Syllabus

Note

This is a tentative syllabus and subject to change.

Parallel computing is found everywhere in modern computing. Multi-core CPUs and GPUs, supercomputers, and even mobile devices such as smartphones all provide ways to efficiently utilize parallel processing on these architectures and devices. The goal of this course is to provide an introduction to the foundations of parallel programming and to consider the performance gains and trade-offs involved in implementing and designing parallel computing systems. Specifically, this course will place an emphasis on concepts related to parallel programming on multicore processors. Programming exercises and projects will be implemented using the Go programming language.

Prerequisite: Core Programming, Familiarity with C, Java, and/or Python

Course Staff

Instructor

Teaching Assistants

  • Jacky Dai

  • Ashish Verma

Graders

  • Abhishek Agarwal

  • Edgar Pal

Lecture Structure

You will meet with the instructor once a week. We will use this time to introduce concepts for the current module, review material covered in the pre-recorded lectures, work through exercises, or have a discussion. This class will be fully in-person this means there will be no recorded Zoom sessions or live Zoom sessions, unless if you are required to quarantine due to Covid-19 or a sickness then let your instructor known in advance and he will livestream that day’s lecture and provide you with a link. If you are given permission to livestream then you may not record it and no questions will be If the instructor cannot hold an in-person lecture then the instructor will reach out with alternatives for holding that lecture.

Most weeks, there will be a series of pre-recorded lectures that you must watch after attending lecture. More details are provided in the syllabus.

Section

Time

Location

Instructor

Section #1

T 12:30pm-2:20pm CDT

JCL 011

Samuels

Course Structure

MPCS 52060 will follow a flipped classroom model, where the lecture material and course content will be delivered in two ways:

  • Asynchronous content (i.e., pre-recorded videos): Each week after our normal class time, you will be required to watch pre-recorded videos and do readings. The videos will include lecture content or more advanced coding examples that would be normally covered in a traditional lecture setting (i.e., non-remote setting). These videos and readings will typically range between 1 hour to 1.5 hours and be posted a few days after our scheduled time. Feel free to watch them at your convenience. However, you are required to watch and do the readings before our next meeting.

  • Synchronous content (i.e., in-person lectures) : Each week during our normal class time, we will meet in-person at the time/location designated above. The synchronous lectures will include the following:

    • Introduce concepts for the current module.

    • Followup on the material covered in the videos. The first 15-20 minutes will allow you to ask questions about the content provided in those videos.

    • Have live coding sessions/activities with everyone.

Please see the calendar for more details on what happens on a week to week basis.

Coursework

The course will include weekly homework, one midterm exam, attendance and projects:

  • Homework - The weekly homework assignments will contain practice problems to help enforce the concepts learned during a lecture.

  • Attendance - In order to receive you full attendance weight, you must do the following

    1. Attend every lecture. If you cannot attend a lecture then you must reach out to the instructor letting them know you won’t be able to attend.

    2. You must watch any specified pre-recorded lectures. We will look at the statistics on Panopto to ensure you watch the videos.

    As long as you continuously do those two main points you will receive full credit for attendance.

  • Projects - The projects provide the opportunity to apply the skills you learned to develop systems that can benefit from parallelization. Potential project domains could include: AI and machine learning, computer graphics, cryptocurrency technologies, scientific visualization, etc.

All homework and projects will be due and out on Wednesday @ 11:59pm. Please note that I am normally busy on Friday afternoons and weekends so I may not be available for additional help. Please make sure to take this into consideration as you manage the coursework in this class.

Please see the Assignments Rubric page for more details on how the assignments will be graded.

Exams

There will be one midterm exam in this course. The midterm will be a mixture of coding exercises and short-answer questions. You will be given 24 hours to complete the online exam.

Exam

Time Limit

Out Date

Due Date

Midterm

90 minutes

October 26th @ 12:00pm

October 27th @ 12:00pm

The exact details about how this exam will be administered will come as we approach the exam date. There are no make-up exams in this class. There also will not be any earlier exams taken unless due to extraordinary circumstances such as an medical emergency.

Course Software

Go will be the required language to code all programming assignment. You do not need to know the language in order to take this course. If you haven’t already learned it then you will learn it as the course progresses. It’s a very small and simple language that prior students all had no difficulty learning. I will provide a rationale for the language during the first lecture.

Please make sure you have following software installed for this course:

The course software is also accessible via logging in remotely to a CS Linux Machine. We will explain how to do this during the first week of the quarter.

Grading

Your final grade will be based on the following:

Projects (each 15%)

45%

Homework (each 5%)

30%

Attendance

5%

Midterm

20%

Grades are not curved in this class or, at least, not in the traditional sense. We use a standard set of grade boundaries:

  • 95-100: A

  • 90-95: A-

  • 85-90: B+

  • 80-85: B

  • 75-80: B-

  • 70-75: C+

  • <70: Dealt on a case-by-case basis

We curve only to the extent we might lower the boundaries for one or more letter grades, depending on the distribution of the raw scores. We will not raise the boundaries in response to the distribution.

So, for example, if you have a total score of 82 in the course, you are guaranteed to get, at least, a B (but may potentially get a higher grade if the boundary for a B+ is lowered).

Late submissions

You are allowed to make at most two late submissions on the programming assignments. This is total across all assignments and not on a per-assignment basis. For example, you can use one late submission on homework #1 and homework #2 and then you’ll be out of late extensions. Please ask on Ed if you are confused by the late submission policy. Late submissions will be accepted up to 24 hours after the deadline. You are allowed to double up on your late submissions (i.e., if you have not used your two late submissions, then you can use them back to back).

No credit will be given for late submissions after you have used up your two allowed late submissions.

No credit will be given for any submission made 24 hours after the deadline.

Please note that, while Gradescope does enforce the 24-hour limit on late submissions and will clearly flag late submissions with a red “LATE” label, it does not enforce our specific limit of two late submissions. It is your responsibility to keep track of how many late submissions you have made so far, and to ensure you don’t make more than two late submissions.

If extraordinary circumstances (medical and family emergency etc.) prevent a student from meeting a deadline, we may grant additional extensions on a case-by-case basis. Whenever possible, the student must inform their instructor of these extraordinary circumstances before the deadline.

Please note that having a heavy workload in a given week does not qualify as an extraordinary circumstance. The purpose of the two extensions is precisely to give you some flexibility in weeks when you are busier than usual. Additionally mild COVID or common cold illness is also does not qualify as an extraordinary circumstance.

Regrades

We sometimes make mistakes, and are happy to review any incorrect grading decision.

However, please note that we will only consider regrade requests where a grader made an actual mistake (e.g., they took points off claiming you didn’t do something, when you actually did do it and the grader maybe missed that when reading over your submission). We will not consider regrade requests that ask for point penalties to be reduced, or try to argue that we should not be taking points off for a given issue in your code.

For example, suppose you receive a penalty that says “-2 points: Function X did not check that parameter Y is greater than zero”. If function X in your code did perform this check, and the grader missed this fact (and erroneously applied that penalty), you can submit a regrade request asking us to review this decision. We ask that you keep these requests brief and to the point: no more than 1-2 paragraphs identifying the exact penalty and the reasons you believe it was applied erroneously, including references to specific parts of your code (e.g., “I did check the value of the parameter in line 107”). Focus on laying out the facts, and nothing else.

On the other hand, let’s say you received the “Function X did not check that parameter Y is greater than zero” penalty, and function X in your code did not perform this check. In this case, you cannot submit a regrade request arguing that this is not something for which we should deduct points, or that the point deduction should be lower. Please note that all penalties are explicitly approved by an instructor (graders have no discretion to come up with penalties on their own and, if they took points off for something, it is because they were directed to do so by the instructors).

Please note that, while you may request a regrade for a specific issue, an instructor may do a full regrade of your submission if they feel there are other issues with the grading of your submission. This can result in you ending up with a lower score on the assignment.

Steps to Submit a Regrade Request

  1. Read over the above section to make sure your request will not be denied.

  2. All regrades must be submitted on Gradescope. Do not write on Ed asking for a regrade request. However, you can on Ed notify the instructor(s) that you submitted a regrade request on Gradescope.

  3. Finally, it is also your responsibility to make these requests in a timely manner. Requests for regrades must be submitted no later than one week after a graded piece of work is returned to you. After that time, we will not consider any requests for regrades, regardless of whether the regrade request is reasonable and justified.

Please allow time for the course staff to review your regrade request. We cannot provide a specific timeframe when your request will be handled. We are on a tight schedule grading other assignments but your request will be reviewed before the end of the quarter.

Books

This course will not have a required textbook; although, for those students who may find it helpful to know the topics we will discuss each week, readings will come from the text:

  • The Art of Multiprocessor Programming by Maurice Herlihy and Nir Shavit

and will be shown on the course schedule. Along with these readings and the lecture notes, students may find the following references helpful in understanding the course material:

  • An Introduction to Parallel Programming by Peter S. Pacheco

  • The Go Programming Language by Alan A. A. Donovan and Brian W. Kernighan

Policies

Policy on academic honesty

We take academic honesty very seriously in this class. Please make sure to read our Academic Honesty page.

Diversity statement

The University of Chicago is committed to diversity and rigorous inquiry that arises from multiple perspectives. We concur with that commitment and also believe that we have the highest quality interactions and can creatively solve more problems when we recognize and celebrate our diversity. We thus expect to maintain a productive learning environment based upon open communication, mutual respect, and non-discrimination. We view the diversity that students bring to this class as a resource, strength and benefit. It is our intent to present materials and activities that are respectful of diversity: gender, sexuality, disability, socioeconomic status, ethnicity, race, religious background, and immigration status. Any suggestions as to how to further such a positive and open environment in the class will be appreciated and given serious consideration.

If you have a preferred name different from what appears on the class roster, or preferred gender pronouns you would like us to use, please let us know.

Disability statement

If there are circumstances that make our learning environment and activities difficult, please let us know. We will maintain the confidentiality of any such discussions.

MS-CAPP students who need to request formal accommodations due to a disability should follow the Harris Accommodations Process. PhD students should contact their program administrator for information about how to request formal accommodations.