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CUDA for Engineers: An Introduction to High-Performance Parallel Computing
Free Download CUDA for Engineers: An Introduction to High-Performance Parallel Computing
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The authors introduce the essentials of CUDA C programming clearly and concisely, quickly guiding you from running sample programs to building your own code. Throughout, you’ll learn from complete examples you can build, run, and modify, complemented by additional projects that deepen your understanding. All projects are fully developed, with detailed building instructions for all major platforms.
Ideal for any scientist, engineer, or student with at least introductory programming experience, this guide assumes no specialized background in GPU-based or parallel computing. In an appendix, the authors also present a refresher on C programming for those who need it.
Coverage includes
Realistic example applications include
Product details
Paperback: 352 pages
Publisher: Addison-Wesley Professional; 1 edition (November 12, 2015)
Language: English
ISBN-10: 013417741X
ISBN-13: 978-0134177410
Product Dimensions:
7.4 x 0.8 x 9.1 inches
Shipping Weight: 1.3 pounds (View shipping rates and policies)
Average Customer Review:
4.3 out of 5 stars
11 customer reviews
Amazon Best Sellers Rank:
#261,315 in Books (See Top 100 in Books)
This is an outstanding, relevant and timely text. However, the real question is: for the price, how does it compare to the plethora of FREE materials online from NVIDIA?First, registration and access to the online supplements from Addison at informit dot com is a MUST. As anyone knows who has mastered a new language, "wash on, wash off" (intense, repeated skill practice) is a must, and the steps to proficiency (especially if you know some C or C++), are really here with the online examples in addition to those in the book.On relevance, the battle between GPU and CPU supercomputing/ hpc is hardly over, regardless of IBM and Intel "flooring" the processing Ferrari with new GPU competitors. Just look at DOD buying truckloads of gaming platforms lately (including gaming systems, not just Alienware!) to pull and wire the GPUs into workstation HPCs. Buyers have to decide if the GPU phenomenon will last the next 10 years in HPC, but NVIDIA is investing SO much in this community that even hybrid CPU/GPUs like the old math co-processor dance and integration will NOT obviate you getting into this now, even if your career is just starting.I teach HPC online, but more from the circuit/CPU/GPU side than code, so my students are more C types from that point of view. Even so, if you don't have access to Argonne, Los Alamos or NOAA, this text clearly gives you the skills to work at differential equation level on gaming workstations, which is enough to get your projects to the stage of sharing time on the bigger machines. The text covers all the topics you need, in fine fashion, to understand the GPU angle of HPC, without making it so specific that it won't help your overall HPC skills.Now, the budget question: since NVIDIA has been so up front, generous and frankly prolific in supporting the GPU HPC community, why can't you just get the CUDA manual and find the rest of this text's topics online? IMHO that is a non-issue, and would be a big mistake. These authors have made painstaking efforts to structure this text in a way that builds from basic setup and SDK to advanced topics, in an order that is far more didactic than just looking up topics manual style. If you're already a guru in this area, this will be less helpful, but my students, many of whom are graduate Engineers, LOVE the approach of this text. Issues that seem easy to 20,000 CPUs like thread independence vs. competition vs. cooperation become important with GPUs and GPU/CPU combinations, for example. You can look this up with NVIDIA's web help, but the material here gives you the resources to save hours by taking the discussion from basic to advanced in a stepwise fashion that a manual + web search won't do.Highly recommended. If this were a Springer or dedicated programming publisher's text, it would be $150 US! My recommendation, at the price, is to go for it, even if you're like me and my students: on a serious budget. NVIDIA's "GPU" argument for PetaFLOPS/watt instead of PetaFLOPS may seem spurious or self serving to the casual observer, but if you don't have access to a fusion reactor or gallons of freon, you know that it is a real issue! Besides, a cruncher on your desktop is way cool in other ways, pun intended, and maybe you'll solve Riemann, or figure out lossless, controllable magnetic containment under 150 million degrees with it, I'm betting on you!
I have five other books on CUDA programming going back to 2011 and this is the most comprehensive and well introduced book in this group. It covers a basic introduction, 2D, 3D, shading, use of CUDA libraries and a how to on exploring the full CUDA system of applications with a large list of resources in about 312 pages. WOW.In my recent list of 6 clients in the last two years, 3 of them could have used this knowledge to vastly improve their product but didn't appreciate the value of acquiring the advantages of using CUDA and their projects suffered greatly.Seven years ago I would have dismissed the need for parallel computing in technology companies, but today I say this knowledge is essential for finding many solutions to computing limits. These areas include stock trading, medical image processing, statistical result calculations, deep learning networks, and municipal lighting systems. Don't be ignorant of this computing enhancement!In my personal testing of the starfield simulations, I found a speed enhancement of 112:1 at 80,000 stars using a mid-level NVIDIA GPU. This is like having a supercomputer plug-in available for every desktop computer made in the last ten years for under $700! Incredible!!!
This is an excellent book! a lot of the cuda material out there seems a bit of a disconnect in terms of integration with an engineering development environment. Also I personally find when cuda material in particular makes so few assumptions about the readers level of maturity, easy concepts can become very confusing. I find book really nails the right level of explanation for the right level of the reader. Very happy about this book.I am taking of half a star because I find having different windows and linux versions of of various things covered gets a bit confusing and i think especially wacky ont he kindle.
Good Book. Nothing boring, shows what matters. Like porn movie, closes only what matters
I can't wait until I read it! The other book CUDA BY EXAMPLE was in fact a great account of parallelism and GPU programming. I am in the middle of that book; entreating it with most care and attendance!
A slightly different approach to the basics of Cuda computing.
Quite informative and well written.
This book has less info than what you could easily find in google. I returned it.
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