Key Features

Learn parallel programming principles, practices, and performance analysis in GPU programming

Learn to design and implement optimized parallel algorithms

Learn to write programs in the CUDA language with the latest CUDA toolkit

What You Will Learn

Use CUDA to speed up your applications using machine learning, image processing, linear algebra, and more

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

About

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.

The code files and related files are placed on GitHub at https://github.com/PacktPublishing/Learning-CUDA-10-Programming