edX site | Autograder Guide |
COMP 322: Fundamentals of Parallel Programming (Spring
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2023)
Instructor: |
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Mackale Joyner, DH |
2063 |
TAs: |
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Admin Assistant:
Annepha Hurlock, annepha@rice.edu, DH 3080, 713-348-5186
Graduate TAs:
Prasanth Chatarasi, Arghya Chatterjee, Yuhan Peng, Jonathan Sharman
Mohamed Abead, Chase Hartsell, Taha Hasan, Harrison Huang, Jerry Jiang, Jasmine Lee, Michelle Lee, Hung Nguyen, Quang Nguyen, Ryan Ramos, Oscar Reynozo, Delaney Schultz, Tina Wen, Raiyan Zannat, Kailin Zhang | |
Piazza site: |
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comp322 (Piazza is the preferred medium for all course communications |
) | Cross |
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-listing: | ELEC 323 |
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Lecture location: | Herzstein |
Amphitheater | Lecture times: | MWF 1:00pm - 1:50pm |
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Lab |
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locations: |
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Mon (Herzstein Amp), |
Tue (Keck 100) | Lab times: |
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Mon 3:00pm - |
3:50pm (Raiyan, Oscar, Mohamed, Ryan, Michelle, Taha) Tue 4:00pm - 4:50pm (Tina, Delaney, Chase, Hung, Jerry, Kailin, Jasmine) |
Course Course Syllabus
A summary PDF file containing the course syllabus for the course can be found here. Much of the syllabus information is also included below in this course web site, along with some additional details that are not included in the syllabus.
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The desired learning outcomes fall into three major areas (course modules):
1) Parallelism: functional programming, Java streams, creation and coordination of parallelism (async, finish), abstract performance metrics (work, critical paths), Amdahl's Law, weak vs. strong scaling, data races and determinism, data race avoidance (immutability, futures, accumulators, dataflow), deadlock avoidance, abstract vs. real performance (granularity, scalability), collective & point-to-point synchronization (phasers, barriers), parallel algorithms, systolic algorithms.
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3) Locality & Distribution: memory hierarchies, locality, cache affinity, data movement, message-passing (MPI), communication overheads (bandwidth, latency), MapReduce, accelerators, GPGPUs, CUDA, OpenCL., MapReduce
To achieve To achieve these learning outcomes, each class period will include time for both instructor lectures and in-class exercises based on assigned reading and videos. The lab exercises will be used to help students gain hands-on programming experience with the concepts introduced in the lectures.
To ensure that students gain a strong knowledge of parallel programming foundations, the classes and homeworks homework will place equal emphasis on both theory and practice. The programming component of the course will mostly use the Habanero-Java Library (HJ-lib) pedagogic extension to the Java language developed in the Habanero Extreme Scale Software Research project at Rice University. The course will also introduce you to real-world parallel programming models including Java Concurrency, MapReduce, MPI, OpenCL and CUDA. An important goal is that, at the end of COMP 322, you should feel comfortable programming in any parallel language for which you are familiar with the underlying sequential language (Java or C). Any parallel programming primitives that you encounter in the future should be easily recognizable based on the fundamentals studied in COMP 322.
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The prerequisite course requirements are COMP 182 and COMP 215. COMP 322 should be accessible to anyone familiar with the foundations of sequential algorithms and data structures, and with basic Java programming. COMP 321 is also recommended as a co-requisite.
Textbooks and Other Resources
There are no required textbooks for the class. Instead, lecture handouts are provided for each module as follows. The links to the latest versions on Owlspace are included below:
- Module 1 handout (Parallelism)
- Module 2 handout (Concurrency)
- Module 3 handout (Distribution and Locality)
You You are expected to read the relevant sections in each lecture handout before coming to the lecture. We will also provide a number of references in the slides and handouts.The links to the latest versions of the lecture handouts are included below:
- Module 1 handout (Parallelism)
- Module 2 handout (Concurrency)
There are also a few optional textbooks that we will draw from quite heavilyduring the course. You are encouraged to get copies of any or all of these books. They will serve as useful references both during and after this course:
- Fork-Join Parallelism with a Data-Structures Focus (FJP) by Dan Grossman (Chapter 7 in Topics in Parallel and Distributed Computing)
- Java Concurrency in Practice by Brian Goetz with Tim Peierls, Joshua Bloch, Joseph Bowbeer, David Holmes and Doug Lea
- Principles of Parallel Programming by Calvin Lin and Lawrence Snyder
- The Art of Multiprocessor Programming by Maurice Herlihy and Nir Shavit
Past Offerings of COMP 322
Lecture Schedule
worksheet13
worksheet28
Lecture Schedule
Week | Day | Date (2023) | Lecture | Assigned Reading | Assigned Videos (see Canvas site for video links) | In-class Worksheets | Slides | Work Assigned | Work Due | Worksheet Solutions | |
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1 | Mon | Jan 09 | Lecture 1: Introduction | worksheet1 | lec1-slides | WS1-solution | |||||
Wed | Jan 11 | Lecture 2: Functional Programming | worksheet2 | lec02-slides | WS2-solution | ||||||
Fri | Jan 13 | Lecture 3: Higher order functions | worksheet3 | lec3-slides | WS3-solution | ||||||
2 | Mon | Jan 16 | No class: MLK | ||||||||
Wed | Jan 18 | Lecture 4: Lazy Computation | worksheet4 | lec4-slides | WS4-solution | ||||||
Fri | Jan 20 | Lecture 5: Java Streams | worksheet5 | lec5-slides | Homework 1 | WS5-solution | |||||
3 | Mon | Jan 23 | Lecture 6: Map Reduce with Java Streams | Module 1: Section 2.4 | Topic 2.4 Lecture, Topic 2.4 Demonstration | worksheet6 | lec6-slides | WS6-solution | |||
Wed | Jan 25 | Lecture 7: Futures | Module 1: Section 2.1 | Topic 2.1 Lecture , Topic 2.1 Demonstration | worksheet7 | lec7-slides | WS7-solution | ||||
Fri | Jan 27 | Lecture 8: Async, Finish, Computation Graphs | Module 1: Sections 1.1, 1.2 | Topic 1.1 Lecture, Topic 1.1 Demonstration, Topic 1.2 Lecture, Topic 1.2 Demonstration | worksheet8 | lec8-slides | WS8-solution | ||||
4 | Mon | Jan 30 | Lecture 9: Ideal Parallelism, Data-Driven Tasks | Module 1: Section 1.3, 4.5 | Topic 1.3 Lecture, Topic 1.3 Demonstration, Topic 4.5 Lecture, Topic 4.5 Demonstration | worksheet9 | lec9-slides | WS9-solution | |||
Wed | Feb 01 | Lecture 10: Event-based programming model | worksheet10 | lec10-slides | Homework 1 | WS10-solution | |||||
Fri | Feb 03 | Lecture 11: GUI programming, Scheduling/executing computation graphs | Module 1: Section 1.4 | Topic 1.4 Lecture , Topic 1.4 Demonstration | worksheet11 | lec11-slides | Homework 2 | WS11-solution | |||
5 | Mon | Feb 06 | Lecture 12: Abstract performance metrics, Parallel Speedup, Amdahl's Law | Module 1: Section 1.5 | Topic 1.5 Lecture , Topic 1.5 Demonstration | worksheet12 | lec12-slides | WS12-solution | |||
Wed | Feb 08 | Lecture 13: Accumulation and reduction. Finish accumulators |
Week
Day
Date (2016)
Topic
Assigned Videos (Quizzes due by Friday of each week)
In-class Worksheets
Work Assigned
Work Due
1
Mon
Jan 11
Lecture 1: Task Creation and Termination (Async, Finish)
Wed
Jan 13
Lecture 2: Computation Graphs, Ideal Parallelism
(2 weeks)
2
Mon
Jan 18
No lecture, School Holiday (Martin Luther King, Jr. Day)
Wed
Jan 20
Lecture 4: Parallel Speedup and Amdahl's Law
Fri
Jan 22
Lecture 5: Future Tasks, Functional Parallelism
3
Mon
Jan 25
Lecture 6: Memoization
Module 1: Section 2.3 | Topic 2.3 Lecture |
Topic 2.3 Demonstration |
worksheet13 |
lec13-slides |
WS13-solution | |
Fri |
Jan 29
Feb 10 | No class: Spring Recess | ||||||||
6 | Mon | Feb 13 | Lecture 14: |
Data Races, Functional & Structural Determinism | Module 1: Sections 2.5, 2.6 | Topic 2.5 Lecture , Topic 2.5 Demonstration, Topic 2.6 Lecture |
, Topic 2.6 Demonstration |
worksheet14 |
lec14-slides |
(2 weeks)
4
Mon
Feb 01
Lecture 9: Map Reduce
Wed
Feb 03
Fri
Feb 05
WS14-solution | |||||||||||
Wed | Feb 15 | Lecture 15: Limitations of Functional parallelism. | worksheet15 | lec15-slides | Homework 2 | WS15-solution | |||||
Fri | Feb 17 | Lecture 16: Recursive Task Parallelism | worksheet16 | lec16-slides | Homework 3 | WS16-solution | |||||
7 | Mon | Feb 20 | Lecture 17: Midterm Review | lec17-slides | |||||||
Wed | Feb 22 | Lecture 18: Midterm Review | lec18-slides | ||||||||
Fri | Feb 24 | Lecture 19: Data-Parallel Programming model. Loop-Level Parallelism, Loop Chunking |
Module 1: Sections 3.1, 3.2, 3.3 | Topic 3.1 Lecture, Topic 3.1 Demonstration , Topic 3.2 Lecture, |
Topic 3.2 Demonstration, Topic 3.3 Lecture, |
Topic 3.3 Demonstration |
worksheet19 |
lec19-slides |
WS19-solution | |||
8 |
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Mon | Feb |
27 | Lecture |
20: |
Barrier Synchronization with Phasers | Module 1: |
Sections 3. |
4 | Topic 3.4 Lecture, Topic 3.4 Demonstration |
worksheet20 |
lec20-slides |
Wed
WS20-solution | |||
Wed | Mar 01 |
Lecture |
21:Stencil computation. Point-to-point Synchronization with Phasers | Module 1: Sections |
4. |
2, 4.3 |
Topic |
4. |
2 Lecture, Topic |
4. |
2 Demonstration, Topic 4.3 |
Lecture, |
Topic 4.3 |
Demonstration |
worksheet21 |
lec21-slides |
Fri
Feb 12
WS21-solution | |||
Fri | Mar 03 | Lecture 22: Fuzzy Barriers with Phasers |
Module 1: |
Section 4. |
1 |
Topic 4. |
1 Lecture, |
Topic 4. |
1 Demonstration |
worksheet22 |
lec22-slides |
(5 weeks, with two intermediate checkpoints)
Homework 2, Lecture & demo quizzes for topics 3.4 , 3.5, 3.6, 4.5
6
Mon
Feb 15
Lecture 15: Phasers, Point-to-point Synchronization
Wed
Feb 17
Lecture 16: Pipeline Parallelism, Signal Statement, Fuzzy Barriers
Fri
Feb 19
Lecture 17: Abstract vs. Real Performance
7
Mon
Feb 22
Lecture 18: Midterm Summary
Wed
Feb 24
Midterm Review (Interactice Q&A using PollEverywhere)
Fri
Feb 26
Lecture 19: Task Scheduling Policies
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M-F
Feb 29- Mar 04
Spring Break
8
Mon
Mar 07
Lecture 20: Critical sections, Isolated construct, Parallel Spanning Tree algorithm (start of Module 2)
Wed
Mar 09
Lecture 21: Atomic variables, Read-Write Isolation
Fri
Mar 11
Lecture 22: Parallelism in Java Streams, Parallel Prefix Sums
Homework 3 Checkpoint-2, Lecture & demo quizzes for topics 5.1 to 5.6
9
Mon
Mar 14
Lecture 23: Java Threads, Java synchronized statement
Wed
Mar 16
Lecture 24: Java synchronized statement (contd), wait/notify
Fri
Mar 18
Lecture 25: Concurrent Objects, Linearizability of Concurrent Objects
Homework 3, Lecture quizzes for topics 7.1 - 7.4
10
Mon
Mar 21
Lecture 26: Linearizability (contd), Java locks
(3 weeks, with one intermediate checkpoint)
Wed
Mar 23
Lecture 27: Parallel Design Patterns, Safety and Liveness Properties
Fri
Mar 25
Lecture 28: Actors
Lecture & demo quizzes for topics 7.5
11
Mon
Mar 28
Lecture 29: Actors (contd)
Lec29Slide2ThreadRing.java
Lec29Slide4EchoActor.java
Lec29Slide6Pipeline.java
Lec29Slide15ReqReplyActor.java
Lec29Slide15SyncReplyActor.java
Wed
Mar 30
Lecture 30: Java Synchronizers, Dining Philosophers Problem
-
Fri
Apr 01
Midterm Recess
12
Mon
Apr 04
Lecture 31: Eureka-style Speculative Task Parallelism
Homework 4 Checkpoint-1
Wed
Apr 06
Lecture 32: Task Affinity with Places (start of Module 3)
Fri
Apr 08
Lecture 33: Message Passing Interface (MPI)
13
Mon
Apr 11
Lecture 34: Message Passing Interface (MPI, contd)
Homework 4
Lecture quiz for topics 6.1 - 6.6.
Wed
Apr 13
Lecture 35: GPU Computing
(2 weeks, with automatic extension till May 2nd)
Fri
Apr 15
Lecture 36: Memory Consistency Models
lec36-slides
14
Mon
Apr 18
Lecture 37: Partitioned Global Address Space (PGAS) programming models
Wed
Apr 20
Lecture 38: Survey of current Parallel Programming Models
Fri
Apr 22
Lecture 39: Course Review (lectures 20-37), Last day of classes
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Tue
May 3
Scheduled final exam in Herzstein Hall Auditorium, 9am - 12noon, May 3rd (Exam 2 – scope of exam limited to lectures 20-37)
Lab Schedule
Lab # | Date (2015) | Topic | Handouts | Code Examples |
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0 | Infrastructure Setup | lab0-handout | - | |
1 | Jan 13 | Async-Finish Parallel Programming | lab1-handout, lab1-slides | lab_1.zip |
2 | Jan 20 | Abstract performance metrics with async & finish | lab2-handout, lab2-slides | lab_2.zip |
3 | Jan 27 | DIY HJ-lib Programming, Futures, HJ-Viz | lab3-handout, lab3-slides | lab_3.zip |
4 | Feb 03 | Finish Accumulators and Loop-Level Parallelism | lab4-handout and lab4-slides | lab_4.zip |
5 | Feb 10 | Loop Chunking and Barrier Synchronization | lab5-handout and lab5-slides | lab_5.zip |
6 | Feb 17 | Data-Driven Futures and Phasers | lab6-handout | lab_6.zip |
- | Feb 24 | No lab this week — Exam 1 | - | - |
- | Mar 02 | No lab this week — Spring Break | - | - |
7 | Mar 09 | Isolated Statement and Atomic Variables | lab7-handout | |
8 | Mar 16 | Java Threads | lab8-handout | |
9 | Mar 23 | Java Locks | lab9-handout | |
10 | Mar 30 | Actors and Selectors | lab10-handout | |
11 | Apr 06 | Eureka-style Speculative Task Parallelism | lab11-handout | |
12 | Apr 13 | Message Passing Interface (MPI) | lab12-handout | |
13 | Apr 20 | Parallel Pretty Pictures | lab13-handout |
Grading, Honor Code Policy, Processes and Procedures
Grading will be based on your performance on five homeworks (weighted 40% in all), two exams (weighted 40% in all), weekly lab exercises (weighted 10% in all), and class participation including worksheets, in-class Q&A, Piazza participation, and online quizzes (weighted 10% in all).
The purpose of the homeworks is to train you to solve problems and to help deepen your understanding of concepts introduced in class. Homeworks are due on the dates and times specified in the course schedule. Please turn in all your homeworks using the subversion system set up for the class. Homework is worth full credit when turned in on time. No late submissions (other than those using slip days mentioned below) will be accepted.
As in COMP 321, all students will be given 3 slip days to use throughout the semester. When you use a slip day, you will receive up to 24 additional hours to complete the assignment. You may use these slip days in any way you see fit (3 days on one assignment, 1 day each on 3 assignments, etc.). If you use slip days, you must submit a SLIPDAY.txt file in your SVN homework folder before the actual submission deadline indicating the number of slip days that you plan to use. Other than slip days, no extensions will be given unless there are exceptional circumstances (such as severe sickness, not because you have too much other work). Such extensions must be requested and approved by the instructor (via e-mail, phone, or in person) before the due date for the assignment. Last minute requests are likely to be denied. If you do receive an extension from the instructor, please indicate this by placing an EXTENSION.txt file in your SVN homework folder before the actual submission deadline indicating the date that the extension was granted by the instructor as well as the length of the extension.
Labs must be checked off by a TA prior to the start of the lab the following week.
Worksheets are due by the beginning of the class after they are distributed, so that solutions to the worksheets can be discussed.
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WS22-solution | |||||||||||
9 | Mon | Mar 06 | Lecture 23: Fork/Join programming model. OS Threads. Scheduler Pattern | Topic 2.7 Lecture, Topic 2.7 Demonstration, Topic 2.8 Lecture, Topic 2.8 Demonstration | worksheet23 | lec23-slides | Homework 3 (CP 1) | WS23-solution | |||
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Wed | Mar 08 | Lecture 24: Confinement & Monitor Pattern. Critical sections | Module 2: Sections 5.1, 5.2 | Topic 5.1 Lecture, Topic 5.1 Demonstration, Topic 5.2 Lecture, Topic 5.2 Demonstration, Topic 5.6 Lecture, Topic 5.6 Demonstration | worksheet24 | lec24-slides | WS24-solution | ||||
Fri | Mar 10 | Lecture 25: Atomic variables, Synchronized statements | Module 2: Sections 5.4, 7.2 | Topic 5.4 Lecture, Topic 5.4 Demonstration, Topic 7.2 Lecture | worksheet25 | lec25-slides | WS25-solution | ||||
Mon | Mar 13 | No class: Spring Break | |||||||||
Wed | Mar 15 | No class: Spring Break | |||||||||
Fri | Mar 17 | No class: Spring Break | |||||||||
10 | Mon | Mar 20 | Lecture 26: Parallel Spanning Tree, other graph algorithms | worksheet26 | lec26-slides | WS26-solution | |||||
Wed | Mar 22 | Lecture 27: Java Threads and Locks | Module 2: Sections 7.1, 7.3 | Topic 7.1 Lecture, Topic 7.3 Lecture | worksheet27 | lec27-slides | Homework 3 (CP 2) | WS27-solution | |||
Fri | Mar 24 | Lecture 28: Java Locks - Soundness and progress guarantees | Module 2: Section 7.5 | Topic 7.5 Lecture | worksheet28 | lec28-slides | WS28-solution | ||||
11 | Mon | Mar 27 | Lecture 29: Dining Philosophers Problem | Module 2: Section 7.6 | Topic 7.6 Lecture | worksheet29 | lec29-slides | WS29-solution | |||
Wed | Mar 29 | Lecture 30: Read-Write Locks, Linearizability of Concurrent Objects | Module 2: Sections 7.3, 7.4 | Topic 7.3 Lecture, Topic 7.4 Lecture | worksheet30 | lec30-slides | WS30-solution | ||||
Fri | Mar 31 | Lecture 31: Message-Passing programming model with Actors | Module 2: Sections 6.1, 6.2 | Topic 6.1 Lecture, Topic 6.1 Demonstration, Topic 6.2 Lecture, Topic 6.2 Demonstration | worksheet31 | lec31-slides | WS31-solution | ||||
12 | Mon | Apr 03 | No class | Homework 4 | Homework 3 (All) | ||||||
Wed | Apr 05 | Lecture 32: Active Object Pattern. Combining Actors with task parallelism | Module 2: Sections 6.3, 6.4 | Topic 6.3 Lecture, Topic 6.3 Demonstration, Topic 6.4 Lecture, Topic 6.4 Demonstration | worksheet32 | lec32-slides | WS32-solution | ||||
Fri | Apr 07 | Lecture 33: Task Affinity and locality. Memory hierarchy | worksheet33 | lec33-slides | WS33-solution | ||||||
13 | Mon | Apr 10 | Lecture 34: Eureka-style Speculative Task Parallelism | worksheet34 | lec34-slides | WS34-solution | |||||
Wed | Apr 12 | Lecture 35: Scan Pattern. Parallel Prefix Sum | worksheet35 | lec35-slides | Homework 4 (CP 1) | WS35-solution | |||||
Fri | Apr 14 | Lecture 36: Parallel Prefix Sum applications | worksheet36 | lec36-slides | WS36-solution | ||||||
14 | Mon | Apr 17 | Lecture 37: Overview of other models and frameworks | lec37-slides | |||||||
Wed | Apr 19 | Lecture 38: Course Review (Lectures 19-34) | lec38-slides | Homework 4 (All) | |||||||
Fri | Apr 21 | Lecture 39: Course Review (Lectures 19-34) | lec39-slides |
Lab Schedule
Lab # | Date (2023) | Topic | Handouts | Examples |
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1 | Jan 09 | Infrastructure setup | lab0-handout lab1-handout | |
- | Jan 16 | No lab this week (MLK) | ||
2 | Jan 23 | Functional Programming | lab2-handout | |
3 | Jan 30 | Futures | lab3-handout | |
4 | Feb 06 | Data-Driven Tasks | lab4-handout | |
5 | Feb 13 | Async / Finish | lab5-handout | |
- | Feb 20 | No lab this week (Midterm Exam) | ||
6 | Feb 27 | Loop Parallelism | lab6-handout | image kernels |
7 | Mar 06 | Recursive Task Cutoff Strategy | lab7-handout | |
- | Mar 13 | No lab this week (Spring Break) | ||
- | Mar 20 | No lab this week | ||
8 | Mar 27 | Java Threads | lab8-handout | |
9 | Apr 03 | Concurrent Lists | lab9-handout | |
10 | Apr 10 | Actors | lab10-handout | |
- | Apr 17 | No lab this week |
Grading, Honor Code Policy, Processes and Procedures
Grading will be based on your performance on four homework assignments (weighted 40% in all), two exams (weighted 40% in all), lab exercises (weighted 10% in all), online quizzes (weighted 5% in all), and in-class worksheets (weighted 5% in all).
The purpose of the homework is to give you practice in solving problems that deepen your understanding of concepts introduced in class. Homework is due on the dates and times specified in the course schedule. No late submissions (other than those using slip days mentioned below) will be accepted.
The slip day policy for COMP 322 is similar to that of COMP 321. All students will be given 3 slip days to use throughout the semester. When you use a slip day, you will receive up to 24 additional hours to complete the assignment. You may use these slip days in any way you see fit (3 days on one assignment, 1 day each on 3 assignments, etc.). Slip days will be tracked using the README.md file. Other than slip days, no extensions will be given unless there are exceptional circumstances (such as severe sickness, not because you have too much other work). Such extensions must be requested and approved by the instructor (via e-mail, phone, or in person) before the due date for the assignment. Last minute requests are likely to be denied.
Labs must be submitted by the following Wednesday at 4:30pm. Labs must be checked off by a TA.
Worksheets should be completed by the deadline listed in Canvas so that solutions to the worksheets can be discussed in the next class.
You will be expected to follow the Honor Code in all homework and exams. The following policies will apply to different work products in the course:
- In-class worksheets: You are free to discuss all aspects of in-class worksheets with your other classmates, the teaching assistants and the professor during the class. You can work in a group and write down the solution that you obtained as a group. If you work on the worksheet outside of class (e.g., due to an absence), then it must be entirely your individual effort, without discussion with any other students. If you use any material from external sources, you must provide proper attribution.
- Weekly lab assignments: You are free to discuss all aspects of lab assignments with your other classmates, the teaching assistants and the professor during the lab. However, all code and reports that you submit are expected to be the result of your individual effort. If you work on the lab outside of class (e.g., due to an absence), then it must be entirely your individual effort, without discussion with any other students. If you use any material from external sources, you must provide proper attribution (as shown here).
- Homework: All submitted homework is expected to be the result of your individual effort. You are free to discuss course material and approaches to
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- problems with your other classmates, the teaching assistants and the professor, but you should never misrepresent someone else’s work as your own. If you use any material from external sources, you must provide proper attribution.
- Quizzes: Each online quiz will be an open-notes individual test. The student may consult their course materials and notes when taking the quizzes, but may not consult any other external sources.
- Exams: Each exam will be a open-book, open-notes, and open-computer individual test, which must be completed within a specified time limit. No external materials may be consulted when taking the exams.
For grade disputes, please send an email to the course instructors within 7 days of receiving your grade. The email subject should include COMP 322 and the assignment. Please provide enough information in the email so that the instructor does not need to perform a checkout of your code, but you should never misrepresent someone else’s work as your own. If you use any material from external sources, you must provide proper attribution (as shown here). Exams 1 and 2 test your individual understanding and knowledge of the material. Exams are closed-book, and collaboration on exams is strictly forbidden. Finally, it is also your responsibility to protect your homeworks and exams from unauthorized access. Graded homeworks will be returned to you via email, and exams as marked-up hardcopies. If you believe we have made an error in grading your homework or exam, please bring the matter to our attention within one week.
Accommodations for Students with Special Needs
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