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COMP 322: Fundamentals of Parallel Programming (Spring 2024)


Instructor:

Mackale Joyner, DH 2063

TAs:Haotian Dang, Andrew Ondara, Stefan Boskovic, Huzaifa Ali, Raahim Absar

Piazza site:

https://piazza.com/rice/spring2024/comp322 (Piazza is the preferred medium for all course communications)

Cross-listing:

ELEC 323

Lecture location:

Herzstein Amp

Lecture times:

MWF 1:00pm - 1:50pm

Lab locations:

Mon (Brockman 101)

Tue (Herzstein Amp)

Lab times:

Mon  3:00pm - 3:50pm (SB, HA, AO)

Tue   4:00pm - 4:50pm  (RA, HD)

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.

Course Objectives

The primary goal of COMP 322 is to introduce you to the fundamentals of parallel programming and parallel algorithms, by following a pedagogic approach that exposes you to the intellectual challenges in parallel software without enmeshing you in the jargon and lower-level details of today's parallel systems.  A strong grasp of the course fundamentals will enable you to quickly pick up any specific parallel programming system that you may encounter in the future, and also prepare you for studying advanced topics related to parallelism and concurrency in courses such as COMP 422. 

The desired learning outcomes fall into three major areas:

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.

2) Concurrency: critical sections, atomicity, isolation, high level data races, nondeterminism, linearizability, liveness/progress guarantees, actors, request-response parallelism, Java Concurrency, locks, condition variables, semaphores, memory consistency models.

3) Locality & Distribution: memory hierarchies, locality, data movement, message-passing, 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 homework will place equal emphasis on both theory and practice. The programming component of the course will 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. 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.

Prerequisite    

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.  You are expected to read the relevant sections in each lecture handout before coming to the lecture.  We will also provide a number of references in the slides and handouts.The links to the latest versions of the lecture handouts are included below:

  • Module 1 handout (Parallelism)
  • Module 2 handout (Concurrency)

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

Lecture Schedule



Week

Day

Date (2024)

Lecture

Assigned Reading

Assigned Videos (see Canvas site for video links)

In-class Worksheets

Slides

Work Assigned

Work Due

Worksheet Solutions

1

Mon

Jan 08

Lecture 1: Introduction



worksheet1lec1-slides  



WS1-solution


Wed

Jan 10

Lecture 2:  Functional Programming



worksheet2lec02-slides



WS2-solution

FriJan 12Lecture 3: Higher order functions

worksheet3 lec3-slides   



WS3-solution

2

Mon

Jan 15

No class: MLK










Wed

Jan 17

Lecture 4: Lazy Computation



worksheet4lec4-slides

WS4-solution


Fri

Jan 19

Lecture 5: Java Streams



worksheet5lec5-slidesHomework 1
WS5-solution
3MonJan 22

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 24

Lecture 7: Futures

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



WS7-solution


Fri

Jan 26

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 29 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

WedJan 31Lecture 10: Event-based programming model




worksheet10lec10-slides
Homework 1WS10-solution

FriFeb 02Lecture 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 05

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 07

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 09

No class: Spring Recess










6

Mon

Feb 12

Lecture 14: 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 Demonstrationworksheet14lec14-slides

WS14-solution


Wed

Feb 14

Lecture 15: Limitations of Functional parallelism.
Abstract vs. real performance. Cutoff Strategy



worksheet15lec15-slides



Homework 2WS15-solution

FriFeb 16

Lecture 16: Recursive Task Parallelism  



worksheet16 lec16-slidesHomework 3
WS16-solution

7

Mon

Feb 19

Lecture 17: Midterm Review




lec17-slides




Wed

Feb 21

Lecture 18: Midterm Review




lec18-slides




Fri

Feb 23 

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 Demonstrationworksheet19lec19-slides

WS19-solution

8

Mon

Feb 26 

Lecture 20: 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 Demonstrationworksheet20lec20-slides  

WS20-solution


Wed

Feb 28

Lecture 21: Barrier Synchronization with Phasers

Module 1: Sections 3.4 Topic 3.4 Lecture, Topic 3.4 Demonstrationworksheet21    lec21-slides

WS21-solution


Fri

Mar 01

Lecture 22: Stencil computation. Point-to-point Synchronization with Phasers

Module 1: Sections 4.2, 4.3

Topic 4.2 Lecture, Topic 4.2 Demonstration, Topic 4.3 Lecture, Topic 4.3 Demonstrationworksheet22lec22-slides

WS22-solution

9

Mon

Mar 04

Lecture 23: Fuzzy Barriers with Phasers

Module 1: Section 4.1 Topic 4.1 Lecture, Topic 4.1 Demonstrationworksheet23lec23-slides

Homework 3 (CP 1)

WS23-solution


Wed

Mar 06

Lecture 24: Confinement & Monitor Pattern. Critical sections
Global lock

Module 2: Sections 5.1, 5.2Topic 5.1 Lecture, Topic 5.1 Demonstration, Topic 5.2 Lecture, Topic 5.2 Demonstration, Topic 5.6 Lecture, Topic 5.6 Demonstrationworksheet24 lec24-slides


WS24-solution


Fri

Mar 08

 Lecture 25:  Atomic variables, Synchronized statementsModule 2: Sections 5.4, 7.2Topic 5.4 Lecture, Topic 5.4 Demonstration, Topic 7.2 Lecture worksheet25lec25-slides


WS25-solution

Mon

Mar 11

No class: Spring Break


 






WedMar 13No class: Spring Break








Fri

Mar 15

No class: Spring Break









10

Mon

Mar 18

Lecture 26: Parallel Spanning Tree, other graph algorithms



worksheet26lec26-slides

WS26-solution


Wed

Mar 20

Lecture 27: Java Threads and Locks

Module 2: Sections 7.1, 7.3Topic 7.1 Lecture, Topic 7.3 Lectureworksheet27lec27-slides


Homework 3 (CP 2)WS27-solution


Fri

Mar 22

Lecture 28: Java Locks - Soundness and progress guarantees

Module 2: Section 7.5Topic 7.5 Lectureworksheet28lec28-slides




WS28-solution

11

Mon

Mar 25

Lecture 29:  Dining Philosophers Problem

Module 2: Section 7.6Topic 7.6 Lectureworksheet29lec29-slides



WS29-solution


Wed

Mar 27

Lecture 30: Read-Write Locks, Linearizability of Concurrent Objects

Module 2: Sections 7.3, 7.4Topic 7.3 Lecture, Topic 7.4 Lectureworksheet30lec30-slides



WS30-solution


Fri

Mar 29

Lecture 31: Message-Passing programming model with Actors

Module 2: Sections 6.1, 6.2Topic 6.1 Lecture, Topic 6.1 Demonstration,   Topic 6.2 Lecture, Topic 6.2 Demonstrationworksheet31lec31-slides


WS31-solution

12

Mon

Apr 01

Lecture 32: Active Object Pattern. Combining Actors with task parallelismModule 2: Sections 6.3, 6.4Topic 6.3 Lecture, Topic 6.3 Demonstration,   Topic 6.4 Lecture, Topic 6.4 Demonstrationworksheet32lec32-slides

Homework 4

Homework 3 (All)

WS32-solution


Wed

Apr 03

Lecture 33: Task Affinity and locality. Memory hierarchy



worksheet33lec33-slides



WS33-solution


Fri

Apr 05

Lecture 34: Eureka-style Speculative Task Parallelism

 
worksheet34lec34-slides


WS34-solution

13

Mon

Apr 08

No class: Solar Eclipse









WedApr 10Lecture 35: Scan Pattern. Parallel Prefix Sum


worksheet35lec35-slides
Homework 4 (CP 1)WS35-solution

FriApr 12Lecture 36: Parallel Prefix Sum applications

worksheet36lec36-slides

WS36-solution
14MonApr 15Lecture 37: Overview of other models and frameworks


lec37-slides




WedApr 17Lecture 38: Course Review (Lectures 19-34)
 
lec38-slides
Homework 4 (All)


FriApr 19Lecture 39: Course Review (Lectures 19-34)


lec39-slides




Lab Schedule

Lab #

Date (2023)

Topic

Handouts

Examples

1

Jan 08

Infrastructure setup

lab0-handout

lab1-handout


-Jan 15No lab this week (MLK)

2Jan 22Functional Programminglab2-handout

3

Jan 29

Futures

lab3-handout

4Feb 05Data-Driven Taskslab4-handout

-

Feb 12

No lab this week



-Feb 19No lab this week (Midterm Exam)

5

Feb 26

Loop Parallelism 

lab5-handoutimage kernels
6Mar 04Java Threadslab6-handout
-Mar 11No lab this week (Spring Break)

7Mar 18Recursive Task Cutoff Strategylab7-handout
8Mar 25Concurrent Listslab8-handout
9Apr 01Actorslab9-handout
-

Apr 08

No lab this week (Solar Eclipse)



-

Apr 15

No lab this week



Grading, Honor Code Policy, Processes and Procedures

Grading will be based on your performance on four homework assignments (weighted 40% in all), two exams (weighted 40% in all), lab exercises (weighted 10% in all), online quizzes (weighted 5% in all), and in-class worksheets (weighted 5% in all).

The purpose of the homework is to give you practice in solving problems that deepen your understanding of concepts introduced in class. Homework is due on the dates and times specified in the course schedule.  No late submissions (other than those using slip days mentioned below) will be accepted.

The slip day policy for COMP 322 is similar to that of COMP 321. All students will be given 3 slip days to use throughout the semester. When you use a slip day, you will receive up to 24 additional hours to complete the assignment. You may use these slip days in any way you see fit (3 days on one assignment, 1 day each on 3 assignments, etc.). Slip days will be tracked using the README.md file. Other than slip days, no extensions will be given unless there are exceptional circumstances (such as severe sickness, not because you have too much other work). Such extensions must be requested and approved by the instructor (via e-mail, phone, or in person) before the due date for the assignment. Last minute requests are likely to be denied.

Labs must be submitted by the following Monday at 3pm.  Labs must be checked off by a TA.

Worksheets should be completed by the deadline listed in Canvas so that solutions to the worksheets can be discussed in the next class.

You will be expected to follow the Honor Code in all homework and exams.  The following policies will apply to different work products in the course:

  • In-class worksheets: You are free to discuss all aspects of in-class worksheets with your other classmates, the teaching assistants and the professor during the class. You can work in a group and write down the solution that you obtained as a group. If you work on the worksheet outside of class (e.g., due to an absence), then it must be entirely your individual effort, without discussion with any other students.  If you use any material from external sources, you must provide proper attribution.
  • Weekly lab assignments: You are free to discuss all aspects of lab assignments with your other classmates, the teaching assistants and the professor during the lab.  However, all code and reports that you submit are expected to be the result of your individual effort. If you work on the lab outside of class (e.g., due to an absence), then it must be entirely your individual effort, without discussion with any other students.  If you use any material from external sources, you must provide proper attribution (as shown here).
  • Homework: All submitted homework is expected to be the result of your individual effort. You are free to discuss course material and approaches to 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

Students with disabilities are encouraged to contact me during the first two weeks of class regarding any special needs. Students with disabilities should also contact Disabled Student Services in the Ley Student Center and the Rice Disability Support Services.


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