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COMP 322: Fundamentals of Parallel Programming (Spring

...

2023)

 

Dr. Shams Imam Prudhvi Boyapalli, Peter Elmers, Nicholas Hanson-Holtry, Ayush Narayan, Timothy Newton, Alitha Partono, Tom Roush, Hunter Tidwell, Bing XueCross-

Instructor:

Prof. Vivek SarkarMackale Joyner, DH 30802063

Head TATAs:Max Grossman

Admin Assistant:

Annepha Hurlock, annepha@rice.edu, DH 3080, 713-348-5186

Graduate TAs:

Prasanth Chatarasi, Arghya Chatterjee, Yuhan Peng, Jonathan Sharman

Co-Instructor:Undergraduate TAs: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:

https://piazza.com/rice/classspring2022/iirz0u74egl2q9comp322 (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 210TBD

Lecture times:

MWF 1:00pm - 1:50pm (followed by office hours in Duncan Hall 3092 during 2pm - 3pm)

Lab locationlocations:

DH 1064 (Section A01), DH 1070 (Section A02)TBD

Lab times:

Wednesday, 07Mon  3:00pm - 083:30pm

Course Syllabus

50pm ()

Tue 4:00pm - 4:50pm ()

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.

...

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.

...

3) Locality & Distribution: memory hierarchies, locality, cache affinity, data movement, message-passing (MPI), communication overheads (bandwidth, latency), MapReduce, accelerators, GPGPUs, CUDA, OpenCL.

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.

...

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  You are expected to the latest versions on Owlspace are included below:

You are expected to read the relevant sections 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:

Lecture Schedule

worksheet13

worksheet28

...

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)

...

Lecture Schedule

 

 

2  Computation Graphs, Ideal Parallelism 15Wed 4Lecture 5: Future Tasks, Functional Parallelism   Parallel Speedup and Amdahl's Law1   1 6 Memoization2    2

BinomialCoefficient.java

Worksheet5.javaWed 7: Finish Accumulators   2  lec9-slides  10: Java’s Fork/Join Library

Lecture 22:  Parallelism in Java Streams, Parallel Prefix Sums

16 worksheet25 lec25-slides   25   Demonstration, Topic 6.3 Lecture, Topic 6.3 Lecture & demo quizzes for topics 7.5Fri Fri 15 Memory Consistency Models Mon 18 GPU ComputingWed 20 Fortress languageFri 22lectures 2037, Last day of classesScheduled final exam in Herzstein Hall Auditorium, 9am - 12noon, May 3rd (Exam 2 – scope of exam limited to lectures 20-37

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 

1

Mon

Jan 09

Lecture 1: Introduction

 

 

worksheet1lec1-slides  

 

 

WS1-solution 

 

Wed

Jan 11

Lecture 2:  Functional Programming

GList.java worksheet2lec02-slides

 

 

WS2-solution Wed
 FriJan 13Lecture 3: Module 1: Sections 1.2, 1.3Topic 1.2 Lecture, Topic 1.2 Demonstration, Topic 1.3 Lecture, Topic 1.3 Demonstrationworksheet2lec2-slidesHigher order functions  worksheet3 lec3-slides   

 

 WS3-solution Fri

2

Mon

Jan

16

Lecture 3: Abstract Performance Metrics, Multiprocessor SchedulingModule 1: Section 1.4Topic 1.4 Lecture, Topic 1.4 Demonstrationworksheet3lec3-slides

Homework 1

(2 weeks)

Lecture & demo quizzes for topics 1.1, 1.2, 1.3, 1.4

2

Mon

Jan 18

No lecture, School Holiday (Martin Luther King, Jr. Day)

     

No class: MLK

        

 

Wed

Jan 18

Lecture 4: Lazy Computation

LazyList.java

Lazy.java

 worksheet4lec4-slides  WS4-solution 

 

Fri

Jan 20

Lecture

Module 1: Section 1.5Topic 1.5 Lecture, Topic 1.5 Demonstrationworksheet4lec4-slides  

 

Fri

Jan 22

5:

Java Streams

  worksheet5lec5-slidesHomework 1 WS5-solution 
3MonJan 23

Lecture 6: Map Reduce with Java Streams

Module 1: Section 2.14Topic 2.4 Lecture, Topic 2.4 Demonstration  worksheet5worksheet6lec5lec6-slides

 

Lecture & demo quizzes for topics 1.5, 2.1 (topic 1.6 is optional)

3

Mon WS6-solution 

 

Wed

Jan 25

Lecture

7:

Futures

Module 1: Section 2.21Topic 2.1 Lecture, Topic 2.1 Demonstrationworksheet6worksheet7lec6lec7-slides

 

 WS7-solution 

 

Fri

Jan 27

Lecture

8:  Computation Graphs, Ideal Parallelism

Module 1: Section Sections 1.2, 1.3Topic 1.2 Lecture, Topic 1.2 Demonstration, Topic 1.3 Lecture, Topic 1.3 Demonstrationworksheet7worksheet8lec7lec8-slides  WS8-solution Fri

4

Mon

 

Jan 2930 Lecture 89: Data Races, Functional & Structural DeterminismAsync, Finish, Data-Driven Tasks 

Module 1:

Sections 2

Section 1.

5

1,

2

4.

6

5

 

Topic

2

1.

5

1 Lecture

 

,

 

Topic

2

1.

5

1 Demonstration, Topic

2

4.

6

5 Lecture

 

,

 

Topic

2

4.

6 Demonstration   
worksheet8lec8-slides

Homework 2

Homework 2 JARs (optional)

(2 weeks)

Homework 1, Lecture & demo quizzes for topics 2.2, 2.3, 2.5, 2.6

4

Mon

Feb 01

Lecture 9: Map Reduce

Module 1: Section 2.4Topic 2.4 Lecture  ,  Topic 2.4 Demonstration   worksheet9

5 Demonstration

worksheet9

lec9-slides   WS9-solution 
 WedFeb 01Lecture 10: Event-based programming model

 

  worksheet10lec10-slides Homework 1WS10-solution 
 Fri

Wed

Feb 03Lecture FJP chapter: Sections 7.3 & 7.5 worksheet10lec10-slides

ArraySum.java

ArraySumFourWay.java

 

 

Fri

Feb 05

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

worksheet11lec11-slides Lecture & demo quizzes for topics 2.4, 3.1, 3.2, 3.3

5

Mon

Feb 08

Lecture 12:  Barrier Synchronization

Module 1: Section 3.4Topic 3.4 Lecture , Topic 3.4 Demonstration worksheet12 lec12-slides   

 

Wed

Feb 10

Lecture 13: Iterative Averaging Revisited, SPMD pattern

Module 1: Sections 3.5, 3.6Topic 3.5 Lecture , Topic 3.5 Demonstration , Topic 3.6 Lecture,   Topic 3.6 Demonstration    worksheet13 lec13-slides Worksheet12.java

 

Fri

Feb 12

Lecture 14:  Data-Driven Tasks and Data-Driven Futures

Module 1: Section 4.5Topic 4.5 Lecture ,   Topic 4.5 Demonstration worksheet14 lec14-slides

Homework 3

(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

Module 1: Sections 4.2, 4.3Topic 4.2 Lecture ,   Topic 4.2 Demonstration, Topic 4.3 Lecture,  Topic 4.3 Demonstration worksheet15 lec15-slides   

 

Wed

Feb 17

Lecture 16: Pipeline Parallelism, Signal Statement, Fuzzy Barriers

Module 1: Sections 4.4, 4.1Topic 4.4 Lecture ,   Topic 4.4 Demonstration, Topic 4.1 Lecture,  Topic 4.1 Demonstration, worksheet16 lec16-slides   

 

Fri

Feb 19

Lecture 17: Abstract vs. Real Performance

   worksheet17 lec17-slides  Lecture & demo quizzes for topics 4.1, 4.2, 4.3, 4.4

7

Mon

Feb 22

Lecture 18: Midterm Summary

    lec18-slides   

 

Wed

Feb 24

Midterm Review (Interactice Q&A using PollEverywhere)

    Exam 1 held during lab time (7:00pm - 10:00pm), scope of exam limited to lectures 1-18 

 

Fri

Feb 26

Lecture 19: Task Scheduling Policies

 Topic 4.6 Lecture ,   Topic 4.6 Demonstration worksheet19 lec19-slides Lec19HelpFirstWorkStealing.javaHomework 3 Checkpoint-1, Lecture & demo quizzes for topic 4.6

-

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)

Module 2: Sections 5.1, 5.2, 5.3, 5.6Topic 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  

 

 

Wed

Mar 09

Lecture 21: Atomic variables, Read-Write Isolation

Module 2: Sections 5.4, 5.5Topic 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  

 

 

Fri

Mar 11

   worksheet22 lec22-slides  

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

 Topic 7.1 Lecture, Topic 7.2 Lecture worksheet23 lec23-slides 11: GUI programming as an example of event-based,
futures/callbacks in GUI programming
  worksheet11lec11-slidesHomework 2 WS11-solution 
5

Mon

Feb 06

Lecture 12: Scheduling/executing computation graphs
Abstract performance metrics
Module 1: Section 1.4Topic 1.4 Lecture , Topic 1.4 Demonstrationworksheet12lec12-slides  WS12-solution 

 

Wed

Feb 08

Lecture 13: Parallel Speedup, Critical Path, Amdahl's Law

Module 1: Section 1.5

Topic 1.5 Lecture , Topic 1.5 Demonstration

worksheet13lec13-slides  WS13-solution 

 

Fri

Feb 10

No class: Spring Recess

 

        
6

Mon

Feb 13

Lecture 14: Accumulation and reduction. Finish accumulators

Module 1: Section 2.3Topic 2.3 Lecture   Topic 2.3 Demonstrationworksheet14lec14-slides  WS14-solution 

 

Wed

Feb 15

Lecture 15: Recursive Task Parallelism  

  worksheet15lec15-slides

 

 

 WS15-solution 
 FriFeb 17

Lecture 16: Data Races, Functional & Structural Determinism

Module 1: Sections 2.5, 2.6Topic 2.5 Lecture ,  Topic 2.5 Demonstration,  Topic 2.6 Lecture,  Topic 2.6 Demonstrationworksheet16 lec16-slidesHomework 3Homework 2WS16-solution 

7

Mon

Feb 20

Lecture 17: Midterm Review

   lec17-slides    

 

Wed

Feb 22

Lecture 18: Limitations of Functional parallelism.
Abstract vs. real performance. Cutoff Strategy

  worksheet18lec18-slides  WS18-solution 

 

Fri

Feb 24 

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, worksheet19lec19-slides  WS19-solution 

8

Mon

Feb 27

Lecture 20: Confinement & Monitor Pattern. Critical sections
Global lock

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 Demonstrationworksheet20lec20-slides        WS20-solution 

 

Wed

Mar 01

Lecture 21:  Atomic variables, Synchronized statements

Module 2: Sections 5.4, 7.2

Topic 5.4 Lecture, Topic 5.4 Demonstration, Topic 7.2 Lectureworksheet21lec21-slides  WS21-solution 

 

Fri

Mar 03

Lecture 22: Parallel Spanning Tree, other graph algorithms 

  worksheet22lec22-slidesHomework 4

Homework 3

WS22-solution 

9

Mon

Mar 06

Lecture 23: Java Threads and Locks

Module 2: Sections 7.1, 7.3

Topic 7.1 Lecture, Topic 7.3 Lecture

worksheet23 lec23-slides  

 

WS23-solution 

 

Wed

Mar 08

Lecture 24: Java Locks - Soundness and progress guarantees  

Module 2: 7.5Topic 7.5 Lecture worksheet24 lec24-slides 

 

WS24-solution 

 

Fri

Mar 10

 Lecture 25: Dining Philosophers Problem  Module 2: 7.6Topic 7.6 Lectureworksheet25lec25-slides 

 

WS25-solution 
 

Mon

Mar 13

No class: Spring Break

     

 

  
 WedMar 15

Lecture 24: Java synchronized statement (contd), wait/notify

 Topic 7.3 Lecture worksheet24 lec24-slides No class: Spring Break    

 

   

 

Fri

Mar 1817

Lecture 25: Concurrent Objects, Linearizability of Concurrent ObjectsNo class: Spring Break

    Topic 7.4 Lecture 

 

  Homework 3, Lecture quizzes for topics 7.1 - 7.4 

10

Mon

Mar 2120

Lecture 26: Linearizability (contd), Java locks

 Topic 7.3 Lecture (recap), Topic 7.4 Lecture (recap) worksheet26 lec26-slides

Homework 4

(3 weeks, with one intermediate checkpoint)N-Body problem, applications and implementations 

  worksheet26lec26-slides   WS26-solution 

 

Wed

Mar 2322

Lecture 27: Parallel Design Patterns, Safety and Liveness Properties  

Read-Write Locks, Linearizability of Concurrent Objects

Module 2: 7.3, 7.4Topic 7.5 Lecture3 Lecture, Topic 7.4 Lectureworksheet27lec27-slides

 

 WS27-solution 

 

Fri

Mar

24

Lecture 28: Message-Passing programming model with Actors

 Module 2: 6.1, 6.2Topic 6.1 Lecture, Topic 6.1 Demonstration,   Topic 6.2 Lecture, Topic 6.2 Demonstration  worksheet28lec28-slides

 

 

 

WS28-solution 

11

Mon

Mar 2827

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, 6.4Topic 6.3 Lecture, Topic 6.5 3 Demonstration,   Topic 6.6 4 Lecture, Topic 6.6 4 Demonstrationworksheet29lec29-slides

Lec29Slide2ThreadRing.java
Lec29Slide4EchoActor.java
Lec29Slide6Pipeline.java
Lec29Slide15ReqReplyActor.java
Lec29Slide15SyncReplyActor.java

 

 

WS29-solution 

 

Wed

Mar 3029

Lecture 30: Java Synchronizers, Dining Philosophers Problem Task Affinity and locality. Memory hierarchy 

 Topic 7.6 Lecture worksheet30lec30-slides

 

-

Apr 01

Midterm Recess

 WS30-solution 

 

 Lecture quiz for topic 7.6

12

Mon

Apr 04

Fri

Mar 31

Lecture 31: Eureka-style Speculative Task Parallelism

  

Data-Parallel Programming model. Loop-Level Parallelism, Loop Chunking

Module 1: Sections 3.1, 3.2, 3.3Topic 3.1 Lecture, Topic 3.1 Demonstration , Topic 3.2 Lecture,  Topic 3.2 Demonstration, Topic 3.3 Lecture,  Topic 3.3 Demonstrationworksheet31lec31-slides Homework 5

Homework 4 Checkpoint

WS31-1solution 

12

WedMon

Apr 0603

Lecture 32:   Task Affinity with Places (start of Module 3)  Barrier Synchronization with PhasersModule 1: Section 3.4Topic 3.4 Lecture,  Topic 3.4 Demonstrationworksheet32lec32-slides

 

 

WS32-solution 

 

FriWed

Apr 0805

 

Lecture 33: Message Passing Interface (MPI)

  worksheet33lec33-slides

Homework 5

(2 weeks, with 1-week automatic extension)

 

13

Mon

Apr 11

Lecture 34: Message Passing Interface (MPI, contd)

 

  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

worksheet33lec33-slides

 

 WS33-solution 

 

Fri

Apr 07

Lecture 34: Fuzzy Barriers with Phasers

Module 1: Section 4.1Topic 4.1 Lecture, Topic 4.1 Demonstrationworksheet34lec34-slides 

Homework 4
Lecture quiz for topics 6.1 - 6.6.
 

WS34-solution Wed

13

Mon

Apr 1310

Lecture 35: PGAS languages Eureka-style Speculative Task Parallelism 

 

worksheet35lec35-slides

 

 

WS35-solution 
 WedApr 12Lecture 36: Scan Pattern. Parallel Prefix Sum 

 

worksheet36lec36-slides

14

  WS36-solution 
 FriApr 14Lecture 37: Parallel Prefix Sum applications  worksheet-37worksheet37lec37-slides    
14MonApr 17Lecture 38: Overview of other models and frameworks   lec38-slides    
 WedApr 19Lecture 39: Course Review (Lectures 19-38)   lec39-slides Homework 5 (automatic extension till April 29)

-

Tue

May 3

   
 FriApr 21Lecture 40: Course Review (Lectures 19-38)   lec40-slides Homework 5  

Lab Schedule

0  Setup1 13Async-Finish Parallel lab1, lab1-slides
30 and Selectors 06Eureka-style Speculative Task 13 20lab13-handout

Lab #

Date (20152022)

Topic

Handouts

Code Examples

1

Jan 10

Infrastructure

setup

lab0-handout

lab1-handout

 
2Jan 17Functional Programminglab2-handout 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 

3

Jan 24

Java Streams

lab3-handout
 
4Jan 31Futureslab4-handout 

5

Feb 07

Data-Driven Tasks

lab5-handout 
6

Feb 14

Async / Finish

lab6-handout 
-

Feb 21

No lab this week (Midterm)

  
7Feb 28Recursive Task Cutoff Strategylab7-handout 
8Mar 07Java Threadslab8-handout 

-

Mar 14

No lab this week (Spring Break)

  
9Mar 21Concurrent Listslab9-handout 
10Mar 28Actorslab10-handout 
11

Apr

04

Loop Parallelism

lab11-handout 

12-

Apr 13

Parallel Pretty Pictures

lab12-handout

11

No lab this week

  

-

Apr

Message Passing Interface (MPI)

18

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 worksheets, in-class Q&A, Piazza participation, and online quizzes (weighted 10% in-class worksheets (weighted 5% in all).

The purpose of the homeworks homework is to train you to solve problems and to help 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. 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  No late submissions (other than those using slip days mentioned below) will be accepted.

As in COMP 321, all 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.). 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, 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 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

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 are due by the beginning of the class after they are 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.  All submitted homeworks are expected to be the result of your individual effort.  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 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.

Accommodations for Students with Special Needs

...