware level, which really helps to grasp the material. The examples work great and I usually input them and change some things around to make sure I get a solid understanding of it. I haven't yet got to it, but Chapter 8 goes over various libraries, with cuFFT being the one I'm particularly interested in - I count that as a big plus even if they're just basic introductions. Highly recommended!
Awesome book!!! easy to understand, code works way better and easier in VS2013 than the one in CUDA by example, also tried the code in OpenSuse 13.1 and it worked nicely in my 780m card, also the excersises are fun to try, I totally recomend this book!! note that its examples are mostly run in Linux, and is focused in Fermi and Kepler archs and the runtime API not the driver API
This is a great place to learn CUDA programming. It may even be helpful to people new to CUDA, but understand the principles CUDA is built on.
Covers many important aspects in CUDA C programming, nice set of examples although some of them need minor code corrections to run. recommended for all levels of programmers.
Excellent product.
It provides all required knowledge to work on GPU programming.
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