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COMP 322: Fundamentals of Parallel Programming (Spring







Prof. Vivek SarkarMackale Joyner, DH 3131

Head TA:Max Grossman


Zoran Budimlić, DH 3003

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, , DH 3122, 713-348-5186

Graduate TAs:

Jonathan Sharman, Ryan Spring, Bing Xue, Lechen Yu

Co-Instructor:Dr. Mackale JoynerUndergraduate TAs:

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



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:

Herzstein Hall 210Amphitheater (online 1st 2 weeks)

Lecture times:

MWF 1:00pm - 1:50pm (followed by group office hours during 2pm - 3pm, usually in DH 3092)

Lab locations:

DH 1042, DH 1064Keck 100 (online 1st 2 weeks)

Lab times:

Wednesday, 07Mon  3:00pm - 08:30pm3:50pm (Austin, Claire)

Wed 4:30pm - 5:20pm (Hunena, Mantej, Yidi, Victor, Rose, Adrienne, Diep, Maki)

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 to the latest versions on Canvas are included below:

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

You  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 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



20 Future Tasks, Functional ParallelismTopic 2.1 Lecture ,   Topic 2.1 Demonstration 25 Finish Accumulators3   3    01 Java’s Fork/Join Library 03 Loop-Level Parallelism, Parallel Matrix Multiplication, Iteration Grouping (Chunking) 06  Barrier Synchronization 3 3 08 Iterative Averaging Revisited, SPMD pattern 3 3 , Topic 3.6 Lecture,   Topic 3.6 Demonstration   13  Data-Driven Tasks and Data-Driven Futures 45 , 45 17 Pipeline Parallelism, Signal Statement, Fuzzy Barriers 44 44 41  Topic 4.1 Demonstration,910Mon 20Lecture 25: Concurrent Objects, Linearizability of Concurrent ObjectsWed 11 27   , Topic 6.3 Lecture, Topic 6.3 DemonstrationMar 29Fri 31 Java Synchronizers, Dining Philosophers Problem1213 14 Partitioned Global Address Space (PGAS) programming models14Mon 17 Apache Spark frameworkWed 19 Topic TBDFri 21lectures 2037, Last day of classes-



Date (20172022)


Assigned Reading

Assigned Videos (see Canvas site for video links)

In-class Worksheets


Work Assigned

Work Due

Worksheet Solutions 



Jan 0910

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

Module 1: Section 1.1

Topic 1.1 Lecture, Topic 1.1 Demonstration Introduction









Jan 1112

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 13Lecture 3: Abstract Performance Metrics, Multiprocessor SchedulingModule 1: Section 1.4Topic 1.4 Lecture, Topic 1.4 Demonstrationworksheet3lec3-slides





Jan 16

No lecture, School Holiday (Martin Luther King, Jr. Day)Functional Programming worksheet2lec02-slides



 FriJan 14Lecture 3: Higher order functions  worksheet3 lec3-slides   





Jan 17

No class: MLK




Jan 1819

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

 worksheet4lec4-slides  WS4-solution 





Lecture 5:

Module 1: Section 2.1

Java Streams

  worksheet5lec5-slidesHomework 1 WS5-solution 
3MonJan 2324

Lecture 6: Memoization Map Reduce with Java Streams

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







Lecture 7:


Module 1: Section 2.31Topic 2.1 Lecture, Topic 2.1 Demonstrationworksheet7lec7-slidesHomework 2


Homework 1 WS7-solution 



Jan 2728

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 1 WS8-solution 




Jan 3031 Lecture 9: Map ReduceAsync, Finish, Data-Driven Tasks 

Module 1: Section


1.1, 4.5



2.4 Lecture  ,  Topic 2.4 Demonstration   

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


lec9-slidesslides   WS9-solution 
 WedFeb 02Lecture 10: FJP chapter: Sections 7.3 & 7.5Event-based programming model


  worksheet10lec10-slides  WS10-solution 
 FriFeb 04Module 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-slides Homework 2Homework 1WS11-solution 




Lecture 12: Scheduling/executing computation graphs
Abstract performance metrics
Module 1: Section 31.4Topic 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 two intermediate checkpoints)

Homework 2





Feb 1011

No class: Spring Recess


    Quiz for Unit 2    




Lecture 14:

Accumulation and reduction. Finish accumulators

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



Feb 1516Topic 4.2 Lecture ,   Topic 4.2 Demonstration, Topic 4.3 Lecture,  Topic 4.3 Demonstration

Lecture 15:  Phasers, Point-to-point Synchronization

Module 1: Sections 4.2, 4.3

Recursive Task Parallelism  




 FriFeb 18

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



Feb 2021

Lecture 17: Midterm SummaryReview



Feb 22

Midterm Review (interactive Q&A, no lecture)



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



Feb 24Wed

Feb 23

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

   worksheet17 worksheet18 lec17lec18-slides  Quiz for Unit 4



Feb 27 WS18-solution 



Feb 25 

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 Wed


Mar 01Mon

Feb 28

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



Mar 0302

Lecture 21:  Atomic variables, Read-Write IsolationSynchronized statements

Module 2: Sections 5.4,





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



Mar 0604

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

  worksheet22lec22-slides Homework 4


 Homework 3

WS22-solution Wed



Mar 0807

Lecture 23: Java Threads , Java synchronized statement


and Locks

Module 2: Sections 7.1, 7.3

Topic 7.1 Lecture, Topic 7.


3 Lecture

worksheet23 lec23-slides  

Homework 3, Checkpoint-2 

WS23-solution Fri



Mar 1009

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




Mar 13 - Mar 17




Mar 11

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




Mar 14

No class: Spring Break



 WedMar 16No class: Spring Break  Topic 7.4 Lecture worksheet25 lec25-slides    





Topic 7.3 Lecture (recap), Topic 7.4 Lecture (recap)

Mar 22

Lecture 26: Linearizability (contd), Java locks



No class: Spring Break






Mar 21

Lecture 26: N-Body problem, applications and implementations 


Homework 4

(includes one intermediate checkpoint)

Homework 3 (all)  WS26-solution Fri



Mar 2423

Lecture 27: Parallel Design Patterns, Safety and Liveness Properties  

Read-Write Locks, Linearizability of Concurrent Objects

Module 2: 7.3, 7.4Topic 7.5 Lecture3 Lecture, Topic 7.4 Lectureworksheet27lec27-slides







Lecture 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








Mar 28

Topic 6.4 Lecture , Topic 6.4 Demonstration ,   Topic 6.5

Lecture 29:   Actors (contd)


Active Object Pattern. Combining Actors with task parallelism 

Module 2: 6.3, 6.4Topic 6.3 Lecture, Topic 6.5 3 Demonstration,   Topic 6.6 4 Lecture, Topic 6.6 4 Demonstrationworksheet29lec29-slides








Lecture 30:

Task Affinity and locality. Memory hierarchy 

 Topic 7.6 Lecture worksheet30lec30-slides





Apr 0301


Lecture 31: Eureka-style Speculative Task Parallelism


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-slides Homework 5


Homework 4

WS31-solution Wed



Apr 0504

Lecture 32:   Task Affinity with Places (start of Module 3)  Barrier Synchronization with PhasersModule 1: Section 3.4Topic 3.4 Lecture,  Topic 3.4 Demonstrationworksheet32lec32-slides


Homework 4 Checkpoint-1 

WS32-solution Fri



Apr 0706


Lecture 33: Message Passing Interface (MPI)


  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






Apr 1008


Lecture 34: Message Passing Interface (MPI, contd)


 Fuzzy Barriers with Phasers

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



Apr 12WS34-solution 



Apr 11

Lecture 35: GPU Computing Eureka-style Speculative Task Parallelism 



Homework 5  

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

Homework 4 (all) 

WS35-solution Fri
 WedApr 13Lecture 36: Scan Pattern. Parallel Prefix Sum 


worksheet36lec36-slides  WS36-solution 
 FriApr 15Lecture 37: Parallel Prefix Sum applications  worksheet37lec37-slides    
14MonApr 18Lecture 38: Overview of other models and frameworks   lec38-slides    
 WedApr 20Lecture 39: Course Review (Lectures 19-38)   lec38lec39-slides 

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

-MonApr 24Review session / Office Hours, 1pm - 3pm, location TBD   
-WedApr 26Review session / Office Hours, 1pm - 3pm, location TBD      
-FriApr 2822Review session / Office Hours, 1pm - 3pm, location TBDLecture 40: Course Review (Lectures 19-38)      lec40-slides 

April 26 - May 3

Scheduled final exam (Exam 2 – scope of exam limited to lectures 18-37), location and time TBD by registrar

Homework 5    



Lab Schedule

18TBD, lab2-slides Finish Accumulators and Loop-Level Parallelism, lab4-slides 22 — Exam 1-Isolated Statement and Atomic Variables 08 29 and Selectors 05Eureka-style Speculative Task 13 19lab13-handout

Lab #

Date (20172022)



Code Examples


Jan 11Async-Finish Parallel Programming with abstract metrics10

Infrastructure setup



, lab1-slides

2Jan 17Functional Programminglab2-handout 


Jan 25

DIY HJ-lib Programming, Futures


Java Streams

lab3-handout, lab3-slides

Feb 01

Jan 31Futureslab4-handout


Feb 08Loop Chunking and Barrier Synchronization07

Data-Driven Tasks

lab5-handout, lab5-slides  

Feb 15Data-Driven Futures and Phasers14

Async / Finish




No lab this week




Mar 01

Feb 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 



Loop Parallelism



Apr 12

Message Passing Interface (MPI)



No lab this week




Apache Spark


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 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

Labs must be submitted by the following Wednesday at 4:30pm.  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, 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 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.

 Graded homeworks will be returned to you via email, and exams as marked-up hardcopies. If you believe we have made an error in grading your homework or exam, please bring the matter to our

attention within one weekFor 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