Learning-CUDA-10-Programming

Learning CUDA 10 Programming, published by Packt This is the code repository for Learning CUDA 10 Programming, published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.

About the Video Course

Do you want to write GPU-accelerated applications, but don't know how to get started? With CUDA 10, you can easily add GPU processing to your C and C++ projects. CUDA 10 is the de-facto framework used to develop high-performance, GPU-accelerated applications. In this course, you will be introduced to CUDA programming through hands-on examples. CUDA provides a general-purpose programming model which gives you access to the tremendous computational power of modern GPUs, as well as powerful libraries for machine learning, image processing, linear algebra, and parallel algorithms. After working through this course, you will understand the fundamentals of CUDA programming and be able to start using it in your applications right away.

What You Will Learn

Use CUDA to speed up your applications using machine learning, image processing, linear algebra, and more functions
Learn to debug CUDA programs and handle errors
Use optimization techniques to get the maximum performance from your CUDA programs
Master the fundamentals of concurrency and parallel algorithms on GPUs
Learn about the wide range of GPU-accelerated libraries included with CUDA
Learn the next steps you can take to continue building your CUDA skills

Instructions and Navigation

Assumed Knowledge

If you want to learn how to use parallel and high-performance computing techniques to develop modern applications using GPUs and CUDA, then this course is for you. A good understanding of programming in modern C++ (C++17) is required in order to implement the concepts in this course.

Technical Requirements

Minimum Hardware Requirements

OS: Windows, MacOS, or Linux
Processor: any 64-bit Intel or AMD processor
Memory: 2GB of RAM
Storage: 3GB of free space
Storage: 2GB

Recommended Hardware Requirements

For an optimal experience with hands-on labs and other practical activities, we recommend the following configuration:
OS: Windows 10 version 1703 or higher: Home, Professional, Education and Enterprise (LTSC and S are not supported) 1.8 GHz or faster processor. Quad-core or better recommended.
Memory: 2GB; 8GB of RAM recommended (2.5 GB minimum if running on a Virtual Machine)
Storage: Minimum of 800 MB up to 210 GB of disk space depending on the features installed.
Video Card that supports a minimum display resolution of 720p (1280 by 720); Visual Studio will work best at a resolution of WXGA (1366 by 768) or higher.

Software Requirements

OS: Windows, MacOS, or Linux
Processor: any 64-bit Intel or AMD processor
Memory: 8GB of RAM
Storage: 30GB of free space