This year, Spring 2020, CS179 is taught online, like the other Caltech classes, due to COVID-19.
The course is "live" and ready to go, starting on Monday, April 6, 2020. We won't be presenting video recordings or live lectures. The materials and slides are intended to be self-contained, found below.
The web page you're viewing is the main webpage for the course.
Due Dates & Office Hours
As of May 14, 2020, we will soon be transitioning to "appointment" style TA office hours. The purpose is for quick questions about your project, now that the assignments are mostly finished. Let a TA know by email that you will be wanting some help, ahead of time, for their regular office hours, or perhaps at other times.
Based on the 2020 Piazza surveys, homework sets are due Tuesdays 3 pm. The following dates should match the homework due dates on Piazza.
Office hours are on Zoom, every week. Times are in Pacific Time. See Piazza for Zoom links. As mentioned above, we may be transitioning to "appointment-style" office hours for your projects for quick questions as of May 14, since the homework assignments are mainly finished now.
Email a TA ahead of time to confirm, but they can likely be available at other times too, for quick questions to help you with your project, if you can set up a Zoom time with them in email.
|Course Time and Place:|| The course time and place will be primarily online
through this website! Check for links!
In other years, ordinarily MWF 3:00-3:55 PM
|Announcements:|| There may be additional resources for the course on
A Piazza link has been set up, CS179 Piazza, 2020.
Piazza will be the main forum for discussion, so it is important to make sure you are enrolled there!
For office hours with the TAs, we will be using Zoom, which allows screen sharing and real-time discussion. The Zoom meeting links will be posted on Piazza. It will be good for you to set up your Zoom account or system now, if this hasn't been done already.
You will be submitting your assignments on a GPU-enabled remote computer located at Caltech in the Barr lab.
The computer accounts have been set up now, and you should have received an email. Please test your accounts, and let us know if there is trouble!
The use of Graphics Processing Units for rendering is well
known, but their power for general parallel computation has only
recently been explored. Parallel algorithms running on GPUs can
often achieve up to 100x speedup over similar CPU algorithms,
with many existing applications for physics simulations, signal
processing, financial modeling, neural networks, and countless
This course covers programming techniques for the GPU. The course will introduce NVIDIA's parallel computing language, CUDA. Beyond covering the CUDA programming model and syntax, the course will also discuss GPU architecture, high performance computing on GPUs, parallel algorithms, CUDA libraries, and applications of GPU computing.
Problem sets cover performance optimization and a few specific example GPU applications such as numerical mathematics, medical imaging, finance, and other fields, ending with a 4-week project of the student's choice.
This quarter we will also cover uses of the GPU in Machine Learning.
Labwork will require significant programming. A working knowledge of the C programming language will be necessary. Although CS 24 is not a prerequisite, it (or equivalent systems programming experience) is strongly recommended.
9 units; third term.
George Stathopoulos - email@example.com
Ethan Jaszewski - firstname.lastname@example.org
Alden Rogers- @email@example.com
|Supervising Professor:|| Professor
Al Barr - firstname.lastname@example.org
|Programming Guide and Textbook:|| Useful to consult!
CUDA Programming Guide
Also perhaps? Programming Massively Parallel Processors (3rd Edition) is recommended but not required. Amazon Link.
We have posted Zoom links for Zoom screen
sharing and interactive Zoom sessions with the TAs.
Students and TAs are in multiple time zones, so it was tricky to set up office hour times that were convenient enough.
As of May 14, we will be transitioning more to "appointment-style" office hours, now that the assignments are mainly finished. The purpose is for quick questions to help you with your projects. Email a TA ahead of time, and other times can likely be available.
Again, the original Office hours were:
|Piazza and HW Submission:||
|Grading policy:|| Here is the grading scheme for the class:
Homework extensions may be granted if the TAs see it as appropriate. E grades will not be granted except under extreme circumstances.
Please contact the Deans when
there are unusual or difficult circumstances, since the Deans can
give us more leeway than usual, if they feel your circumstance
Week 1 (Introduction), MWF 3pm PDT
Note that there are no "live" lectures. The course is intended to be self-contained, below, and on Piazza.
Lecture 1 (Mon. 04/06): PPT PDF
(... The following are still being set up for 2020!) Lecture 2 (Wed. 04/08): PPT PDF
Lecture 3 (Fri. 04/10): PPT PDF
Week 2 (Shared Memory), MWF 3pm PDT
Lecture 4 (Mon. 04/13): PPT PDF
Lecture 5 (Wed. 04/15): PPT PDF
Lecture 6 (Fri. 04/17): PPT PDF
Week 3 (Reductions, FFT) MWF 3pm PDT
Lecture 7 (Mon. 04/20): PPT PDF
Lecture 8 (Wed. 04/22): PPT PDF
Lecture 9 (Fri. 04/24): PPT PDF
Week 4 (cuBLAS and Graphics) MWF 3pm PDT
Lecture 10 (Mon. 04/27): PPT PDF Google Doc
Lecture 11 (Wed. 04/29): cuBLAS example
Lecture 12 (Fri. 05/1): PPT PDF
Week 5 (Machine Learning and cuDNN I) MWF 3pm PDT
Lecture 13 (Mon. 05/04): PPT PDF
Lecture 14 (Wed. 05/06): PPT PDF
Lecture 15 (Fri. 05/08): PPT PDF
Week 6 (Machine Learning and cuDNN II) MWF 3pm PDT
Lecture 16 (Mon. 05/11): PPT PDF
Lecture 17 (Wed. 05/13): PPT PDF
Week 7 (Projects) MWF no class. Zoom discussions.
Week 8 (Projects) MWF no class. Zoom discussions.
Week 9 (Projects) MWF no class. Zoom discussions.
Week 10 (Projects) MWF no class. Zoom discussions.
|CUDA Installation|| There is a Caltech computer to remotely log into, in
the Barr lab in Annenberg, where you should already have the computer
name, an account and
remote access to it.
For installing CUDA onto your own computer, you could also
consider this Guide
(In 2019) DANGER! Especially for older non-Windows machines, make
a Clone of the whole computer system before attempting installation!
(In 2019) DANGER! Especially for older non-Windows machines, make a Clone of the whole computer system before attempting installation!Don't try to install CUDA casually. You can easily lose your ability to log in and your entire laptop/desktop environment without this type of backup! The loss of a working computer environment can affect your other classes. With the clone backup, however, you should not lose too much time if there was a problem. But you should know how to clone your computer and then restore it!
Using the Barr machine in Annenberg will be a safer option.
FYI, Ubuntu 20.04 will be coming out shortly, and the installation process for CUDA will be a lot easier and safer, than on older Unix systems. We will try to create an instruction sheet, on how to do this.
To do the full partition backup, a suggested cloning tool is
where you can use these
Clonezilla instructions as a reminder.
Other cloning and burning tools are acceptable, if you have your own favorites.
Finally, use this code to retrieve your hardware info after you setup CUDA.
CUDA C Programming Guide
List of NVIDIA GPUs
Mapping from GPU name to Compute Capability
|Material from previous year(s):||
|Less useful, but cool resources:||
NVIDIA's Parallel Forall Blog
Videos from last several years of NVIDIA's conference on CUDA
How to Write Code the Compiler Can Actually Optimize (2015)
Excellent CPU optimization manuals
What Every Programmer Should Know About Memory
GPU focused systems guide to deep learning