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CUDA for Engineers gives you direct, hands-on engagement with personal, high-performance parallel computing, enabling you to do computations on a gaming-level PC that would have required a supercomputer just a few years ago.
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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.
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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.
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Coverage includes
- Preparing your computer to run CUDA programs
- Understanding CUDA’s parallelism model and C extensions
- Transferring data between CPU and GPU
- Managing timing, profiling, error handling, and debugging
- Creating 2D grids
- Interoperating with OpenGL to provide real-time user interactivity
- Performing basic simulations with differential equations
- Using stencils to manage related computations across threads
- Exploiting CUDA’s shared memory capability to enhance performance
- Interacting with 3D data: slicing, volume rendering, and ray casting
- Using CUDA libraries
- Finding more CUDA resources and code
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Realistic example applications include
- Visualizing functions in 2D and 3D
- Solving differential equations while changing initial or boundary conditions
- Viewing/processing images or image stacks
- Computing inner products and centroids
- Solving systems of linear algebraic equations
- Monte-Carlo computations
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- Sales Rank: #172481 in Books
- Published on: 2015-11-12
- Original language: English
- Number of items: 1
- Dimensions: 9.10" h x .90" w x 7.30" l, .0 pounds
- Binding: Paperback
- 352 pages
About the Author
Duane Storti is a professor of mechanical engineering at the University of Washington in Seattle. He has thirty-five years of experience in teaching and research in the areas of engineering mathematics, dynamics and vibrations, computer-aided design, 3D printing, and applied GPU computing.
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Mete Yurtoglu is currently pursuing an M.S. in applied mathematics and a Ph.D. in mechanical engineering at the University of Washington in Seattle. His research interests include GPU-based methods for computer vision and machine learning.
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Most helpful customer reviews
5 of 5 people found the following review helpful.
How does it compare to NVIDIAs free stuff?
By Let's Compare Options Preptorial
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!
5 of 6 people found the following review helpful.
the perfect level of explination 4.5 stars
By Vivace
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.
2 of 2 people found the following review helpful.
Excellent introduction to High-Performance Parallel Computing
By Lanmi
This is with no doubt a great book for anyone who intends to learn and use CUDA.
The authors introduce the essentials of CUDA C programming clearly and concisely.
Generally, I was interested in 3D visualisation with OpenGL and CUDA and was glad that I've found in this book what I was looking for.
It helped me a lot. The codes in the book are written with clarity. Also the use of tools for profiling and performance optimization are discussed.
This is an excellent introduction to High-Performance Parallel Computing.
See all 5 customer reviews...
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