Syllabus¶
Warning
This is a tentative syllabus and subject to change.
Course Description¶
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
Tina Oberoi (contact on Ed)
Nishchay Karle (contact on Ed)
Graders
None
Course Structure¶
The course material will be split into seven modules that will span 1-2 weeks through out the quarter. The delivery of each module will follow a flipped classroom model, where the lectures/module content will be delivered in two ways:
Synchronous content (i.e., in-person) : 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:
An introduction to the material/topics for the current week’s module.
Allow you to ask questions about the content provided in the videos from the prior week.
Have live coding sessions/activities with everyone regarding the material covered in the current week’s module.
Asynchronous content (i.e., pre-recorded videos/readings): Each week after our normal class time, you will be required to watch pre-recorded videos and do readings from the current week. The videos will include lecture content 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 after our scheduled time. Feel free to watch them at your convenience. However, you are required to watch the pre-recorded videos before we meet the next week. I will try post readings for the next module in advance in case you would like to read about the topics for the next module before the next class.
Please see the calendar for more details on what happens on a week to week basis.
Coursework¶
The course will include weekly homework, one quarter exam, attendance and projects:
Homework - The weekly homework assignments will contain practice problems to help enforce the concepts learned during a lecture.- Milestone - There will be milestones that will you need complete by certain due dates. These milestones check to ensure that you are making progress on the various components of the compiler. The grading of these milestones will be somewhat subjective in nature but each milestone description will provide a grading outline to help understand what needs to be completed by the milestone due date.
- Attendance - In order to receive you full attendance weight, you must do the following
Attend every lecture (either in-person or remotely). 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 on Thursdays @ 11:59pm. The new assignments will be out by Thursday evenings. I will post when they are available. 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 exam in this course. The exam will be a mixture of coding exercises and short-answer questions. The exam will be given during the normal class time and you will have the full period.
Exam |
Time Limit |
Day |
|---|---|---|
Quarter Exam |
120 minutes |
May 8th @ 5:30pm-7:30pm |
The exact details about how this exam will be structured 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:
Text editor (suggestion, not required): Visual Studio Code
IDE (suggestion, not required): GoLand
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. There might be additional software to install as the quarter progresses but the course staff will let you know how to install it.
Grading¶
Your final grade will be based on the following:
Homework (5% each) |
10% |
Projects (20% each) |
60% |
Quarter Exam |
25% |
Attendance |
5% |
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 cannot use a late extension on the last project (i.e., Project 3) in the course!
You are allowed to make at most two late submissions on the programming assignments. 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
Read over the above section to make sure your request will not be denied.
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.
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.
Policy on Generative AI¶
Using generative AI tools should be viewed as a means to enhance learning and explore new avenues, rather than as a substitute for rigorous study and practice. While generative AI tools can assist in generating content, you are expected to have a clear understanding of the concepts being explored, the generated output, and its accuracy. Relying solely on AI-generated content without understanding the underlying principles is discouraged. Assessments and exams are designed to evaluate your understanding of concepts and problem-solving skills. Relying solely on AI tools for assessment preparation may hinder your mastery of essential skills.
Specifically, you are allowed to use Generative AI in this course as long as correct citation is provide:
The use of AI tools, such as ChatGPT or Dall-E 2, for this course is allowed for specific assignments only when determined to be in support of the course learning goals. Assignments in which AI tools are permitted will be clearly identified by the instructor and noted in the assignment directions. You are not required to use AI tools, but if you choose to use them for any part of the assignment (from brainstorming to text editing), you must use proper citation (please use APA citation format ). Failure to properly cite AI tools is considered a violation of the University of Chicago’s Academic Honesty and Plagiarism policy. If you are unclear if something is an AI Tool, please check with your instructor. (CCTL, UChicago)
Improperly citing or no citation when a Generative AI resource was used in assignment will be treated as an academic misconduct case.
The Generative AI policy comes from the hardwork of the Citation: Generative AI: Policy Guidance for MPCS Faculty group.
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.