GPU Programming

CS 179

This year, Spring 2020, CS179 will be taught online, like the other Caltech classes, due to COVID-19.

The course should be "live" and nearly 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.

There *will* be some surveys posted on Piazza, on which day your homework assignments should be due, and also a survey about Zoom meeting scheduling, for interactive office hours with the TAs, with Zoom screen sharing. We're all in different timezones, so the times are listed in PDT, Caltech's timezone. (Please test out your Zoom accounts, ahead of time!)

Meanwhile, the website is still being set up for the online instruction this quarter.

Note that homework will start to be due sometime the second week, on or after Monday April 13, 2020, despite dates in the links. We will select the day after we get preferences from you in the survey, about a preferred day-of-week. In previous years, Wednesday 3pm PDT has been the customary time and date for HW submissions.

A Piazza link has also 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!

The TAs and the CS179 students enrolled on Regis as of 6pm PDT March 26 have been added to the Piazza list.

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.

A survey for office hour preferences should soon be posted on Piazza, and another survey on preferred HW submission day.

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!

This course will cover 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 will cover performance optimization and specific GPU applications such as numerical mathematics, medical imaging, finance, and other fields.

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.

Students and TAs will be in multiple time zones, so it may be tricky to set up office hour times that are convenient.

A survey for office hour time preferences should soon be posted on Piazza.

• TBD (Ethan)
• TBD (Alden)
• TBD (George)
• Zoom link TBD, to be found on Piazza.

• Piazza Please ask through Piazza if you have a question/issue that likely affects other students. Piazza will be used to make announcements throughout the course.
• Global TA email: cs179.ta@gmail.com.
  But use Piazza, generally, for questions on the assignments or the material. These may be of interest to other people. Send an email to the TAs if you have something that only affects you or your project group.
• HW submission: You will be logging in remotely onto a Caltech computer in the Barr lab to submit your work. The TAs will log in there to test your work and grade it.
• IMPORTANT: updated for 2020 Instead of emailing your lab solution to the TA email, please put a zip file of your solution in your home directory on the remote Barr-lab computer, with the name lab[N]_2020_submission.zip .

  See Piazza for more details!

  Again, homework will start being due the second week, sometime on or after Monday April 13, 2020, after we post the survey and get its results.

• 6 labs (60% of grade) (but in five weeks, due to COVID-19)
• 4 week project (40% of grade)

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 warrants it.  

Note! To pass the course, a "sufficient" number of "good" assignments will need to be submitted and graded before Drop Day!!! That's May 20, 2020. If you're behind or not doing well, please drop the course before Drop Day!! We may send out a "scary note" in email near that time, to students who may be in trouble of not passing, and might need to drop.

For installing CUDA onto your own computer, you could also consider this Guide

(updated 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 Clonezilla, where you can use these Clonezilla instructions as a reminder.
An excellent USB "burning" tool (for making a Clonezilla drive or the CUDA boot drive) is Rufus, although it requires a Windows environment to run it .

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.