Udacity CS344: Intro to Parallel Programming

What: Intro to Parallel Programming is a free online course created by NVIDIA and Udacity. In this class you will learn the fundamentals of parallel computing using the CUDA parallel computing platform and programming model.

Who: This class is for developers, scientists, engineers, researchers and students who want to learn about GPU programming, algorithms, and optimization techniques.

Why: Learn new skills, enhance your career opportunities, and have fun.

How: Currently this class is closed - but please visit us again soon for any updates.

Instructors

Dr. David Luebke
Senior Director at NVIDIA Research
Read more about David:
homepage, twitter: @davedotluebke
Dr. John Owens
Associate Professor at UC Davis
Read more about John:
homepage, twitter: @jowens

 

 

 

 

 

Instructional Design Advisor

Dr. Dan Connors
Assistant Professor at University of Colorado Denver
Adjunct Professor at University of Colorado Boulder
homepage

 

Class Syllabus:

  • Lesson 1 - GPU Programming Model
  • Lesson 2 - GPU Hardware and Parallel Communication
  • Lesson 3 - Fundamental Parallel Algorithms
  • Lesson 4 - Using Sort and Scan
  • Lesson 5 - Optimizing GPU Programs
  • Lesson 6 - Parallel Computing Patterns
  • Lesson 7 - The Future of GPU Computing

 

What People are saying:

Simon Brown @sjb3d : Completed the @Udacity parallel programming #CS344 online course. Very well taught and great #CUDA challenges at the end of each unit

Johan Louwers @johanlouwers:  Free #udacity course on Introduction to Parallel GPU Programming with #CUDA. Excellent lessons and homework/exercises.

Daivik Orth: This Course was excellent. My internship employer wanted me to parallelize some of their code using CUDA. I had no idea what CUDA was going into it. This course was critical for my success this summer.

 

FAQ

What level is this course?
This is an advanced course.

What are the pre-requisites?
Working knowledge of the C programming language, data structures and common algorithms.

How long will it take to complete the course?
Estimated time to complete course: 7 units x avg. of 3 hours each = Approx. 21 hours.

Do I need a CUDA-enabled computer?
No. You'll be able to program and run your assignments on high-end GPUs, via the Udacity system.

When does the course begin?
This class is self-paced. You can begin whenever you like.

How long will the course be available?
This class will always be available.

How much does this cost?
It’s completely free! If you’re feeling generous, we would love to have you contribute your thoughts, questions, and answers to the course discussion forum.

What are the rules on collaboration?
Collaboration is a great way to learn. The key is to use collaboration as a way to enhance learning, not as a way of sharing answers without understanding them.

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