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COMP 322: Fundamentals of Parallel Programming (Spring




Tory Songyang, Zishi Wang


Mackale Joyner, DH 2063

Head TAs:Jonathan Cai (hw), Paul Jiang (lab 1pm), William Su (lab 4pm)Admin Assistant:Annepha Hurlock,, DH 3122, 713-348-5186Undergraduate 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: (Piazza is the preferred medium for all course communications, but you can also send email to comp322-staff at rice dot edu if needed)


ELEC 323

Lecture location:

Sewell Hall 301TBD

Lecture times:

MWF 1:00pm - 1:50pm

Lab locations:

Sewell Hall 301TBD

Lab times:

Thursday, 1Mon  3:00pm - 13:50pm , ()

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.


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 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



Quiz for Unit 1 24Topic 2.1 Lecture, Topic 2.1 Demonstration Future Tasks, Functional Parallelism ("Back to the Future")  29 Map Reduce4 4   Homework 1Quiz for Unit 2 05 Loop-Level Parallelism, Parallel Matrix MultiplicationTopic 3.1 Lecture , Topic 3.1 Demonstration ,  Topic 3.2 Lecture,  Topic 3.2 Demonstration   07 Iteration Grouping (Chunking), Barrier Synchronization Topic 3.3 Lecture , Topic 3.3 Demonstration, Topic 3.4 Lecture  ,   Topic 3.4 Demonstration   10  Parallelism in Java Streams, Parallel Prefix Sums Topic 3.7 Java Streams 37 Java Streams Quiz for Unit 2 12 Iterative Averaging Revisited, SPMD pattern 3 3 , Topic 3.6 Lecture,   Topic 3.6 DemonstrationHomework 2 17 Data-Driven Tasks 45 45 21 Pipeline Parallelism, Signal Statement, Fuzzy Barriers44 44 41  Topic 4.1 Quiz for Unit 3Quiz for Unit 4Topic 6.3 Homework 3, Checkpoint-2 Module 2: 7.2lec28 15 34: Task Affinity with PlacesHomework 4 (all)Lecture 35: Eureka-style Speculative Task Parallelismlec35slidesQuiz for Unit 9Mon 20 36Algorithms based on (Scan) operationsWed 22 37: Algorithms based on Parallel Prefix (Scan) operations, contd.Fri 24 38 20Homework 5



Date (20202022)


Assigned Reading

Assigned Videos (see Canvas site for video links)

In-class Worksheets


Work Assigned

Work Due

Worksheet Solutions 



Jan 1309

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

Module 1: Section 1.1

Topic 1.1 Lecture, Topic 1.1 Demonstration Introduction









Jan 1511

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

 Functional Programming worksheet2lec02-slides



 FriJan 1713Lecture 3: Abstract Performance Metrics, Multiprocessor SchedulingModule 1: Section 1.4Topic 1.4 Lecture, Topic 1.4 Demonstrationworksheet3lec3-slidesHigher order functions  worksheet3 lec3-slides   





Jan 20No lecture, School Holiday (Martin Luther King, Jr. Day)16

No class: MLK




Jan 2218

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








Module 1: Section 2.1

Lecture 5:

Java Streams

  worksheet5lec5-slidesHomework 1 WS5-solution 
3MonJan 2723

Lecture 6:   Finish Accumulators Map Reduce with Java Streams

Module 1: Section 2.34Topic 2.3 4 Lecture, Topic 2.3 4 Demonstration  worksheet6lec6-slides







Lecture 7:


Module 1: Section 2.41Topic 2.1 Lecture , Topic 2.1 Demonstrationworksheet7lec7-slides

Homework 2





Jan 3127

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

Module 1: Section 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 1 WS8-solution 



 Feb 03

Jan 30 Lecture 9: Java’s Fork/Join LibraryAsync, Finish, Data-Driven Tasks 

Module 1:

Sections 2

Section 1.












1 Lecture, Topic

2.8 Lecture

1.1 Demonstration, Topic 4.5 Lecture, Topic 4.5 Demonstration


lec9-slidesslides   WS9-solution 
 WedFeb 01Lecture 10: Module 1: Sections 3.1, 3.2 Event-based programming model


  worksheet10lec10-slides Homework 1WS10-solution 
 FriFeb 03Lecture 11: Module 1: Sections 3.3, 3.4 GUI programming as an example of event-based,
futures/callbacks in GUI programming
  worksheet11lec11-slidesHomework 2 WS11-solution 




Lecture 12: Scheduling/executing computation graphs
Abstract performance metrics
Module 1: Section 31.74Topic 1.4 Lecture , Topic 1.4 Demonstrationworksheet12lec12-slides  WS12-solution 





Lecture 13:

Parallel Speedup, Critical Path, Amdahl's Law

Module 1: Sections 3Section 1.5, 3.6


1.5 Lecture , Topic

1.5 Demonstration


Homework 3 (includes 2 intermediate checkpoints)

Quiz for Unit 3




Feb 1410

No class: Spring Recess






Lecture 14:

Accumulation and reduction. Finish accumulators

Module 1: Sections 4Section 2.53Topic 2.3 Lecture   Topic 2.3 Demonstrationworksheet14lec14-slides  WS14-solution 



Feb 1915

Lecture 15: Recursive Task Parallelism   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 Demonstration




 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-slidesQuiz for Unit 4Homework 3Homework 2WS16-solution 



Feb 2420

Lecture 17: Midterm Summary Midterm Review




Feb 26Midterm Review (interactive Q&A)22

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

   worksheet18lec18-slides   WS18-solution 



Feb 2824 

Lecture 18: Abstract vs. Real Performance

  worksheet18 lec18-slides  Homework 3, Checkpoint-1 



Mar 02

Lecture 19: TBD

Module 1: Sections TBDTopic TBD

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 



Mar 04Feb 27

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

Module 2: Sections 5.1, 5.2, 5.3, 5.4, 5.6 Topic 5.1 Lecture, Topic 5.1 Demonstration, Topic 5.2 Lecture, Topic 5.2 Demonstration, Topic 5.3 Lecture, Topic 5.3 Demonstration, Topic 5.4 Lecture, Topic 5.4 Demonstration, Topic 5.6 Lecture, Topic 5.6 Demonstrationworksheet20lec20-slides        WS20-solution 



Mar 0601

Lecture 21:  Read-Write Isolation, Review of Phasers Atomic variables, Synchronized statements

Module 2:


Sections 5.


4, 7.2

Topic 5.5 4 Lecture, Topic 5.5 Demonstration4 Demonstration, Topic 7.2 Lectureworksheet21lec21-slidesQuiz for Unit 5  WS21-solution 



Mar 0903

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

Lecture 22: Actors

Module 2: 6.1, 6.2

Parallel Spanning Tree, other graph algorithms 

  worksheet22lec22-slides Homework 4


 Homework 3




Mar 1106

Lecture 23:   Actors (contd)Java Threads and Locks

Module 2: 6Sections 7.31, 6.4, 6.5, 6.67.3

Topic 7.1 Lecture, Topic



Demonstration, Topic 6.4 Lecture , Topic 6.4 Demonstration,   Topic 6.5


, Topic 6.5 Demonstration, Topic 6.6 Lecture, Topic 6.6 Demonstration

worksheet23 lec23-slides

Quiz for Unit 6






Mar 1308

Lecture 24: Java Threads, Java synchronized statementLocks - Soundness and progress guarantees  

Module 2: 7.1, 7.25Topic 7.1 Lecture, Topic 7.2 5 Lecture worksheet24 lec24-slides  Quiz for Unit 5




Mar 16 - Mar 20

Spring Break







Mar 23

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

Mar 10

 Lecture 25: Dining Philosophers Problem  Module 2: 7.6Topic 7.2 6 Lectureworksheet25lec25-slides 




Mar 2513

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)No class: Spring Break



 WedMar 15No class: Spring Break    





Mar 2717

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




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

No class: Spring Break






Mar 20

Lecture 26: N-Body problem, applications and implementations 

  worksheet26lec26-slides   WS26-solution 


WedApr 01

Mar 22


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

27: Read-Write Locks, Linearizability of Concurrent Objects

Module 2: 7.3, 7.4Topic 7.3 Lecture, Topic 7.4 Lectureworksheet27lec27-slides





03Mar 24

Topic 9.1 Lecture (optional, overlaps with video 2.4), Topic 9

Lecture 3028: Distributed Map-Reduce using Hadoop and Spark frameworks


Message-Passing programming model with Actors

Module 2: 6.1, 6.2Topic 6.1 Lecture, Topic 6.1 Demonstration,   Topic 6.2 Lecture, Topic 96.3 Lecture2 Demonstrationworksheet30 worksheet28lec30lec28-slides



Quiz for Unit 7





06Mar 27

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

 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






Apr 08



Mar 29

Lecture 30: Task Affinity and locality. Memory hierarchy 






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

Homework 4




Apr 03

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






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





Homework 4 Checkpoint-1WS33-solution 



Apr 1007

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 834: Fuzzy Barriers with Phasers

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





Apr 1310

Lecture 33: Combining Distribution and Multithreading35: Eureka-style Speculative Task Parallelism 





 WedApr 12Lecture   worksheet34lec34-slides

Homework 5

36: Scan Pattern. Parallel Prefix Sum 



Apr 17

worksheet36lec36-slides  worksheet35WS36-solution 


FriApr 14Lecture 37: Parallel Prefix Sum applications  worksheet36worksheet37lec36lec37-slides    
14MonApr 17Lecture 38: Overview of other models and frameworks  worksheet37 lec37lec38-slides    
 WedApr 19Lecture 39: Course Review (Lectures 19-38)   lec38lec39-slides  -  
  Fri  Apr 21Lecture 40: Course Review (Lectures 19-38)    lec40-slides         Homework 5  

Lab Schedule

0  Setup 30Futures- No lab this week - Spring Recess 20DDFs5 27midterm exam
  12lab6- 09-   Java's ForkJoin Framework

Lab #

Date (20202023)





Jan 09






Jan 16

Async-Finish Parallel Programming with abstract metrics


- Jan 16No lab this week (MLK)  
2Jan 23Functional Programminglab2-handout 


Feb 06

Cutoff Strategy and Real World Performance

lab3-handout -



Jan 30

Java Streams

4Feb 06Futureslab4-handout 


Feb 13

Data-Driven Tasks

-Feb 20No lab this week (Midterm)  

Mar 05

Loop-level Parallelism

lab5-handout lab5-intro

Feb 27

Async / Finish


Isolated Statement and Atomic Variables

06Recursive Task Cutoff Strategylab7-handout 
- Mar 13No lab this week - (Spring Break)  
8Mar 2620ActorsJava Threadslab7lab8-handout- 

Apr 02

Java Threads, Java Locks

lab8Mar 27Concurrent Listslab9-handout- 

Message Passing Interface (MPI)

lab9-handout 03Actorslab10-handout 

Apache Spark




Eureka-style Speculative Task Parallelism

Apr 10

Loop Parallelism




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 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 be denied.

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.