COMP 322: Fundamentals of Parallel Programming (Spring
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2022)
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Instructor:
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Prof. Vivek Sarkar, DH 3131
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Instructors: | Mackale Joyner, DH 2063 Zoran Budimlić, DH 3003 | TAs: | Adrienne Li, Austin Hushower, Claire Xu, Diep Hoang, Hunena Badat, Maki Yu, Mantej Singh, Rose Zhang, Victor Song, Yidi Wang | ||
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| Admin Assistant: | Annepha Hurlock, annepha@rice.edu , DH | 30803122, 713-348-5186 | Graduate TAs: | Jonathan Sharman, Ryan Spring, Bing Xue, Lechen Yu | |
| Co-Instructor: | Dr. Mackale Joyner | Undergraduate TAs: | Marc Canby, Anna Chi, Peter Elmers, Joseph Hungate, Cary Jiang, Gloria Kim, Kevin Mullin, Victoria Nazari, Ashok Sankaran, Sujay Tadwalkar, Anant Tibrewal, Eugene Wang, Yufeng Zhou | ||
Piazza site: | https://piazza.com/class/ixdqx0x3bjl6en (Piazza is the preferred medium for all course communications, but you can also send email to comp322-staff at rice dot edu if needed) | Cross-listing: | ELEC 323 | ||
Lecture location: | Herzstein Hall 210 | Lecture times: | MWF 1:00pm - 1:50pm (followed by group office hours during 2pm - 3pm, usually in DH 3092) | ||
Lab locations: | DH 1064, DH 1070 | Lab times: | Wednesday, 07:00pm - 08:30pm
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Piazza site: | https://piazza.com/rice/spring2022/comp322 (Piazza is the preferred medium for all course communications) | Cross-listing: | ELEC 323 | ||
Lecture location: | Herzstein Amphitheater (online 1st 2 weeks) | Lecture times: | MWF 1:00pm - 1:50pm | ||
Lab locations: | Keck 100 (online 1st 2 weeks) | Lab times: | Mon 3:00pm - 3:50pm (Austin, Claire) Wed 4:30pm - 5:20pm (Hunena, Mantej, Yidi, Victor, Rose, Adrienne, Diep, Maki) |
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.To , MapReduce
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 Canvas 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.There are The links to the latest versions of the lecture handouts are included below:
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
- Spring 2016 (Rice University)
- Spring 2015 (Rice University)
- Spring 2014 (Rice University)
- Spring 2013 (Rice University)
- Fall 2012 (Harvey Mudd College CS 181E, half-semester class, co-instructor: Prof. Ran Libeskind-Hadas)
- Spring 2012 (Rice University)
- Spring 2011 (Rice University)
- Fall 2009 (Rice University)
Lecture Schedule
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Week
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Day
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Date (2017)
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Lecture
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Assigned Videos (see Canvas site for vide links)
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In-class Worksheets
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Work Assigned
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Work Due
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1
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Mon
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Jan 09
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Lecture 1: Task Creation and Termination (Async, Finish)
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Wed
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Jan 11
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Lecture 2: Computation Graphs, Ideal Parallelism
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2
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Mon
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Jan 16
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No lecture, School Holiday (Martin Luther King, Jr. Day)
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Wed
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Jan 18
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Lecture 4: Parallel Speedup and Amdahl's Law
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Fri
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Jan 20
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Lecture 5: Future Tasks, Functional Parallelism ("Back to the Future")
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3
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Mon
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Jan 23
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Lecture 6: Memoization
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Lecture 7: Finish Accumulators
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Fri
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Jan 27
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Lecture 8: Map Reduce
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4
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Mon
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Jan 30
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Lecture 9: Data Races, Functional & Structural Determinism
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Wed
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Feb 01
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Lecture Schedule
Week | Day | Date (2022) | 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 10 | Lecture 1: Introduction |
| worksheet1 | lec1-slides |
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| Wed | Jan 12 | Lecture 2: Functional Programming | GList.java | worksheet2 | lec02-slides |
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| Fri | Jan 14 | Lecture 3: Higher order functions | worksheet3 | lec3-slides |
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2 | Mon | Jan 17 | No class: MLK | |||||||||||||||||
| Wed | Jan 19 | Lecture 4: Lazy Computation | LazyList.java Lazy.java | worksheet4 | lec4-slides | WS4-solution | |||||||||||||
| Fri | Jan 21 | Lecture 5: Java Streams | worksheet5 | lec5-slides | Homework 1 | WS5-solution | |||||||||||||
| 3 | Mon | Jan 24 | Lecture 6: Map Reduce with Java Streams | Module 1: Section 2.4 | Topic 2.4 Lecture, Topic 2.4 Demonstration | worksheet6 | lec6-slides |
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| Wed | Jan 26 | Lecture 7: Futures | Module 1: Section 2.1 | Topic 2.1 Lecture , Topic 2.1 Demonstration | worksheet7 | lec7-slides |
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| Fri | Jan 28 | Lecture 8: Computation Graphs, Ideal Parallelism | Module 1: Sections 1.2, 1.3 | Topic 1.2 Lecture, Topic 1.2 Demonstration, Topic 1.3 Lecture, Topic 1.3 Demonstration | worksheet8 | lec8-slides | WS8-solution | ||||||||||||
4 | Mon
| Jan 31 | Lecture 9: Async, Finish, Data-Driven Tasks | Module 1: Section 1.1, 4.5
| Topic 1.1 Lecture, Topic 1.1 Demonstration, Topic 4.5 Lecture, Topic 4.5 Demonstration | worksheet9 | lec9-slides | WS9-solution | ||||||||||||
| Wed | Feb 02 | Lecture 10: Event-based programming model
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| Fri | Feb | 0304 | Lecture 11: | Loop-Level Parallelism, Parallel Matrix Multiplication, Iteration Grouping (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 GUI programming as an example of event-based, futures/callbacks in GUI programming | worksheet11 | lec11-slides | Homework 2 | Homework 1 | WS11-solution | ||||||||||
| 5 | Mon | Feb | 0607 | Lecture 12: | Barrier Synchronization Scheduling/executing computation graphs Abstract performance metrics | Module 1: Section 31.4 | Topic | 31.4 Lecture , Topic | 31.4 Demonstration | worksheet12 | lec12-slides | WS12-solution | ||||||||
| Wed | Feb | 0809 | Lecture 13: | Parallelism in Java Streams, Parallel Prefix SumsParallel Speedup, Critical Path, Amdahl's Law | Module 1: Section 1.5 | Topic 1.5 Lecture , Topic 1.5 Demonstration | worksheet13 | lec13-slides | Homework 3 (includes two intermediate checkpoints) | WS13-solution | |||||||||
| Homework 2 | - | Fri | Feb 1011 | No class: Spring Recess
| Quiz for Unit 2 | ||||||||||||||
| 6 | Mon | Feb | 1314 | Lecture 14: | Iterative Averaging Revisited, SPMD pattern Accumulation and reduction. Finish accumulators | Module 1: Sections 3Section 2.5, 3.6 | Topic | 32. | 5 Lecture , Topic3 | .5 Demonstration , Topic 3.6Lecture | ,Topic 2.3 | .6Demonstration | worksheet14 | lec14-slides | WS14-solution | |||||
| Wed | Feb 1516 | Lecture 15: Phasers, Point-to-point Synchronization | Module 1: Sections 4.5, 4.2, 4.3 | Topic 4.5 Lecture Topic 4.5 Demonstration, Topic 4.2 Lecture , Topic 4.2 Demonstration, Topic 4.3 Lecture, Topic 4.3 Demonstration | worksheet15 | lec15-slides | |||||||||||||
| Fri | Feb 17 | Lecture 16: Phasers Review | Module 1: Sections 4.2 | Topic 4.2 Lecture , Topic 4.2 Demonstration | worksheet16 | lec16-slides | Quiz for Unit 3 | ||||||||||||
7 | Mon | Feb 20 | Lecture 17: Midterm SummaryRecursive Task Parallelism | worksheet15 | lec15-slides |
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| Fri | Feb 18 | Lecture 16: 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 | worksheet16 | lec16-slides | Homework 3 | Homework 2 | WS16-solution | |||||||||||
7 | Mon | Feb 21 | Lecture 17: Midterm Review | lec17-slides | ||||||||||||||||
| Wed | Feb 22 | Midterm Review (interactive Q&A, no lecture) | Exam 1 held during lab time (7:00pm - 10:00pm), scope of exam limited to lectures 1-16 | ||||||||||||||||
| Fri | Feb 24 | Lecture 18: Abstract vs. Real Performance | worksheet18 | lec18-slides | Homework 3, Checkpoint-1 | ||||||||||||||
8 | Mon | Feb 27 | Lecture 19: Pipeline Parallelism, Signal Statement, Fuzzy Barriers | Module 1: Sections 4.4, 4.1 | Topic 4.4 Lecture , Topic 4.4 Demonstration, Topic 4.1 Lecture, Topic 4.1 Demonstration, | worksheet19 | lec19-slides |
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| Wed | Mar 01 | Lecture 20: Critical sections, Isolated construct, Parallel Spanning Tree algorithm (start of Module 2) | Module 2: Sections 5.1, 5.2, 5.3, 5.6 | Topic 5.1 Lecture, Topic 5.1 Demonstration, Topic 5.2 Lecture, Topic 5.2 Demonstration, Topic 5.3 Lecture, Topic 5.3 Demonstration | worksheet20 | lec20-slides |
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| Fri | Mar 03 | Lecture 21: Atomic variables, Read-Write Isolation | Module 2: Sections 5.4, 5.5 | Topic 5.4 Lecture, Topic 5.4 Demonstration, Topic 5.5 Lecture, Topic 5.5 Demonstration, Topic 5.6 Lecture, Topic 5.6 Demonstration | worksheet21 | lec21-slides | Quiz for Unit 4 | ||||||||||||
9 | Mon | Mar 06 | Lecture 23: Java Threads, Java synchronized statement | Topic 7.1 Lecture, Topic 7.2 Lecture | worksheet22 | lec22-slides23 | Lecture 18: Limitations of Functional parallelism. | worksheet18 | lec18-slides | WS18-solution | ||||||||||
| Fri | Feb 25 | Lecture 19: Fork/Join programming model. OS Threads. Scheduler Pattern | Topic 2.7 Lecture, Topic 2.7 Demonstration, Topic 2.8 Lecture, Topic 2.8 Demonstration, | worksheet19 | lec19-slides | WS19-solution | |||||||||||||
8 | Mon | Feb 28 | Lecture 20: Confinement & Monitor Pattern. Critical sections | Module 2: Sections 5.1, 5.2, 5.6 | Topic 5.1 Lecture, Topic 5.1 Demonstration, Topic 5.2 Lecture, Topic 5.2 Demonstration, Topic 5.6 Lecture, Topic 5.6 Demonstration | worksheet20 | lec20-slides | WS20-solution | ||||||||||||
| Wed | Mar 02 | Lecture 21: Atomic variables, Synchronized statements | Module 2: Sections 5.4, 7.2 | Topic 5.4 Lecture, Topic 5.4 Demonstration, Topic 7.2 Lecture | worksheet21 | lec21-slides | WS21-solution | ||||||||||||
| Fri | Mar 04 | Lecture 22: Parallel Spanning Tree, other graph algorithms | worksheet22 | lec22-slides | Homework 4 | Homework 3 | WS22-solution | ||||||||||||
9 | Mon | Mar 07 | Lecture 23: Java Threads and Locks | Module 2: Sections 7.1, 7.3 | Topic 7.1 Lecture, Topic 7.3 Lecture | worksheet23 | lec23-slides |
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| Wed | Mar 09 | Lecture 24: Java Locks - Soundness and progress guarantees | Module 2: 7.5 | Topic 7.5 Lecture | worksheet24 | lec24-slides |
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| Fri | Mar 11 | Lecture 25: Dining Philosophers Problem | Module 2: 7.6 | Topic 7.6 Lecture | worksheet25 | lec25-slides |
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Mon | Mar 14 | No class: Spring Break |
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| Wed | Mar | 0816 | Lecture 24: Java synchronized statement (contd), wait/notifyNo class: Spring Break | Topic 7.3 Lecture | worksheet23 | lec23-slides |
| Homework 3, Checkpoint-2 | ||||||||||||
| Fri | Mar 10 | Lecture 24: TBD | worksheet24 | lec24-slides | Quiz for Unit 5 | ||||||||||||||
| - | M-F | Mar 13 - Mar 17 | Spring Break |
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| Fri | Mar 18 | No class: Spring Break |
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10 | Mon | Mar 2021 | Lecture 25: Concurrent Objects, Linearizability of Concurrent Objects26: N-Body problem, applications and implementations | Topic 7.4 Lecture | worksheet25 | lec25worksheet26 | lec26-slides | WS26-solution | ||||||||||||
| Wed | Mar 2223 | Lecture 26: Linearizability (contd), Java locks | 27: Read-Write Locks, Linearizability of Concurrent Objects | Module 2: 7.3, 7.4 | Topic 7.3 Lecture (recap), Topic 7.4 Lecture (recap) | worksheet26 worksheet27 | lec26lec27-slides | Homework 3 (all) |
| Fri | Mar 24 | Lecture 27: Parallel Design Patterns, Safety and Liveness Properties | Topic 7.5 Lecture | worksheet27 | lec27-slides | WS27-solution | |||
| 11 | MonFri | Mar | 2725 | Lecture 28: Message-Passing programming model with Actors | Module 2: 6.1, 6.2 | Topic 6.1 Lecture, | Topic 6.1 Demonstration, Topic 6.2 Lecture, Topic 6.2 | Demonstration, Topic 6.3 Lecture, Topic 6.3Demonstration | worksheet28 | lec28-slides |
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11 | Mon | Mar 2928 | Topic 6.4 Lecture , Topic 6.4 Demonstration , Topic 6.5 Lecture 29: Actors (contd) | Active Object Pattern. Combining Actors with task parallelism | Module 2: 6.3, 6.4 | Topic 6.3 Lecture, Topic 6.5 3 Demonstration, Topic 6.6 4 Lecture, Topic 6.6 4 Demonstration | worksheet29 | lec29-slides |
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| FriWed | Mar | 3130 | Lecture 30: | Java Synchronizers, Dining Philosophers ProblemTask Affinity and locality. Memory hierarchy | Topic 7.6 Lecture | worksheet30 | lec30-slides |
| 12 | Mon | Apr 03 | Lecture 31: Eureka-style Speculative Task Parallelism WS30-solution | |||||||
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| Wed | Apr 05 | Lecture 32: Task Affinity with Places (start of Module 3) | worksheet32 | lec32-slides |
| Homework 4 Checkpoint-1 | |||||||||||||
| Fri | Apr 07 | Lecture 33: Message Passing Interface (MPI) | worksheet33 | lec33-slides |
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13 | Mon | Apr 10 | Lecture 34: Message Passing Interface (MPI, contd) | worksheet34 | lec34Fri | Apr 01 | Lecture 31: 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 | worksheet31 | lec31-slides | Homework 5 | Homework 4 | WS31-solution | ||||||
12 | Mon | Apr 04 | Lecture 32: Barrier Synchronization with Phasers | Module 1: Section 3.4 | Topic 3.4 Lecture, Topic 3.4 Demonstration | worksheet32 | lec32-slides |
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| Wed | Apr 12 | Lecture 35: GPU ComputingWS32-solution | ||||||||
| Wed | worksheet35 | lec35-slides | Homework 5 (Due April 21st, with automatic extension until May 1st after which slip days may be used) | Homework 4 (all)Apr 06 | Lecture 33: Stencil computation. Point-to-point Synchronization with Phasers | Module 1: Section 4.2, 4.3 | Topic 4.2 Lecture, Topic 4.2 Demonstration, Topic 4.3 Lecture, Topic 4.3 Demonstration | worksheet33 | lec33-slides |
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| Fri | Apr 1408 | Lecture 36: Partitioned Global Address Space (PGAS) programming models34: Fuzzy Barriers with Phasers | Module 1: Section 4.1 | Topic 4.1 Lecture, Topic 4.1 Demonstration | worksheet34 | lec34-slides |
| worksheet36 | lec36WS34-slidessolution | ||||||||||
13 | 14 | Mon | Apr 1711 | Lecture 37: Apache Spark framework35: Eureka-style Speculative Task Parallelism |
| worksheet37worksheet35 | lec37lec35-slides |
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| Wed | Apr | 1913 | Lecture | 38: Topic TBD36: Scan Pattern. Parallel Prefix Sum |
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| Fri | Apr | 2115 | Lecture | 39: Course Review (lectures 20-37), Last day of classes37: Parallel Prefix Sum applications | worksheet37 | lec38lec37-slides | Homework 5 (automatic extension until May 1st, after which slip days may be used) | - | ||||||||||||
| 14 | Mon | Apr 24 | Review session / Office Hours, 1pm - 3pm, location TBD | 18 | Lecture 38: Overview of other models and frameworks | lec38- | Wed | Apr 26 | Review session / Office Hours, 1pm - 3pm, location TBD | slides | ||||||||||
| - | FriWed | Apr 2820 | Review session / Office Hours, 1pm - 3pm, location TBDLecture 39: Course Review (Lectures 19-38) | lec39-slides | - | |||||||||||||||
April 26 - May 3 | Scheduled final exam (Exam 2 – scope of exam limited to lectures 18-37), location and time TBD by registrarFri | Apr 22 | Lecture 40: Course Review (Lectures 19-38) | lec40-slides | Homework 5 |
Lab Schedule
Lab # | Date (20172022) | Topic | Handouts | Code Examples | 0||||||
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1 | Jan 10 | Infrastructure | Setupsetup | lab0-handout | - | 1 | Jan 11 | Async-Finish Parallel Programming with abstract metrics | lab1-handout , lab1-slideslab_1.zip | |
| 2 | Jan | 18Futures and HJ-Viz17 | Functional Programming | lab2-handout | , lab2-slides||||||
3 | Jan 25 Cutoff Strategy and Real World Performance24 | Java Streams | lab3-handout, lab3-slides lab_3.zip | |||||||
| 4 | Feb 01 | Java's ForkJoin FrameworkJan 31 | Futures | lab4-handout | , lab4-slides lab_4.zip||||||
5 | Feb 0807Loop | Data- Driven Tasks | lab5-handout, lab5-slides lab_5.zip | |||||||
| 6 | Feb 1514 | PhasersAsync / Finish | lab6-handout | lab_6.zip | ||||||
| - | Feb | 2221 | No lab this week | — Exam 1- | -(Midterm) | |||||
| 7 | Mar 01 | Isolated Statement and Atomic VariablesFeb 28 | Recursive Task Cutoff Strategy | lab7-handout | ||||||
| 8 | Mar | 0807 | Java Threads | lab8-handout | ||||||
- | Mar 1514 | No lab this week — (Spring Break) | ||||||||
| 9 | Mar 2221 | Java LocksConcurrent Lists | lab9-handout | |||||||
| 10 | Mar | 2928 | Actors | and Selectorslab10-handout | ||||||
| 11 | Apr | 05Eureka-style Speculative Task 04 | Loop Parallelism | lab11-handout | ||||||
12- | Apr 12 | Message Passing Interface (MPI) | lab12-handout 11 | No lab this week | ||||||
- | Apr | 19Apache Spark | lab13-handout18 | No lab this week |
Grading, Honor Code Policy, Processes and Procedures
Grading will be based on your performance on five homeworks four homework assignments (weighted 40% in all), two exams (weighted 40% in all), weekly lab exercises (weighted 10% in all), online quizzes (weighted 5% in all), and class participation including in-class Q&A, worksheets, Piazza participation class worksheets (weighted 5% in all).
The purpose of the homeworks homework is to give you practice in solving problems that deepen your understanding of concepts introduced in class. Homeworks are 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 automatically tracked through the Autograder, more details are available later in this document and in the Autograder user guideusing 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 prior to the start of the lab the following week.
Worksheets should be completed in class for full credit. For partial credit, a worksheet can be turned in before the start of the class following the one in which the worksheet for distributed, 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 homeworks and 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).
- HomeworksHomework: All submitted homeworks are homework is expected to be the result of your individual effort. You are free to discuss course material and approaches to 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 closedopen-book, closedopen-notes, and closedopen-computer individual written test, which must be completed within a specified time limit. No class notes or external materials may be consulted when taking the exams.
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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.
Accommodations for Students with Special Needs
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