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COMP 322: Fundamentals of Parallel Programming (Spring

...

2023)

...


Instructor:

Mackale Joyner, DH 2063

Head
TAs:
Jonathan Cai (hw), Paul Jiang (lab 1pm), William Su (lab 4pm)Admin Assistant:Annepha Hurlock, annepha@rice.edu, DH 3122, 713-348-5186Undergraduate TAs:Tory Songyang, Zishi Wang
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/

configure-classes

rice/

spring2020

spring2023/comp322 (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:

Sewell Hall 301

Herzstein Amphitheater

Lecture times:

MWF 1:00pm - 1:50pm

Lab locations:

Sewell Hall 301

Mon (Herzstein Amp), Tue (Keck 100)

Lab times:

Thursday, 1

Mon  3:00pm -

1

3:50pm (Raiyan, Oscar, Mohamed, Ryan, Michelle, Taha, Jasmine)

Tue 4:00pm - 4:50pm (Tina, Delaney, Chase, Hung, Jerry, Kailin)

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., 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.

...

There are no required textbooks for the class. Instead, lecture handouts are provided for each module as follows.  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 is no lecture handout for Module 3 (Distribution and Locality).  The instructors will refer you to optional resources to supplement the lecture slides and videos.

There are also a few optional textbooks that we will draw from during 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:

 

Finally, here are some additional resources that may be helpful for you:

Lecture Schedule

 

...

Lecture Schedule



Week

Day

Date (

2020

2023)

Lecture

Assigned Reading

Assigned Videos (see Canvas site for video links)

In-class Worksheets

Slides

Work Assigned

Work Due

 
Worksheet Solutions

1

Mon

Jan

13

Lab #

Date (2020)

Topic

Handouts

Examples

0 Infrastructure Setuplab0-handout-

1

Jan 16

Async-Finish Parallel Programming with abstract metrics

lab1-handout
- - No lab this week  

2

Jan 30

Futures

lab2-handout
-

3

Feb 06

Cutoff Strategy and Real World Performance

lab3-handout -

-

 

No lab this week - Spring Recess -4

Feb 20

DDFs

lab4-handout -

5

Feb 27

No lab this week (midterm exam)

 

  

6

Mar 05

Loop-level Parallelism

lab5-handout lab5-intro

7

Mar 12

Isolated Statement and Atomic Variables

lab6-handout -

-

 

No lab this week - Spring Break

  8Mar 26Actorslab7-handout-9

Apr 02

Java Threads, Java Locks

lab8-handout -

10

Apr 09

Message Passing Interface (MPI)

lab9-handout -

 

 

Apache Spark

 -

 

 

Eureka-style Speculative Task Parallelism

    

Java's ForkJoin Framework

  

09

Lecture 1:

Task Creation and Termination (Async, Finish)Module 1: Section 1.1Topic 1.1 Lecture, Topic 1.1 Demonstration worksheet1lec1-slides

 

 

 

 

Wed

Jan 15

Lecture 2:  Computation Graphs, Ideal Parallelism

Module 1: Sections 1.2, 1.3Topic 1.2 Lecture, Topic 1.2 Demonstration, Topic 1.3 Lecture, Topic 1.3 Demonstrationworksheet2lec2-slides

Homework 1

 

  FriJan 17Lecture 3: Abstract Performance Metrics, Multiprocessor SchedulingModule 1: Section 1.4Topic 1.4 Lecture, Topic 1.4 Demonstrationworksheet3lec3-slides

 

  

2

Mon

Jan 20

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

       

 

Wed

Jan 22

Lecture 4: Parallel Speedup and Amdahl's Law

Module 1: Section 1.5Topic 1.5 Lecture, Topic 1.5 Demonstrationworksheet4lec4-slidesQuiz for Unit 1  

 

Fri

Jan 24

Lecture 5: Future Tasks, Functional Parallelism ("Back to the Future")Module 1: Section 2.1Topic 2.1 Lecture, Topic 2.1 Demonstrationworksheet5lec5-slides   

3

Mon

Jan 27

Lecture 6:   Finish Accumulators

Module 1: Section 2.3Topic 2.3 Lecture, Topic 2.3 Demonstrationworksheet6lec6-slides    WedJan 29

Lecture 7: Map Reduce

Module 1: Section 2.4Topic 2.4 Lecture, Topic 2.4 Demonstration  worksheet7lec7-slides

Homework 2

Homework 1 

 

Fri

Jan 31

Lecture 8: Data Races, Functional & Structural Determinism

Module 1: Section 2.5, 2.6Topic 2.5 Lecture, Topic 2.5 Demonstration, Topic 2.6 Lecture, Topic 2.6 Demonstration   worksheet8lec8-slides

 

Quiz for Unit 1 

4

Mon

Feb 03

Lecture 9: Java’s Fork/Join Library

Module 1: Sections 2.7, 2.8Topic 2.7 Lecture, Topic 2.8 Lectureworksheet9lec9-slidesQuiz for Unit 2  

 

Wed

Feb 05

Lecture 10: Loop-Level Parallelism, Parallel Matrix MultiplicationModule 1: Sections 3.1, 3.2Topic 3.1 Lecture , Topic 3.1 Demonstration ,  Topic 3.2 Lecture,  Topic 3.2 Demonstration worksheet10lec10-slides   

 

Fri

Feb 07

Lecture 11: Iteration Grouping (Chunking), Barrier Synchronization

Module 1: Sections 3.3, 3.4

Topic 3.3 Lecture , Topic 3.3 Demonstration, Topic 3.4 Lecture  ,   Topic 3.4 Demonstration

worksheet11lec11-slides   

5

Mon

Feb 10

Lecture 12:  Parallelism in Java Streams, Parallel Prefix Sums

Module 1: Section 3.7Topic Topic 3.7 Java Streams, Topic 3.7 Java Streams Demonstrationworksheet12lec12-slides Quiz for Unit 2  

Wed

Feb 12

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 Demonstrationworksheet13lec13-slides

Homework 3 (includes 2 intermediate checkpoints)

Quiz for Unit 3

Homework 2 

-

Fri

Feb 14

Spring Recess

       

6

Mon

Feb 17

Lecture 14: Data-Driven Tasks 

Module 1: Sections 4.5Topic 4.5 Lecture   Topic 4.5 Demonstrationworksheet14 lec14-slides   

 

Wed

Feb 19

Lecture 15:  Point-to-point Synchronization with Phasers

Module 1: Section 4.2, 4.3Topic 4.2 Lecture ,   Topic 4.2 Demonstration, Topic 4.3 Lecture,  Topic 4.3 Demonstrationworksheet15lec15-slides   

 

Fri

Feb 21

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 Demonstrationworksheet16lec16-slidesQuiz for Unit 4Quiz for Unit 3 

7

Mon

Feb 24

Lecture 17: Midterm Review

   lec17-slides   

 

Wed

Feb 26

Lecture 18: Abstract vs. Real Performance

  worksheet18 lec18-slides    

 

Fri

Feb 28

Lecture 19: Critical Sections, Isolated construct (start of Module 2)

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 Demonstrationworksheet19lec19-slides Homework 3, Checkpoint-1 

8

Mon

Mar 02

Lecture 20: Parallel Spanning Tree algorithm, Atomic variables

Module 2: Sections 5.3, 5.4, 5.5Topic 5.3 Demonstration, Topic 5.4 Lecture, Topic 5.4 Demonstration, Topic 5.5 Lecture, Topic 5.5 Demonstrationworksheet20lec20-slides 

 

 

 

Wed

Mar 04

Lecture 21: Actors

Module 2: 6.1, 6.2

Topic 6.1 Lecture ,   Topic 6.1 Demonstration ,   Topic 6.2 Lecture, Topic 6.2 Demonstration

worksheet21 lec21-slides  

 

 

 

Fri

Mar 06

Lecture 22: Actors (contd)

Module 2: 6.3, 6.4, 6.5Topic 6.3 Lecture, Topic 6.3 Demonstration, Topic 6.4 Lecture , Topic 6.4 Demonstration,   Topic 6.5 Lecture, Topic 6.5 Demonstration worksheet22 lec22-slides 

Quiz for Unit 4

 

9

Mon

Mar 09

Lecture 23: TBD

Module 2: 6.6Topic 6.6 Lecture, Topic 6.6 Demonstrationworksheet23 lec23-slides

 

 

 

 

 

Wed

Mar 11

Lecture 24:  TBD

Module 2: TBDTopic TBD  

 

Homework 3, Checkpoint-2

 

 

Fri

Mar 13

No class

    Quiz for Unit 6Quiz for Unit 5 -

M-F

Mar 16 - Mar 20

Spring Break

       

10

Mon

Mar 23

Lecture 25: Java Threads, Java synchronized statement (contd), wait/notify

Module 2: 7.1, 7.2Topic 7.1 Lecture, Topic 7.2 Lectureworksheet25lec25-slides 

 

 

  

Wed

Mar 25

Lecture 26: Java Locks, Linearizability of Concurrent Objects

Module 2: 7.3, 7.4Topic 7.3 Lecture, Topic 7.4 Lectureworksheet26 lec26-slides

Homework 4

(includes one intermediate checkpoint)

 

 

 

 

 

 

  

 

Fri

Mar 27

Lecture 27: Safety and Liveness Properties, Java Synchronizers, Dining Philosophers Problem

Module 2: 7.5, 7.6Topic 7.5 Lecture, Topic 7.6 Lectureworksheet27lec27-slides Quiz for Unit 7

Homework 3 (all)

Quiz for Unit 6

 

11

Mon

Mar 30

Lecture 28: Message Passing Interface (MPI), (start of Module 3)

 Topic 8.1 Lecture, Topic 8.2 Lecture, Topic 8.3 Lecture,worksheet28

lec28-slides

   

 

Wed

Apr 01

Lecture 29:  Message Passing Interface (MPI, contd)

 Topic 8.4 Lecture, Topic 8.5 Lecture, Topic 8 Demonstration Videoworksheet29 lec29-slides

Quiz for Unit 8

  

 

Fri

Apr 03

Lecture 30: Distributed Map-Reduce using Hadoop and Spark frameworks

 Topic 9.1 Lecture (optional, overlaps with video 2.4), Topic 9.2 Lecture, Topic 9.3 Lectureworksheet30 lec30-slides  Quiz for Unit 7 

12

Mon

Apr 06

Lecture 31: TF-IDF and PageRank Algorithms with Map-Reduce

 Topic 9.4 Lecture, Topic 9.5 Lecture, Unit 9 Demonstrationworksheet31 lec31-slides Quiz for Unit 9

 

 

 

Wed

Apr 08

TBD

    

 

Homework 4 Checkpoint-1

 

 

Fri

Apr 10

Lecture 32: Partitioned Global Address Space (PGAS) programming models

 Lectures 10.1 - 10.5, Unit 10 Demonstration (all videos optional – unit 10 has no quiz)worksheet32lec32-slides

 

Quiz for Unit 8

 

13

Mon

Apr 13

Lecture 33: Combining Distribution and Multithreading

  worksheet33lec33-slides  

 

Wed

Apr 15

Lecture 34: Task Affinity with Places

  worksheet34lec34-slides

Homework 5

Homework 4 (all)

 

 

Fri

Apr 17

Lecture 35: Eureka-style Speculative Task Parallelism

  worksheet35

lec35-slides

 

Quiz for Unit 9 

14

Mon

Apr 20

Lecture 36: Algorithms based on Parallel Prefix (Scan) operations

  worksheet36lec36-slides 

 

 

 

Wed

Apr 22

Lecture 37: Algorithms based on Parallel Prefix (Scan) operations, contd.  worksheet37lec37-slides

 

 

 

 

Fri

Apr 24

Lecture 38: Course Review (Lectures 20-38)

   lec38-slides 

Homework 5

 -                     

Lab Schedule

Introduction



worksheet1lec1-slides  



WS1-solution


Wed

Jan 11

Lecture 2:  Functional Programming



worksheet2lec02-slides



WS2-solution

FriJan 13Lecture 3: Higher order functions

worksheet3 lec3-slides   



WS3-solution

2

Mon

Jan 16

No class: MLK










Wed

Jan 18

Lecture 4: Lazy Computation



worksheet4lec4-slides

WS4-solution


Fri

Jan 20

Lecture 5: Java Streams



worksheet5lec5-slidesHomework 1
WS5-solution
3MonJan 23

Lecture 6: Map Reduce with Java Streams

Module 1: Section 2.4Topic 2.4 Lecture, Topic 2.4 Demonstration  worksheet6lec6-slides



WS6-solution


Wed

Jan 25

Lecture 7: Futures

Module 1: Section 2.1Topic 2.1 Lecture , Topic 2.1 Demonstrationworksheet7lec7-slides



WS7-solution


Fri

Jan 27

Lecture 8:  Async, Finish, Computation Graphs

Module 1: Sections 1.1, 1.2Topic 1.1 Lecture, Topic 1.1 Demonstration, Topic 1.2 Lecture, Topic 1.2 Demonstrationworksheet8lec8-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

WedFeb 01Lecture 10: Event-based programming model




worksheet10lec10-slides
Homework 1WS10-solution

FriFeb 03Lecture 11: GUI programming, Scheduling/executing computation graphs

Module 1: Section 1.4Topic 1.4 Lecture , Topic 1.4 Demonstrationworksheet11lec11-slidesHomework 2
WS11-solution
5

Mon

Feb 06

Lecture 12: Abstract performance metrics, Parallel Speedup, Amdahl's Law Module 1: Section 1.5Topic 1.5 Lecture , Topic 1.5 Demonstrationworksheet12lec12-slides

WS12-solution


Wed

Feb 08

Lecture 13: Accumulation and reduction. Finish accumulators

Module 1: Section 2.3

Topic 2.3 Lecture   Topic 2.3 Demonstration

worksheet13lec13-slides 
WS13-solution


Fri

Feb 10

No class: Spring Recess










6

Mon

Feb 13

Lecture 14: Recursive Task Parallelism  



worksheet14lec14-slides

WS14-solution


Wed

Feb 15

Lecture 15: 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 Demonstrationworksheet15lec15-slides



Homework 2WS15-solution

FriFeb 17

Lecture 16: Limitations of Functional parallelism.
Abstract vs. real performance. Cutoff Strategy



worksheet16 lec16-slidesHomework 3
WS16-solution

7

Mon

Feb 20

Lecture 17: Midterm Review




lec17-slides




Wed

Feb 22

Lecture 18: Midterm Review




lec18-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
Homework 3WS21-solution


Fri

Mar 03

Lecture 22: Parallel Spanning Tree, other graph algorithms 


 worksheet22lec22-slidesHomework 4


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 15No class: Spring Break








Fri

Mar 17

No class: Spring Break









10

Mon

Mar 20

Lecture 26: N-Body problem, applications and implementations 



worksheet26lec26-slides

WS26-solution


Wed

Mar 22

Lecture 27: Read-Write Locks, Linearizability of Concurrent Objects

Module 2: 7.3, 7.4Topic 7.3 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 27

Lecture 29: Active Object Pattern. Combining Actors with task parallelism 

Module 2: 6.3, 6.4Topic 6.3 Lecture, Topic 6.3 Demonstration,   Topic 6.4 Lecture, Topic 6.4 Demonstrationworksheet29lec29-slides



WS29-solution


Wed

Mar 29

Lecture 30: Task Affinity and locality. Memory hierarchy 



worksheet30lec30-slides


Homework 4WS30-solution


Fri

Mar 31

Lecture 31: 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-slidesHomework 5


WS31-solution

12

Mon

Apr 03

Lecture 32: Barrier Synchronization with PhasersModule 1: Section 3.4Topic 3.4 Lecture,  Topic 3.4 Demonstrationworksheet32lec32-slides



WS32-solution


Wed

Apr 05

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

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


WS34-solution

13

Mon

Apr 10

Lecture 35: Eureka-style Speculative Task Parallelism


worksheet35lec35-slides



WS35-solution

WedApr 12Lecture 36: Scan Pattern. Parallel Prefix Sum


worksheet36lec36-slides

WS36-solution

FriApr 14Lecture 37: Parallel Prefix Sum applications

worksheet37lec37-slides



14MonApr 17Lecture 38: Overview of other models and frameworks


lec38-slides




WedApr 19Lecture 39: Course Review (Lectures 19-38)


lec39-slides
Homework 5


FriApr 21Lecture 40: Course Review (Lectures 19-38)


lec40-slides



Lab Schedule

Lab #

Date (2023)

Topic

Handouts

Examples

1

Jan 09

Infrastructure setup

lab0-handout

lab1-handout


-Jan 16No lab this week (MLK)

2Jan 23Functional Programminglab2-handout

3

Jan 30

Futures

lab3-handout

4Feb 06Data-Driven Taskslab4-handout

5

Feb 13

Async / Finish

lab5-handout
-Feb 20No lab this week (Midterm Exam)

6

Feb 27

Recursive Task Cutoff Strategy

lab6-handout
7Mar 06Java Threadslab7-handout
-Mar 13No lab this week (Spring Break)

8Mar 20Concurrent Listslab8-handout
9Mar 27Actorslab9-handout
-Apr 03TBD

10

Apr 10

Loop Parallelism

lab10-handout

-

Apr 17

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 in-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 .

Labs must be submitted by the following Wednesday at 4:30pm.  Labs must be checked off by a TA by the following Monday at 11:59pm.

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, 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.

...