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COMP 322: Fundamentals of Parallel Programming (Spring

...

2023)

 

Marc Canby, Anna Chi, Peter Elmers, Joseph Hungate, Cary Jiang, Gloria Kim, Kevin Mullin, Victoria Nazari, Ashok Sankaran, Sujay Tadwalkar, Anant Tibrewal, Vidhi Vakharia, Eugene Wang, Yufeng Zhou

Instructor:

Prof. Vivek SarkarMackale Joyner, DH 31312063

Head TATAs:Max Grossman

Admin Assistant:

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

Graduate TAs:

Jonathan Sharman, Ryan Spring, Bing Xue, Lechen Yu

Co-Instructor:Dr. Mackale JoynerUndergraduate 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/classrice/spring2022/ixdqx0x3bjl6encomp322 (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 group office hours during 2pm - 3pm, usually in DH 3092)

Lab locations:

DH 1042, DH 1064TBD

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 Canvas are included below:

  • Module 1 handout (Parallelism)
  • Module 2 handout (Concurrency)
  • Module 3 handout (Distribution and Locality)

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:

Past Offerings of COMP 322

Lecture Schedule

 

...

Week

...

Day

...

Date (2017)

...

Lecture

...

Assigned Videos

...

Lecture Schedule

 

 

Future Tasks, Functional ParallelismTopic 2.1 Lecture ,   Topic 2.1 Demonstration Finish Accumulators3   3   Homework 1 Java’s Fork/Join Library Loop-Level Parallelism, Parallel Matrix Multiplication, Iteration Grouping (Chunking)   Barrier Synchronization 3 3 Iterative Averaging Revisited, SPMD pattern 3 3 , Topic 3.6 Lecture,   Topic 3.6 Demonstration  -  Data-Driven Tasks and Data-Driven Futures 45 , 45 Pipeline Parallelism, Signal Statement, Fuzzy Barriers 44 44 41  Topic 4.1 Demonstration,  lec21-slides   Wed 22 lec27-slides   lec38slides- 24Review session / Office Hours, 1pm - 3pm, location TBD-Scheduled final exam (Exam 2 – scope of exam limited to lectures 18-37), location and time TBD by registrar

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: Task Creation and Termination (Async, Finish)

Module 1: Section 1.1

Topic 1.1 Lecture, Topic 1.1 Demonstration Introduction

 

 

worksheet1lec1-slidesslides  

 

 

WS1-solution 

 

Wed

Jan 11

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 1Functional Programming

GList.java worksheet2lec02-slides

 

 

WS2-solution 
 FriJan 13Lecture 3: Abstract Performance Metrics, Multiprocessor SchedulingModule 1: Section 1.4Topic 1.4 Lecture, Topic 1.4 Demonstrationworksheet3lec3-slidesHigher order functions  worksheet3 lec3-slides   

 

 WS3-solution 

2

Mon

Jan 16

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

        

 

Wed

Jan 18

Lecture 4:   Parallel Speedup and Amdahl's LawModule 1: Section 1.5Topic 1.5 Lecture, Topic 1.5 DemonstrationLazy Computation

LazyList.java

Lazy.java

 worksheet4lec4-slides  WS4-solution 

 

Fri

Jan 20

Lecture 5:

Module 1: Section 2.1

Java Streams

  worksheet5lec5-slidesHomework 1 WS5-solution 
3MonJan 23

Lecture 6: Memoization Map Reduce with Java Streams

Module 1: Section 2.24Topic 2.2 4 Lecture  ,  Topic 2.2 4 Demonstration  worksheet6lec6-slides

 

 WS6-solution 

 

Wed

Jan 25

Lecture 7:

Futures

Module 1: Section 2.31Topic 2.1 Lecture, Topic 2.1 Demonstrationworksheet7lec7-slides

Homework 2

 

 WS7-solution 

 

Fri

Jan 27

Lecture 8: Data Races, Functional & Structural Determinism  Computation Graphs, Ideal Parallelism

Module 1: Sections 1.2.5, 21.63Topic 1.2 .5 Lecture  ,   Topic 1.2 .5 Demonstration, Topic 21.6 3 Lecture  ,   Topic 21.6 3 Demonstration   worksheet8lec8-slides  Quiz for Unit 1WS8-solution 

4

Mon

 

Jan 30 Lecture 9: Map ReduceAsync, Finish, Data-Driven Tasks 

Module 1: Section

2

1.1, 4.5

 

Topic 2.4 Lecture  ,  Topic 2.4 Demonstration   

Topic 1.1 Lecture, Topic 1.1 Demonstration, Topic 4.5 Lecture, Topic 4.5 Demonstration

worksheet9

lec9-slidesslides   WS9-solution 
 WedFeb 01Lecture 10: FJP chapter: Sections 7.3 & 7.5Event-based programming model

 

  worksheet10lec10-slides Homework 1WS10-solution 
 FriFeb 03Module 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

Lecture 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 31.4Topic 1.4 Lecture , Topic 1.4 Demonstrationworksheet12lec12-slides  WS12-solution 

 

Wed

Feb 08

Lecture 13:

Parallel Speedup, Critical Path, Amdahl's Law

Module 1: Sections 3Section 1.5, 3.6

Topic

1.5 Lecture , Topic

1.5 Demonstration

  worksheet13lec13-slides 

Homework 3

(includes two intermediate checkpoints)

Homework 2 WS13-solution 

 

Fri

Feb 10

No class: Spring Recess

 

     Quiz for Unit 2   
6

Mon

Feb 13

Lecture 14:

Accumulation and reduction. Finish accumulators

Module 1: Section 42.53Topic 2.3 Lecture   Topic 2.3 Demonstrationworksheet14lec14-slides  WS14-solution 

 

Wed

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

Recursive Task Parallelism  

  worksheet15lec15-slides

 

 

 WS15-solution 
 FriFeb 17

Lecture 16:

Data Races, Functional & Structural Determinism

Module 1: Sections 42.45, 42.16Topic 2.5 Lecture ,  Topic 2.5 Demonstration,  Topic 2.6 Lecture,  Topic 2.6 Demonstrationworksheet16 lec16-slidesHomework 3Homework 2WS16-solution Quiz for Unit 3

7

Mon

Feb 20

Lecture 17: Midterm SummaryReview

    lec18lec17-slides   

Wed

Feb 22

Midterm Review (interactive Q&A, no lecture) 

 

Wed

 Exam 1 held during lab time (7:00pm - 10:00pm), scope of exam limited to lectures 1-17 Homework 3, Checkpoint-1

 

Fri

Feb 24

Lecture 18: Abstract vs. Real PerformanceFeb 22

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

   worksheet17 worksheet18 lec17lec18-slides  Quiz for Unit 4

8

Mon

Feb 27 WS18-solution 

 

Fri

Feb 24 

Lecture 19: Task Scheduling Policies

 

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

WedMon

Mar 01Feb 27

Lecture 20: Critical sections, Isolated construct, Parallel Spanning Tree algorithm (start of Module 2) Confinement & Monitor Pattern. Critical sections
Global lock

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 6 Lecture, Topic 5.3 Demonstration 6 Demonstrationworksheet20lec20-slides        WS20-solution Fri

 

Wed

Mar 0301

Lecture 21:  Atomic variables, Read-Write IsolationSynchronized statements

Module 2: Sections 5.4,

5

7.

5

2

Topic 5.4 Lecture, Topic 5.4 Demonstration, Topic 57.5 Lecture, Topic 5.5 Demonstration, Topic 5.6 Lecture, Topic 5.6 Demonstration  worksheet21 2 Lectureworksheet21lec21-slides  WS21-solution 

 

9

MonFri

Mar 0603

Lecture 22:   Parallelism in Java Streams, Parallel Prefix SumsParallel Spanning Tree, other graph algorithms 

  worksheet22lec22-slidesHomework 4

 

 Homework 3

WS22-solution 

 9

WedMon

Mar 0806

Lecture 23: Java Threads , Java synchronized statement

and Locks

Module 2: Sections 7.1, 7.3

Topic 7.1 Lecture, Topic 7.

2

3 Lecture

worksheet23 lec23-slides  

 Homework 3, Checkpoint-2

WS23-solution 

 

FriWed

Mar 1008

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

Java Locks - Soundness and progress guarantees  

Module 2: 7.5Topic 7.3 5 Lecture worksheet24 lec24-slides  Quiz for Unit 5

 

WS24-

M-F

Mar 13 - Mar 17

Spring Break

      

10

Mon

Mar 20

Lecture 25: Concurrent Objects, Linearizability of Concurrent Objects

  Topic 7.4 solution 

 

Fri

Mar 10

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

 

WS25-solution 
 

Mon

Mar 13

Lecture 26: Linearizability (contd), Java locks

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

Homework 4

(includes one intermediate checkpoint)

Homework 3 (all)

 

Fri

Mar 24

Lecture 27: Parallel Design Patterns, Safety and Liveness Properties  

 Topic 7.5 Lecture worksheet27

No class: Spring Break

     

 

  
 WedMar 15No class: Spring Break    

 

   

11 

MonFri

Mar 2717

Lecture 28: Actors

 Topic 6.1 Lecture ,   Topic 6.1 Demonstration ,   Topic 6.2 Lecture, Topic 6.2 Demonstration, Topic 6.3 Lecture, Topic 6.3 Demonstration worksheet28

lec28-slides

 

No class: Spring Break

     

 

  

10

Mon

Mar 20

Lecture 26: N-Body problem, applications and implementations 

  worksheet26lec26-slides   WS26-solution 

 

Wed

Mar 2922

Lecture 29:  Actors (contd)

 Topic 6.4 Lecture , Topic 6.4 Demonstration ,   Topic 6.5 Lecture, Topic 6.5 Demonstration, Topic 6.6 Lecture, Topic 6.6 Demonstration worksheet29 lec29-slides

 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 3124

lec30

Lecture 30: Java Synchronizers, Dining Philosophers Problem

 Topic 7.6 Lecture worksheet30

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

 

 

12

Mon

Apr 03

Lecture 31: Eureka-style Speculative Task Parallelism

   worksheet31 lec31-slides

 

WS28-solution 

11 

Mon

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)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 

   worksheet33 worksheet30 lec33lec30-slides

 

 

13

Mon

Apr 10

Lecture 34: Message Passing Interface (MPI, contd)WS30-solution 

 

 

Fri

worksheet34 lec34-slides  

 

Wed

Apr 12

Lecture 35: GPU Computing

  worksheet35lec35-slides

Homework 5  

(Due April 21st, with automatic extension until May 1st after which slip days may be used)

Homework 4 (all)

 

Fri

Apr 14

Lecture 36: Partitioned Global Address Space (PGAS) programming models

  worksheet36

lec36Mar 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

Homework 4

WS31-solution 

12

Mon

Apr 03

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

 

 

14

Mon

Apr 17

Lecture 37: Apache Spark framework

  worksheet37lec37-slides WS32-solution 

 

Wed

Apr 1905

Lecture

38: Topic TBD

 

  

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 2107

Lecture 39: Course Review (lectures 20-37), Last day of classes

34: Fuzzy Barriers with Phasers

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

 

WS34-solution 

Homework 5 (automatic extension until May 1st, after which slip days may be used)

13

Mon

Apr

10

Lecture 35: Eureka-style Speculative Task Parallelism 

 

worksheet35lec35-slides

 

 

WS35-solution 
 -WedApr 26Review session / Office Hours, 1pm - 3pm, location TBD12Lecture 36: Scan Pattern. Parallel Prefix Sum 

 

worksheet36lec36-slides  WS36-solution 
 -FriApr 28Review session / Office Hours, 1pm - 3pm, location TBD14Lecture 37: Parallel Prefix Sum applications  worksheet37lec37-slides    
14

 

April 26 - May 3

MonApr 17Lecture 38: Overview of other models and frameworks   lec38-slides   

Lab Schedule

Isolated Statement and Atomic Variables

Lab #

Date (2017)

Topic

Handouts

Code Examples

0 Infrastructure Setuplab0-handout-

1

Jan 11

Async-Finish Parallel Programming

lab1-handout, lab1-slides
lab_1.zip

2

Jan 18

Abstract performance metrics with async & finish

lab2-handout, lab2-slides
lab_2.zip

3

Jan 25

DIY HJ-lib Programming, Futures, HJ-Viz 

lab3-handout, lab3-slides lab_3.zip

4

Feb 01

Finish Accumulators and Loop-Level Parallelism

lab4-handout, lab4-slides   lab_4.zip

5

Feb 08

Loop Chunking and Barrier Synchronization

lab5-handout, lab5-slides lab_5.zip

6

Feb 15

Data-Driven Futures and Phasers

lab6-handout   lab_6.zip

-

Feb 22

No lab this week — Exam 1

--

7

Mar 01

 
 WedApr 19Lecture 39: Course Review (Lectures 19-38)   lec39-slides    
 FriApr 21Lecture 40: Course Review (Lectures 19-38)   lec40-slides Homework 5  

Lab Schedule

08 29 and Selectors 05Eureka-style Speculative Task 13 19lab13-handout

Lab #

Date (2022)

Topic

Handouts

Examples

1

Jan 10

Infrastructure setup

lab0-handout

lab1-handout

 
2Jan 17Functional Programminglab2-handout 

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 1514

No lab this week (Spring Break)

  
9Mar 2221Java LocksConcurrent Listslab9-handout 
10Mar 28Actorslab10-handout 
11

Apr

04

Loop Parallelism

lab11-handout 

12-

Apr 12

Message Passing Interface (MPI)

lab12-handout

11

No lab this week

  

-

Apr

Apache Spark

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 in-class Q&A, worksheets, Piazza participation 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.  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.). 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 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,

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 homeworks and exams.  All submitted homeworks are expected 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

...

  • 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

...