Using CUDA to Harness the Power of GPUsStart Free Course
Learn the fundamentals of parallel computing with the GPU and the CUDA programming environment! In this class, you'll learn about parallel programming by coding a series of image processing algorithms, such as you might find in Photoshop or Instagram. You'll be able to program and run your assignments on high-end GPUs, even if you don't own one yourself.
Why It’s Important to Think Parallel
Third Pillar of Science
Learn how scientific discovery can be accelerated by combining theory and experimentation with computing to fight cancer, prevent heart attacks, and spur new advances in robotic surgery.
This free course is your first step towards a new career with the Machine Learning Engineer Nanodegree Program.
Enhance your skill set and boost your hirability through innovative, independent learning.
Project 1: Greyscale Conversion (for that classy touch!)
Project 2: Smart Blurring (miracle product for removing wrinkles!)
Project 3: HDR Tonemapping (when 1000:1 contrast is not enough!)
Project 4: Red Eye Removal (soothing relief for bright red eyes)
Project 5: Accelerating Histograms (when fast isn't fast enough)
Project 6: Seamless Image Compositing (polar bear in the swimming pool)
We expect students to have a solid experience with the C programming language and basic knowledge of data structures and algorithms.
See the Technology Requirements for using Udacity.
You'll master the fundamentals of massively parallel computing by using CUDA C/C++ to program modern GPUs. You'll learn the GPU programming model and architecture, key algorithms and parallel programming patterns, and optimization techniques. Your assignments will illustrate these concepts through image processing applications, but this is a parallel computing course and what you learn will translate to any application domain. Most of all we hope you'll learn how to think in parallel.