Online Course
Intro to Parallel Programming
Nvidia and University of California, Davis via Udacity
Overview
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.
Why Take This Course?
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.
Syllabus
Lesson 1: GPU Programming Model
Project 1: Greyscale Conversion (for that classy touch!)
Lesson 2: GPU Hardware and Parallel Communication
Project 2: Smart Blurring (miracle product for removing wrinkles!)
Lesson 3: Fundamental Parallel Algorithms
Project 3: HDR Tonemapping (when 1000:1 contrast is not enough!)
Lesson 4: Using Sort and Scan
Project 4: Red Eye Removal (soothing relief for bright red eyes)
Lesson 5: Optimizing GPU Programs
Project 5: Accelerating Histograms (when fast isn't fast enough)
Lesson 6: Parallel Computing Patterns
Project 6: Seamless Image Compositing (polar bear in the swimming pool)
Lesson 7: The Frontiers and Future of GPU Computing
Taught by
John Owens and David Luebke
Tags
Reviews
3.4 rating, based on 7 reviews
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Otto Smith completed this course.
This is a great course for getting an overview of parallel computer algorithms. It is also a good course for learning cuda. It really requires a NVIDIA processor on your own machine. Debugging on the remote servers is quite difficult. I installed... -
Stephan Schulte completed this course, spending 5 hours a week on it and found the course difficulty to be medium.
Really good course. Unfortunately the IDE is not working anymore and for the last year there is no update of the state of the issue!
You still can use the course with your own hardware, but you will not pass the corresponding exams. -
Soares Chen completed this course, spending 8 hours a week on it and found the course difficulty to be medium.
Good introduction to CUDA programming. It assume prior knowledge in C/C++ programming. The course introduce most of the basic CUDA concepts such as memory coalescing and streaming multiprocessor. -
Shaodong Qin
Shaodong Qin completed this course.
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Colin Khein completed this course.
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Anonymous
Anonymous completed this course.
A very basic class on parallel programming. Very low emphasis on hardware/device architecture, most of it being pushed to the second half of the class. -
Thomas Kamara completed this course.
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