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COMP 322: Fundamentals of Parallel Programming (Spring 2021)



Mackale Joyner, DH 2063

TAs:Elian Ahmar, Timothy Goh, Kelly Park, Tucker Reinhardt, Mantej Singh, Minh Vu, Thanh Vu, Robert Walsh, Frederick Wang, Xincheng Wang, Yidi Wang  
Admin Assistant:Annepha Hurlock,, DH 3122, 713-348-5186 


Piazza site: (Piazza is the preferred medium for all course communications)


ELEC 323

Lecture location:

Fully Online

Lecture times:

MWF 1:30pm - 2:25pm

Lab locations:

Fully Online

Lab times:

Tu 1:30pm - 2:25pm (TV, MS, TG, RW)

Th 4:50pm - 5:45pm (XW, TR, KP, YW, FW, EA)

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:

  • 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




Date (2021)


Assigned Reading

Assigned Videos (see Canvas site for video links)

In-class Worksheets


Work Assigned

Work Due




Jan 25

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

Module 1: Section 1.1

Topic 1.1 Lecture, Topic 1.1 Demonstration







Jan 27

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 29Lecture 3: Abstract Performance Metrics, Multiprocessor SchedulingModule 1: Section 1.4Topic 1.4 Lecture, Topic 1.4 Demonstrationworksheet3lec3-slides





Feb 01

Lecture 4: Parallel Speedup and Amdahl's Law

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



Feb 03

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



Feb 05

Lecture 6:   Finish Accumulators

Module 1: Section 2.3Topic 2.3 Lecture, Topic 2.3 Demonstrationworksheet6lec6-slides Quiz for Unit 1  
3MonFeb 08

Lecture 7: Map Reduce

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





Feb 10

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  



Feb 12

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   




Feb 15No class (weather)        
 WedFeb 17Spring "Sprinkle" Day (no class)        
 FriFeb 19No class (weather)        


Feb 22

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 Demonstrationworksheet10lec10-slides    



Feb 24

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




Feb 26

 Lecture 12: Data-Driven Tasks 


Module 1: Sections 4.5

Topic 4.5 Lecture   Topic 4.5 Demonstration

worksheet12lec12-slides Quiz for Unit 2  


Mar 01

Spring "Sprinkle" Day (no class)




Mar 03

Lecture 13: Parallelism in Java Streams, Parallel Prefix Sums 

Module 1: Sections 3.7Topic 3.7 Lecture , Topic 3.7 Demonstrationworksheet13lec13-slides

Homework 3 (includes one intermediate checkpoint)


Homework 2  
 FriMar 05

Lecture 14: 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 Demonstrationworksheet14 lec14-slidesQuiz for Unit 3   



Mar 08

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    



Mar 10

Lecture 16: Midterm Review




Mar 12 

Lecture 17: 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 Demonstrationworksheet17lec17-slides    



Mar 15

Lecture 18: Abstract vs. Real Performance

  worksheet18lec18-slides Quiz for Unit 4Quiz for Unit 3  



Mar 17

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    



Mar 19

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 

Quiz for Unit 4




Mar 22

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 Quiz for Unit 5





Mar 24

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 

Homework 3, Checkpoint-1




Mar 26

Spring "Sprinkle" Day (no class)







Mar 29

Lecture 23: Actors (contd)

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

Quiz for Unit 5

 WedMar 31 Lecture 24: Java Threads, Java synchronized statementModule 2: 7.1, 7.2Topic 7.1 Lecture, Topic 7.2 Lecture lec24-slides

Quiz for Unit 6




Apr 02

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

Module 2: 7.1, 7.2Topic 7.1 Lecture, Topic 7.2 Lecture lec25-slides  





Apr 05

Lecture 26: Java Threads (exercise)

   lec26-handout Homework 4 (includes one intermediate checkpoint)Homework 3 (all)  



Apr 07

Lecture 27: Java Locks

Module 2: 7.3Topic 7.3 Lecture  lec27-slides





Apr 09

Lecture 28: Linearizability of Concurrent Objects

Module 2: 7.4Topic 7.4 Lecture lec28-slides







Apr 12

Lecture 30: Safety and Liveness Properties, Java Synchronizers, Dining Philosophers Problem

Module 2: 7.5, 7.6Topic 7.5 Lecture, Topic 7.6 Lecture lec30-slides

Quiz for Unit 7

Quiz for Unit 6




Apr 14

Lecture 31: Message Passing Interface (MPI), (start of Module 3)

 Topic 8.1 Lecture, Topic 8.2 Lecture, Topic 8.3 Lecture lec31-slides





Apr 16

Lecture 32: Message Passing Interface (MPI, contd)

 Topic 8.4 Lecture  lec32-slides 

Quiz for Unit 7




Apr 19

Lecture 33: Message Passing Interface (MPI, contd)

 Topic 8.5 Lecture, Topic 8 Demonstration Video lec33-slides


Homework 4 Checkpoint-1




Apr 21

Lecture 34: Task Affinity with Places



Quiz for Unit 8




Apr 23

Lecture 35: Eureka-style Speculative Task Parallelism






Apr 26

Lecture 36: Algorithms based on Parallel Prefix (Scan) operations   lec36-slides



 WedApr 28Lecture 37: Course Review (Lectures 19-34)   lec37-slides Homework 4 (all)  
 FriApr 30TBD     Quiz for Unit 8  

Lab Schedule

Lab #

Date (2021)




0 Infrastructure Setuplab0-handout 


Jan 26

Async-Finish Parallel Programming with abstract metrics

-Feb 02No lab this week  


Feb 09


-Feb 16No lab this week (classes cancelled)  


Feb 23

Cutoff Strategy and Real World Performance


Mar 02



Mar 09

No lab this week (Midterm exam)

-Mar 16No lab this week (Spring "Sprinkle" Day)  
5Mar 23Loop-level Parallelismlab5-handoutlab5-intro



Isolated Statement and Atomic Variables

- Actors  


Java Threads, Java Locks




Message Passing Interface (MPI)




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 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:

  • 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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