Logistics
Time and Location
Monday and Wednesday, 12:30-1:50pm, POS A35 (in-person expected)
Office Hours
| Day | Time | Location | Instructor/TA |
|---|---|---|---|
| Monday | 4pm - 5pm | GHC 5417 | Sreeram Vennam |
| Tuesday | 2pm - 3pm | GHC 5417 | Jackey Hua |
| Thursday | 1pm - 2pm | GHC 5417 | Danqing Wang |
| Friday | 2pm - 3pm | GHC 5417 | Adi Tummala |
Prerequisites
- math knowledge (linear algebra, calculus, and probability&stats)
- programming skills in Python and C, C++, or Java (15122 or equivalent)
- prior knowledge of machine learning is preferred but not required.
Class Format
Each class may contain some combination of the following
- Lectures
- Code-walk throuth (if available)
- Small quiz problems
- Homework review
Occasionally, we will invite industrial speakers to present latest advancement and engineering practice in building real LLM systems.
We will have optional recitation sessions on Fridays.
Discussion Forum
We will use the Ed platform for discussions, but coming to office hours is also encouraged. Please do not send email to individual TA/Instructor.
Textbook and Course Material
No text book is required. A select set of recent papers on LLM systems and algorithms will be provided. Students are expected to read the assigned material and papers before each lecture. If you are not familiar with GPU programming, you are recommended to read this excellent book.
Programming Massively Parallel Processors, 4th Ed
Please login using your andrew email for free access.
Homework, Exam and Grading
The course will have five required and two optional programming assignments, and a course project.
| Percentage | |
|---|---|
| Homework | 44% in total(+5% optional) |
| Quiz | 10% |
| Participation | 2% (+ up to 5%) |
| Project | 44% |
You earn participation credits if you have >=5 posts/answers or you have at least one endorsed answer on edstem. You will receive participation bonus if you find typos/bugs in existing homework assignments, submit a pull request to fix, and your pull request is merged.
Required Reading
Students are highly encouraged to read the material or paper before each session.
Computing Resources
We will also provide access to Pittsburgh Supercomputing clusters (PSC). Students may also use Google co-lab if they are new users.
Disclaimer: PSC uses job-based scheduling. There is no guarantee that a job will be running within certain time-limit. Please start working on the assignments and submit your job early.
Policies
Late Day Policy
Each student could use at most 3 late days without penalty for the whole semester (counted in full day!). After that, each additional late day incurs a penalty of 20%. NO late day is allowed for final project report (due to final grading deadline).
We still encourage everybody to complete their work by the designated deadlines. This prevents cascading tardiness from overwhelming both students and teaching staff.
Extensions However, sometimes there are situations that call for extensions. Some examples from the last few years include the following:
- The death of friend or family member
- A wedding in the family
- A serious accident
- A surgery
- A significant illness (overnight hospital stay)
- A mental health crisis or episode
- An important religious or national holiday
We care about you and your well being more than we care about deadlines and if something difficult is happening in your life which is making it hard for you to complete an assignment on time please contact us so we can talk. We have found that, often, the students who most need some leeway are those least likely to ask for it. It never hurts to ask. We will work out a plan so you can complete the requirements of the course with your physical and psychological health intact. Do not feel ashamed to reach out to us. We are eager to see you succeed.
Academic integrity
Any cheating or plagiarism will be dealt with according to the University policies on academic integrity. In general, high-level discussion of tools, concepts, and formalisms is acceptable collaboration and is encouraged. Sharing specific aspects of solutions or results with other students, or consulting work from previous semesters or other universities, is considered cheating. Using Github copilot or any AI agent is ok for explanation purpose. For best learning experience, you should write your own code, debug it, and explain it. You are fully responsible for and should be able to explain any content you submit.
Disability
Many people have disabilities, including members of our own families. We see disabilities as deficits not in disabled people but in the institutions and societies that are structured such that they are disadvantaged. We wish to do our part to overcome this disparate treatment. If you have a disability (visible or invisible), please let us know as soon as possible (you don’t need to tell us the nature of the disability) and work with Disability Service to develop a set of accommodations which we can then approve. These may include extra time on exams, a quiet place in which to take an exam, alt text on all images, documents that work for people with differences in vision, sign language interpretation, captioning, etc.
Diversity, Equity, and Inclusion
Throughout human history, some people have been denied the rights and opportunities available to others on the basis of their race, gender, economic class, caste, ancestry, language community, age, religion, beliefs, political affiliation, and abilities (visible and invisible). A single course cannot undo the injustices of history, but we—as a teaching staff—are committed to fighting inequity and promoting inclusion. We encourage you to join us. If you feel that you, or those around you, have been treated unfairly based upon their identity (or perceived identity) by us, by other members of the teaching staff, or by other students in the course, we ask that you share your experience with Ethics Reporting Hotline. Students, faculty, and staff can anonymously file a report by calling 844-587-0793 or visiting cmu.ethicspoint.com.