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COMP 322: Fundamentals of Parallel Programming (Spring





3080Dr. Shams Imam


Prof. Vivek SarkarMackale Joyner, DH 3080

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:

Prasanth Chatarasi, Arghya Chatterjee, Yuhan Peng, Jonathan Sharman

Co-Instructor:Undergraduate TAs:

Prudhvi Boyapalli, Peter Elmers, Nicholas Hanson-Holtry, Ayush Narayan, Timothy Newton, Alitha Partono, Tom Roush, Hunter Tidwell, Bing Xue



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 office hours in Duncan Hall 3092 during 2pm - 3pm)

Lab locationlocations:

DH 1064 (Section A01), DH 1070 (Section A02)Keck 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 Owlspace are included below:

You are  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 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:

Lecture Schedule



lec2 22 Future Tasks, Functional ParallelismTopic 2.1 Lecture,  Topic 2.1 Demonstration 27 Finish Accumulators3   3    03 Java’s Fork/Join Library 05 Loop-Level Parallelism, Parallel Matrix Multiplication, Iteration Grouping (Chunking) 08 Barrier Synchronization 3 3 Data-Driven Tasks and Data-Driven Futures 45 , 45 Feb 15 Pipeline Parallelism, Signal Statement, Fuzzy Barriers 44 44 41 41 ,  Fri  Topic 6.4 Lecture, Topic 6.4 Demonstration,   Topic 6.5  Actors (contd)5 6 6 lec31lec32 35: PGAS languages 36: Memory Consistency Models lec36 37: GPU Computing 38: Fortress language 39lectures 2037, Last day of classes (automatic extension till April 29)



Date (20162022)


Assigned Reading

Assigned Videos (Quizzes due by Friday of each weeksee Canvas site for video links)

In-class Worksheets


Work Assigned

Work Due

Worksheet Solutions 



Jan 1110

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

Module 1: Section 1.1

Topic 1.1 Lecture, Topic 1.1 Demonstration Introduction









Jan 1312

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 Demonstrationworksheet2

Functional Programming worksheet2lec02-slides



 FriJan 1514Lecture 3: Abstract Performance Metrics, Multiprocessor SchedulingModule 1: Section 1.4Topic 1.4 Lecture, Topic 1.4 Demonstrationworksheet3lec3-slides

Homework 1

(2 weeks)

Lecture & demo quizzes for topics 1.1, 1.2, 1.3, 1.4 Higher order functions  worksheet3 lec3-slides   





Jan 1817

No lecture, School Holiday (Martin Luther King, Jr. Day)class: MLK




Jan 2019

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-slides Lecture & demo quizzes for topics 1.5, 2.1 (topic 1.6 is optional)Homework 1 WS5-solution 
3MonJan 2524

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-slides





Jan 2928

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

Homework 2

Homework 2 JARs (optional)

(2 weeks)

Homework 1, Lecture & demo quizzes for topics 2.2, 2.3, 2.5, 2.6  WS8-solution 



Feb 01


Jan 31 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



 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 Lecture & demo quizzes for topics 2.4, 3.1, 3.2, 3.3
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 



Feb 1009

Lecture 13: Iterative Averaging Revisited, SPMD pattern Parallel Speedup, Critical Path, Amdahl's Law

Module 1: Sections 3Section 1.5, 3.6



1.5 Lecture , Topic


1.5 Demonstration

, Topic 3.6 Lecture,  Topic 3.6 Demonstration  WS13-solution 



Feb 1211

No class: Spring Recess




Feb 14

Lecture 14:

Accumulation and reduction. Finish accumulators

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

Homework 3

(5 weeks, with two intermediate checkpoints)

Homework 2, Lecture & demo quizzes for topics 3.4, 3.5, 3.6, 4.5






Feb 16Topic 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  






Feb 17WS15-solution 
 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 3 Homework 2WS16-solution 



Feb 1921

Lecture 17: Abstract vs. Real Performance Midterm Review

  worksheet17 lec17-slides Lecture & demo quizzes for topics 4.1, 4.2, 4.3, 4.4



Feb 22   



Feb 23

Lecture 18: Midterm Summary

Limitations of Functional parallelism.
Abstract vs. real performance. Cutoff Strategy




Feb 24

Midterm Review (Interactice Q&A using PollEverywhere)

    Exam 1 held during lab time (7:00pm - 10:00pm)WS18-solution 



Feb 2625 

Lecture 19: Task Scheduling Policies Fork/Join programming model. OS Threads. Scheduler Pattern 

 Topic 42.6 7 Lecture,   Topic 42.6 Demonstration7 Demonstration, Topic 2.8 Lecture, Topic 2.8 Demonstration, worksheet19lec19-slidesLec19HelpFirstWorkStealing.javaHomework 3 Checkpoint-1, Lecture & demo quizzes for topic 4.6



Feb 29- Mar 04

Spring Break






07Feb 28

Lecture 20: Confinement & Monitor Pattern. Critical sections, Isolated construct, Parallel Spanning Tree algorithm
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 



Mar 0902

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-solution 



Mar 1104

Lecture 22:  Parallel computing using Java StreamsParallel Spanning Tree, other graph algorithms 

  worksheet22lec22-slides Homework 4

Homework 3 Checkpoint-2, Lecture & demo quizzes for topics 5.1 to 5.6




Mar 1407

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  





Mar 1609

Lecture 24:  Java synchronized statement (contd), advanced locking


Java Locks - Soundness and progress guarantees  

Module 2: 7.5Topic 7.3 5 Lecture worksheet24 lec24-slides 





Mar 1811Lecture

 Lecture 25: Concurrent Objects, Linearizability of Concurrent Objects  Dining Philosophers Problem  Module 2: 7.6Topic 7.4 6 Lectureworksheet25lec25-slides

Homework 4

(3 weeks, with one intermediate checkpoint)

Homework 3, Lecture quizzes for topics 7.1 - 7.4





Mar 14

No class: Spring Break



 WedMar 16No class: Spring Break    





Mar 18

No class: Spring Break






Mar 21

Lecture 26: Safety and Liveness Properties N-Body problem, applications and implementations 

 Topic 7.5 Lecture worksheet26lec26-slides   WS26-solution 



Mar 23

Topic 6.1 Lecture,  Topic 6.1 Demonstration,   Topic 6.2 Lecture, Topic 6.2 Demonstration, Topic 6.3 Lecture, Topic 6.3 Demonstration

Lecture 27: Actors


Read-Write Locks, Linearizability of Concurrent Objects

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





Mar 25


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 Demonstrationworksheet28lec28-slides


Lecture & demo quizzes for topics 7.5, 6.1 - 6.6






Mar 28

Topic 7.6 Lecture

Lecture 29: Dining Philosophers Problem


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






Mar 30

Lecture 30: Eureka-style Speculative Task Parallelism Task Affinity and locality. Memory hierarchy 






Apr 01

Midterm Recess

     Homework 4 Checkpoint-1, Lecture quiz for topic 7.6

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 04

Lecture 31: Task Affinity with Places  worksheet3132: Barrier Synchronization with PhasersModule 1: Section 3.4Topic 3.4 Lecture,  Topic 3.4 Demonstrationworksheet32lec32-slides






Apr 06

Lecture 32: Apache Spark framework for cluster computing


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






Apr 08

Lecture 33: Message Passing Interface (MPI)


Homework 5

(2 weeks, with 1-week automatic extension)

Homework 4 34: Fuzzy Barriers with Phasers

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





Apr 11

Lecture 34: Message Passing Interface (MPI, contd)35: Eureka-style Speculative Task Parallelism 





 WedApr 13Lecture 36: Scan Pattern. Parallel Prefix Sum 


worksheet35worksheet36lec35lec36-slides  WS36-solution 
 FriApr 15Lecture 37: Parallel Prefix Sum applications  worksheet36worksheet37lec37-slides    
14MonApr 18Lecture 38: Overview of other models and frameworks  worksheet-37 lec37lec38-slides    
 WedApr 20Lecture 39: Course Review (Lectures 19-38)   lec38lec39-slides    
 FriApr 22Lecture 40: Course Review (Lectures 19-38)   lec39lec40-slides Homework 5




Scheduled final exam




Lab Schedule

0  Setup 20Abstract performance metrics with async & finish, lab2-slides
lab_2.zipFinish Accumulators and Loop-Level Parallelism and lab4-slides  24 — Exam 1Isolated Statement and Atomic Variables 16 and Locks 30Eureka-style Speculative Task Parallelism 06Parallel Pretty Pictures13 20lab13-handout

Lab #

Date (20152022)



Code Examples


Jan 10






Jan 13

Async-Finish Parallel Programming


, lab1-slides

2Jan 17Functional Programminglab2-handout 


Jan 27

Futures and HJ-Viz 


Java Streams


Feb 03

Jan 31Futureslab4-handout


Feb 10Loop Chunking and Barrier Synchronization07

Data-Driven Tasks

lab5-handout and 

Feb 17Data-Driven Futures and Phasers14

Async / Finish




No lab this week



Mar 02

No lab this week — Spring Break



Mar 09


7Feb 28Recursive Task Cutoff Strategylab7-handout 
8Mar 07Java Threadslab8-handout 


Mar 14

No lab this week (Spring Break)

9Mar 23Actors and Selectors21Concurrent Listslab9-handout 
10Mar 28Actorslab10-handout 



Loop Parallelism



Apr 13




No lab this week




Message Passing Interface (MPI)


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), and class participation including worksheets, in-class Q&A, Piazza participation, and online quizzes (weighted 10% online quizzes (weighted 5% in all), and in-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. Please turn in all your homeworks using the subversion system set up for the class. 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.). If you use slip days, you must submit a SLIPDAY.txt file in your SVN homework folder before the actual submission deadline indicating the number of slip days that you plan to useSlip 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 exams.  All submitted homeworks are expected 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