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COMP 322: Fundamentals of Parallel Programming (Spring 2022)



Mackale Joyner, DH 2063

Zoran Budimlić, DH 1038

TAs:Adrienne Li, Austin Hushower, Claire Xu, Diep Hoang, Hunena Badat, Maki Yu, Mantej Singh, Rose Zhang, Victor Song, Yidi Wang 
Admin Assistant:Annepha Hurlock, annepha@rice.edu, DH 3122, 713-348-5186 


Piazza site:

https://piazza.com/rice/spring2022/comp322 (Piazza is the preferred medium for all course communications)


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 ()

Wed 4:30pm - 5:20pm ()

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 (course modules):

1) Parallelism: 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, 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 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.  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:

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





Date (2022)


Assigned Reading

Assigned Videos (see Canvas site for video links)

In-class Worksheets


Work Assigned

Work Due




Jan 10

Lecture 1: Task Creation and Termination (Async, Finish)

Module 1: Section 1.1

Topic 1.1 Lecture, Topic 1.1 Demonstration







Jan 12

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 14Lecture 3: Abstract Performance Metrics, Multiprocessor SchedulingModule 1: Section 1.4Topic 1.4 Lecture, Topic 1.4 Demonstrationworksheet3lec3-slides





Jan 17

Lecture 4: Parallel Speedup and Amdahl's Law

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



Jan 19

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



Jan 21

Lecture 6:   Finish Accumulators

Module 1: Section 2.3Topic 2.3 Lecture, Topic 2.3 Demonstrationworksheet6lec6-slides Quiz for Unit 1  
3MonJan 24

Lecture 7: Map Reduce

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





Jan 26

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

Homework 2

Homework 1  



Jan 28

Lecture 9: Java’s Fork/Join Library

 Topic 2.7 Lecture, Topic 2.7 Demonstration, Topic 2.8 Lecture, Topic 2.8 Demonstrationworksheet9lec9-slidesQuiz for Unit 2   




Jan 31         
 WedFeb 02

 Lecture 12: Data-Driven Tasks 


Module 1: Section 4.5

Topic 4.5 Lecture   Topic 4.5 Demonstration

 FriFeb 04         


Feb 07

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



Feb 09

Lecture 13: Lightweight task parallelism. Finish/async

Module 1: Section 1.1

Topic 1.1 Lecture , Topic 1.1 Demonstration




Feb 11

No class: Spring Recess


     Quiz for Unit 2  


Feb 14

Lecture 14: Parallel Speedup, Critical Path, Amdah's Law

Module 1: Section 1.5Topic 1.5 Lecture   Topic 1.5 Demonstrationworksheet14lec14-slides    



Feb 16

Lecture 15: Recursive Task Parallelism 


Homework 3 (includes one intermediate checkpoint)


Homework 2  
 FriFeb 18

Lecture 16: Accumulation and reduction. Finish accumulators

Module 1: Section 2.3Topic 2.3 Lecture , Topic 2.3 Demonstrationworksheet16 lec16-slidesQuiz for Unit 3   



Feb 21

Lecture 17: Midterm Review




Feb 23

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




Feb 25 

Lecture 19: 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 Demonstrationworksheet19lec19-slides    



Feb 28

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   Quiz for Unit 4Quiz for Unit 3  



Mar 02

Lecture 21: N-Body problem, applications and implementations




Mar 04

Lecture 22: Fork/Join programming model. OS Threads. Scheduler Pattern

Module 2: Sections 7.1, 7.2Topic 7.1 Lecture, Topic 7.2 Lectureworksheet22lec22-slides 

Quiz for Unit 4




Mar 07

Lecture 23: Locks, Atomic variables

Module 2: 7.3

Topic 7.3 Lecture

worksheet23 lec23-slides Quiz for Unit 5





Mar 09

Lecture 24: Parallel Spanning Tree, other graph algorithms

  worksheet24 lec24-slides 

Homework 3, Checkpoint-1




Mar 11

 Lecture 25: Linearizability of Concurrent ObjectsModule 2: 7.4Topic 7.4 Lectureworksheet25lec25-slidesQuiz for Unit 6

Quiz for Unit 5



Mar 14

No class: Spring Break



 WedMar 16No class: Spring Break    





Mar 18

No class: Spring Break






Mar 21

Lecture 26: Java Locks - Soundness and progress guarantees

Module 2: 7.5Topic 7.5 Lecture worksheet26lec26-slides Homework 4 (includes one intermediate checkpoint)Homework 3 (all)  



Mar 23

Lecture 27: Dining Philosophers Problem

Module 2: 7.6Topic 7.4 Lecture Topic 7.6 Lectureworksheet27lec27-slides





Mar 25

Lecture 28: Read-Write Pattern. Read-Write Locks. Fairness & starvation

Module 2: 7.3, 7.5Topic 7.3 Lecture, Topic 7.5 Lecture, worksheet28lec28-slides

Quiz for Unit 7






Mar 28

Lecture 29: Task Affinity and locality. Memory hierarchy



Quiz for Unit 6




Mar 30

Lecture 30: Reactor Pattern. Web servers






Apr 01

Lecture 31: Scan Pattern. Parallel Prefix Sum, uses and algorithms

  worksheet31lec31-slidesQuiz for Unit 8

Quiz for Unit 7




Apr 04

Lecture 32: Data-Parallel Programming model. Loop-Level Parallelism, Loop ChunkingModule 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 Demonstrationworksheet32lec32-slides


Homework 4 Checkpoint-1




Apr 06

Lecture 33: Barrier Synchronization with phasers

Module 1: Section 3.4

Topic 3.4 Lecture ,   Topic 3.4 Demonstration






Apr 08

Lecture 34:  Stencil computation. 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 Demonstrationworksheet34lec34-slides 

Quiz for Unit 8




Apr 11

Lecture 35: Message-Passing programming model with ActorsModule 2: 6.1, 6.2

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




 WedApr 13Lecture 36: Active Object Pattern. Combining Actors with task parallelismModule 2: 6.3, 6.4

Topic 6.3 Lecture ,   Topic 6.3 Demonstration ,   Topic 6.4 Lecture, Topic 6.4 Demonstration

worksheet36lec36-slides Homework 4 (all)  
 FriApr 15Lecture 37: Eureka-style Speculative Task Parallelism  worksheet37lec37-slides    
14MonApr 18Lecture 38: Overview of other models and frameworks   lec38-slides    
 WedApr 20Lecture 39: Course Review (Lectures 19-38)   lec39-slides    
 FriApr 22Lecture 40: Course Review (Lectures 19-38)   lec40-slides    

Lab Schedule

Lab #

Date (2021)




0 Infrastructure Setuplab0-handout 


Jan 10

Async-Finish Parallel Programming with abstract metrics

-Jan 17   


Jan 24


-Jan 31   


Feb 07

Cutoff Strategy and Real World Performance


Feb 14



Feb 21

No lab this week (Midterm exam)

-Feb 28   
5Mar 07Loop-level Parallelismlab5-handoutlab5-intro


Mar 14

Isolated Statement and Atomic Variables

-Mar 21   
7Mar 28Java Threads, Java Lockslab7-handout 

Apr 04




Apr 11

Message Passing Interface (MPI)



Apr 18

Apache Spark




Eureka-style Speculative Task Parallelism


Java's ForkJoin Framework


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 11:59pm.  Labs must be checked off by a TA.

Worksheets should be completed in Canvas before the start of the following class (for full credit) 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:


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.