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Professional CUDA C Programming Free Download



Designed for professionals across multiple industrial sectors, Professional CUDA C Programming presents CUDA -- a parallel computing platform and programming model designed to ease the development of GPU programming -- fundamentals in an easy-to-follow format, and teaches readers how to think in parallel and implement parallel algorithms on GPUs. Each chapter covers a specific topic, and includes workable examples that demonstrate the development process, allowing readers to explore both the "hard" and "soft" aspects of GPU programming.




Professional CUDA C Programming free download



Computing architectures are experiencing a fundamental shift toward scalable parallel computing motivated by application requirements in industry and science. This book demonstrates the challenges of efficiently utilizing compute resources at peak performance, presents modern techniques for tackling these challenges, while increasing accessibility for professionals who are not necessarily parallel programming experts. The CUDA programming model and tools empower developers to write high-performance applications on a scalable, parallel computing platform: the GPU. However, CUDA itself can be difficult to learn without extensive programming experience. Recognized CUDA authorities John Cheng, Max Grossman, and Ty McKercher guide readers through essential GPU programming skills and best practices in Professional CUDA C Programming, including:


Designed for professionals across multiple industrial sectors, Professional CUDA C Programming presents CUDA -- a parallel computing platform and programming model designed to ease the development of GPU programming -- fundamentals in an easy-to-follow format, and teaches readers how to think in parallel and implement parallel algorithms on GPUs. Each chapter covers a specific topic, and includes workable examples that demonstrate the development process, allowing readers to explore both the "hard" and "soft" aspects of GPU programming.


Computing architectures are experiencing a fundamental shift toward scalable parallel computing motivated by application requirements in industry and science. This book demonstrates the challenges of efficiently utilizing compute resources at peak performance, presents modern techniques for tackling these challenges, while increasing accessibility for professionals who are not necessarily parallel programming experts. The CUDA programming model and tools empower developers to write high-performance applications on a scalable, parallel computing platform: the GPU. However, CUDA itself can be difficult to learn without extensive programming experience. Recognized CUDA authorities John Cheng, Max Grossman, and Ty McKercher guide readers through essential GPU programming skills and best practices in Professional CUDA C Programming, including:


What is CUDA? CUDA is a model created by Nvidia for parallel computing platform and application programming interface. CUDA is the parallel computing architecture of NVIDIA which allows for dramatic increases in computing performance by harnessing the power of the GPU. What is Google Colab? Google Colab is a free cloud service and the most important feature able to distinguish Colab from other free cloud services is; Colab offers GPU and is completely free! With Colab you can work on the GPU with CUDA C/C++ for free!CUDA code will not run on AMD CPU or Intel HD graphics unless you have NVIDIA hardware inside your machine.On Colab you can take advantage of Nvidia GPU as well as being a fully functional Jupyter Notebook with pre-installed Tensorflow and some other ML/DL tools.


There are some general requirements which are common regardless of the operating system you use. The most important one is a CUDA-capable GPU, so you need a recent NVIDIA graphics card. A full list is available here. The other requirement is to have the CUDA Toolkit, which is free and provides you all you need to install and run CUDA code. By clicking on this link you will open the download page. Choose your operating system (in this case I presume Mac OS X) and then download the package by clicking on PKG.


If you want to install CUDA 6.0 on a Mac you need to have OS X 10.8 (Lion) or later running on your computer. If you don't, you can download Mavericks (OS X 10.9) from the App Store, which is free. In the situation where you cannot install OS X 10.8 or later, don't worry: you can install older CUDA releases. The installation is similar, but if some of you experience trouble, I will provide help in future articles.


In order to use CUDA 6.0 you need the GNU Compiler Collection (gcc) and clang on your Mac. They are compliers for the C-family of programming languages and CUDA is a library for them. Actually, you will code the "serial" (non-parallel) parts of your code in C or C++. To get these two compilers you first need the Command Line Tools, which once again are free to download from the App Store.


Since it will get boring to follow this procedure every time you want to use CUDA, you can download gfxCardStatus (which is free and open-source) and switch between graphics cards in a couple of clicks directly from your menu bar, as you can see in the following picture. Actually, this was my choice and I'm pretty satisfied with how it works. Also, it could be useful to have direct control of graphics switching regardless of CUDA.


The University provides downloadable software packages for use on University-owned computers free of charge to staff and students. In some instances, software may also be installed on personal devices.


It is never too late to start learning and it would be a shame to miss an opportunity to learn a tutorial or course that can be so useful as C Notes for Professionals book especially when it is free! You do not have to register for expensive classes and travel from one part of town to another to take classes. All you need to do is download the course and open the PDF file. This specific program is classified in the C-C++ language category where you can find some other similar courses.


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