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COMP 322: Fundamentals of Parallel Programming (Spring 2018)



Mackale Joyner, DH 2071

Head TA:Abbey Baker


Zoran Budimlić, DH 3081

Graduate TAs:

Jonathan Sharman

Admin Assistant:Annepha Hurlock,, DH 3122, 713-348-5186Undergraduate TAs:

Ashok Sankaran, Austin Bae, Avery Whitaker, Aydin Zanager, Eduard Danalache, Frank Chen, Harrison Brown, Jahid Adam, Jeemin Sim, Kitty Cai, Madison Lewis, Ryan Han, Teju Manchenella, Victor Gonzalez, Victoria Nazari

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:


Lecture times:

MWF 1:00pm - 1:50pm

Lab locations:


Lab times:

Thursday, 04:00pm - 05:00pm

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:

Past Offerings of COMP 322

  • Spring 2017 (Rice University)
  • Spring 2016 (Rice University)
  • Spring 2015 (Rice University)
  • Spring 2014 (Rice University)
  • Spring 2013 (Rice University)
  • Fall 2012 (Harvey Mudd College CS 181E, half-semester class, co-instructor: Prof. Ran Libeskind-Hadas)
  • Spring 2012 (Rice University)
  • Spring 2011 (Rice University)
  • Fall 2009 (Rice University)

Lecture Schedule




Date (2018)


Assigned Reading

Assigned Videos (see Canvas site for video links)

In-class Worksheets


Work Assigned

Work Due



Jan 08

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

Module 1: Section 1.1

Topic 1.1 Lecture, Topic 1.1 Demonstration






Jan 10

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 12Lecture 3: Abstract Performance Metrics, Multiprocessor SchedulingModule 1: Section 1.4Topic 1.4 Lecture, Topic 1.4 Demonstrationworksheet3lec3-slides





Jan 15

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




Jan 17

Lecture 4:   Parallel Speedup and Amdahl's Law

Module 1: Section 1.5Topic 1.5 Lecture, Topic 1.5 Demonstrationworksheet4lec4-slides  



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 22

Lecture 6: Memoization

Module 1: Section 2.2Topic 2.2 Lecture, Topic 2.2 Demonstrationworksheet6lec6-slides  
 WedJan 24

Lecture 7: Finish Accumulators

Module 1: Section 2.3Topic 2.3 Lecture, Topic 2.3 Demonstration  worksheet7lec7-slides

Homework 2

Homework 1



Jan 26

Lecture 8: Map Reduce

Module 1: Section 2.4Topic 2.4 Lecture, Topic 2.4 Demonstrationworksheet8lec8-slides


Quiz for Unit 1



Jan 29

Lecture 9: 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 Demonstration   worksheet9lec9-slides  



Jan 31

Lecture 10: Java’s Fork/Join LibraryModule 1: Sections 2.7, 2.8Topic 2.7 Lecture, Topic 2.8 Lecture,worksheet10lec10-slides  



Feb 02

Lecture 11: Loop-Level Parallelism, Parallel Matrix Multiplication, Iteration Grouping (Chunking)

Module 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




Feb 05

Lecture 12:  Barrier Synchronization

Module 1: Section 3.4Topic 3.4 Lecture , Topic 3.4 Demonstrationworksheet12 lec12-slides   


Feb 07

Lecture 13: Parallelism in Java Streams, Parallel Prefix Sums


  Homework 3 (includes two intermediate checkpoints)  

Homework 2



Feb 09

Spring Recess

     Quiz for Unit 2



Feb 12

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 Demonstration  worksheet14 lec14-slides   



Feb 14

Lecture 15:  Data-Driven Tasks, Point-to-Point Synchronization with Phasers

Module 1: Sections 4.5, 4.2, 4.3Topic 4.5 Lecture   Topic 4.5 Demonstration, Topic 4.2 Lecture ,   Topic 4.2 Demonstration, Topic 4.3 Lecture,  Topic 4.3 Demonstrationworksheet15 lec15-slides   



Feb 16

Lecture 16: Phasers Review

Module 1: Sections 4.2Topic 4.2 Lecture ,   Topic 4.2 Demonstrationworksheet16 lec16-slides  Quiz for Unit 3



Feb 19

Lecture 17: Midterm Summary




Feb 21

Midterm Review (interactive Q&A)

    Exam 1 held during lab time (7:00pm - 10:00pm), scope of exam limited to lectures 1-16  



Feb 23

Lecture 18: Abstract vs. Real Performance

  worksheet18 lec18-slides  Homework 3, Checkpoint-1



Feb 26

Lecture 19: 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 Demonstration,worksheet19 lec19-slides  




Feb 28

Lecture 20: Critical sections, Isolated construct, Parallel Spanning Tree algorithm, Atomic variables (start of Module 2)

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 Demonstration

worksheet20 lec20-slides  




Mar 02

Lecture 21:  Read-Write Isolation, Review of Phasers

Module 2: Section 5.5Topic 5.5 Lecture, Topic 5.5 Demonstrationworksheet21 lec21-slides  

Quiz for Unit 4



Mar 05

Lecture 22: Actors

Module 2: 6.1, 6.2Topic 6.1 Lecture ,   Topic 6.1 Demonstration ,   Topic 6.2 Lecture, Topic 6.2 Demonstrationworksheet22 lec22-slides






Mar 07

Lecture 23:  Actors (contd)

Module 2: 6.3, 6.4, 6.5, 6.6Topic 6.3 Lecture, Topic 6.3 Demonstration, Topic 6.4 Lecture , Topic 6.4 Demonstration,   Topic 6.5 Lecture, Topic 6.5 Demonstration, Topic 6.6 Lecture, Topic 6.6 Demonstrationworksheet23 lec23-slides


Homework 3, Checkpoint-2



Mar 09

Lecture 24: Java Threads, Java synchronized statement

Module 2: 7.1, 7.2Topic 7.1 Lecture, Topic 7.2 Lectureworksheet24 lec24-slides   Quiz for Unit 5


Mar 12 - Mar 16

Spring Break




Mar 19

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

Module 2: 7.2Topic 7.2 Lectureworksheet25 lec25-slides  





Mar 21

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)


Homework 3 (all)



Mar 23

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 6



Mar 26

Lecture 28: Message Passing Interface (MPI), (start of Module 3)

 Topic 8.1 Lecture, Topic 8.2 Lecture, Topic 8.3 Lecture,worksheet28





Mar 28

Lecture 29:  Message Passing Interface (MPI, contd)

 Topic 8.4 Lecture, Topic 8.5 Lecture, Topic 8 Demonstration Videoworksheet29 lec29-slides





Mar 30

Lecture 30: Distributed Map-Reduce using Hadoop and Spark frameworks

 Topic 9.1 Lecture (optional, overlaps with video 2.4), Topic 9.2 Lecture, Topic 9.3 Lectureworksheet30 lec30-slides  Quiz for Unit 7



Apr 02

Lecture 31: TF-IDF and PageRank Algorithms with Map-Reduce

 Topic 9.4 Lecture, Topic 9.5 Lecture, Unit 9 Demonstrationworksheet31 lec31-slides  




Apr 04

Lecture 32: Partitioned Global Address Space (PGAS) programming models

  worksheet32 lec32-slides


Homework 4 Checkpoint-1



Apr 06

Lecture 33: Combining Distribution and Multithreading

 Lectures 10.1 - 10.5, Unit 10 Demonstration (all videos optional – unit 10 has no quiz)worksheet33 lec33-slides


Quiz for Unit 8



Apr 09

Lecture 34: Task Affinity with Places

  worksheet34 lec34-slides  



Apr 11

Lecture 35: Eureka-style Speculative Task Parallelism



Homework 4 (all)



Apr 13

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



Homework 5  

Quiz for Unit 9



Apr 16

Lecture 37: Algorithms based on Parallel Prefix (Scan) operations, contd.





Apr 18

Lecture 38: GPU Computing  worksheet38lec38-slides





Apr 20

Lecture 39: Course Review (Lectures 18-38)


Homework 5


Lab Schedule

Lab #

Date (2017)



Code Examples

0 Infrastructure Setuplab0-handout-


Jan 11

Async-Finish Parallel Programming with abstract metrics

lab1-handout, lab1-slides


Jan 18

Futures and HJ-Viz 

lab2-handout, lab2-slides


Jan 25

Cutoff Strategy and Real World Performance

lab3-handout, lab3-slides


Feb 01

Java's ForkJoin Framework

lab4-handout, lab4-slides


Feb 08

Loop-level Parallelism

lab5-handout, lab5-slides


Feb 15




Feb 22

No lab this week — Exam 1



Mar 01

Isolated Statement and Atomic Variables

lab7-handout, lab7-slides 


Mar 08




Mar 15

No lab this week — Spring Break


Mar 22

Java Threads, Java Locks



Mar 29

No lab this week — Willy Week!



Apr 05

Message Passing Interface (MPI) 



Apr 12

Apache Spark

12Apr 19

Eureka-style Speculative Task Parallelism


Grading, Honor Code Policy, Processes and Procedures

Grading will be based on your performance on five homeworks (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 (weighted 5% in all).

The purpose of the homeworks is to give you practice in solving problems that deepen your understanding of concepts introduced in class. Homeworks are 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 guide. 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 checked off by a TA prior to the start of the lab the following week.

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, 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.  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).
  • Homeworks: All submitted homeworks are 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 closed-book, closed-notes, and closed-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 week.

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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