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COMP 322: Fundamentals of Parallel Programming (Spring 2016)


Prof. Vivek Sarkar, DH 3080

Head TA:Max Grossman

Admin Assistant:

Annepha Hurlock,, DH 3080, 713-348-5186

Graduate TAs:

Prasanth Chatarasi, Arghya Chatterjee, Yuhan Peng, Jonathan Sharman

Co-Instructor: Dr. Shams Imam 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 210

Lecture times:

MWF 1:00pm - 1:50pm (followed by office hours in Duncan Hall 3092 during 2pm - 3pm)

Lab location:

DH 1064 (Section A01), DH 1070 (Section A02)

Lab times:

Wednesday, 07:00pm - 08:30pm

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.  


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 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.

There are also a few optional textbooks that we will draw from quite heavily.  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





Date (2016)


Assigned Reading

Assigned Videos (Quizzes due by Friday of each week)

In-class Worksheets


Work Assigned

Work Due



Jan 11

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

Module 1: Section 1.1

Topic 1.1 Lecture, Topic 1.1 Demonstration






Jan 13

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



 FriJan 15Lecture 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



Jan 18

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




Jan 20

Lecture 4:   Parallel Speedup and Amdahl's Law

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



Jan 22

Lecture 5: Future Tasks, Functional Parallelism

Module 1: Section 2.1Topic 2.1 Lecture ,   Topic 2.1 Demonstrationworksheet5lec5-slides Lecture & demo quizzes for topics 1.5, 2.1 (topic 1.6 is optional)



Jan 25

Lecture 6: Memoization

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

 WedJan 27

Lecture 7: Finish Accumulators

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



Jan 29

Lecture 8: 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   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



Feb 01

Lecture 9: Map Reduce

Module 1: Section 2.4Topic 2.4 Lecture  ,  Topic 2.4 Demonstration   worksheet9lec9-slides  



Feb 03

Lecture 10: Java’s Fork/Join LibraryFJP chapter: Sections 7.3 & 7.5 worksheet10lec10-slides




Feb 05

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

worksheet11lec11-slides Lecture & demo quizzes for topics 2.4, 3.1, 3.2, 3.3



Feb 08

Lecture 12:  Barrier Synchronization

Module 1: Section 3.4Topic 3.4 Lecture , Topic 3.4 Demonstration worksheet12 lec12-slides   



Feb 10

Lecture 13: 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    worksheet13 lec13-slides



Feb 12

Lecture 14:  Data-Driven Tasks and Data-Driven Futures

Module 1: Section 4.5Topic 4.5 Lecture ,   Topic 4.5 Demonstration worksheet14 lec14-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 15

Lecture 15: Phasers, Point-to-point Synchronization

Module 1: Sections 4.2, 4.3Topic 4.2 Lecture ,   Topic 4.2 Demonstration, Topic 4.3 Lecture,  Topic 4.3 Demonstration worksheet15 lec15-slides   



Feb 17

Lecture 16: 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, worksheet16 lec16-slides   



Feb 19

Lecture 17: Abstract vs. Real Performance

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



Feb 22

Lecture 18: Midterm Summary




Feb 24

Midterm Review (Interactice Q&A using PollEverywhere)

    Exam 1 held during lab time (7:00pm - 10:00pm), scope of exam limited to lectures 1-18 



Feb 26

Lecture 19: Task Scheduling Policies

 Topic 4.6 Lecture ,   Topic 4.6 Demonstration worksheet19 lec19-slides Lec19HelpFirstWorkStealing.javaHomework 3 Checkpoint-1, Lecture & demo quizzes for topic 4.6



Feb 29- Mar 04

Spring Break








Mar 07

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

Module 2: Sections 5.1, 5.2, 5.3, 5.6Topic 5.1 Lecture, Topic 5.1 Demonstration, Topic 5.2 Lecture, Topic 5.2 Demonstration, Topic 5.3 Lecture, Topic 5.3 Demonstration  worksheet20 lec20-slides  




Mar 09

Lecture 21: Atomic variables, Read-Write Isolation

Module 2: Sections 5.4, 5.5Topic 5.4 Lecture, Topic 5.4 Demonstration, Topic 5.5 Lecture, Topic 5.5 Demonstration, Topic 5.6 Lecture, Topic 5.6 Demonstration  worksheet21 lec21-slides  




Mar 11

Lecture 22:  Parallelism in Java Streams, Parallel Prefix Sums

   worksheet22 lec22-slides  

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



Mar 14

Lecture 23: Java Threads, Java synchronized statement

 Topic 7.1 Lecture, Topic 7.2 Lecture worksheet23 lec23-slides





Mar 16

Lecture 24: Java synchronized statement (contd), wait/notify

 Topic 7.3 Lecture worksheet24 lec24-slides





Mar 18

Lecture 25: Concurrent Objects, Linearizability of Concurrent Objects

  Topic 7.4 Lecture worksheet25 lec25-slides


Homework 3, Lecture quizzes for topics 7.1 - 7.4



Mar 21

Lecture 26: Linearizability (contd), Java locks

 Topic 7.3 Lecture (recap), Topic 7.4 Lecture (recap) worksheet26 lec26-slides

Homework 4

(3 weeks, with one intermediate checkpoint)




Mar 23

Lecture 27: Parallel Design Patterns, Safety and Liveness Properties  

  Topic 7.5 Lecture worksheet27 lec27-slides 




Mar 25

Lecture 28: Actors

 Topic 6.1 Lecture ,   Topic 6.1 Demonstration ,   Topic 6.2 Lecture, Topic 6.2 Demonstration, Topic 6.3 Lecture, Topic 6.3 Demonstration  worksheet28



Lecture & demo quizzes for topics 7.5



Mar 28

Lecture 29:  Actors (contd)

  Topic 6.4 Lecture , Topic 6.4 Demonstration ,   Topic 6.5 Lecture, Topic 6.5 Demonstration, Topic 6.6 Lecture, Topic 6.6 Demonstration worksheet29lec29-slides




Mar 30

Lecture 30: Java Synchronizers, Dining Philosophers Problem

 Topic 7.6 Lectureworksheet30 lec30-slides




Apr 01

Midterm Recess

     Lecture quiz for topic 7.6



Apr 04

Lecture 31: Eureka-style Speculative Task Parallelism


Homework 4 Checkpoint-1



Apr 06

Lecture 32:  Task Affinity with Places (start of Module 3)






Apr 08

Lecture 33: Message Passing Interface (MPI)






Apr 11

Lecture 34: Message Passing Interface (MPI, contd)


Homework 4
Lecture quiz for topics 6.1 - 6.6.



Apr 13

Lecture 35: GPU Computing


Homework 5 

(2 weeks, with automatic extension till May 2nd)




Apr 15

Lecture 36: Memory Consistency Models





Apr 18

Lecture 37: Partitioned Global Address Space (PGAS) programming models






Apr 20

Lecture 38: Survey of current Parallel Programming Models






Apr 22

Lecture 39: Course Review (lectures 20-37), Last day of classes

   lec39-slides Homework 5 (automatic extension till May 2nd)



May 3

Scheduled final exam in Herzstein Hall Auditorium, 9am - 12noon, May 3rd (Exam 2 – scope of exam limited to lectures 20-37)






Lab Schedule

Lab #

Date (2015)



Code Examples

0 Infrastructure Setuplab0-handout-


Jan 13

Async-Finish Parallel Programming

lab1-handout, lab1-slides


Jan 20

Abstract performance metrics with async & finish

lab2-handout, lab2-slides


Jan 27

DIY HJ-lib Programming, Futures, HJ-Viz 

lab3-handout, lab3-slides


Feb 03

Finish Accumulators and Loop-Level Parallelism

lab4-handout   and lab4-slides


Feb 10

Loop Chunking and Barrier Synchronization

lab5-handout and lab5-slides


Feb 17

Data-Driven Futures and Phasers



Feb 24

No lab this week — Exam 1



Mar 02

No lab this week — Spring Break



Mar 09

Isolated Statement and Atomic Variables



Mar 16

Java Threads


Mar 23

Java Locks



Mar 30

Actors and Selectors



Apr 06

Eureka-style Speculative Task Parallelism



Apr 13

Message Passing Interface (MPI)

13Apr 20Parallel Pretty Pictureslab13-handout 

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), and class participation including worksheets, in-class Q&A, Piazza participation, and online quizzes (weighted 10% in all).

The purpose of the homeworks is to train you to solve problems and to help deepen your understanding of concepts introduced in class. Homeworks are 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 late submissions (other than those using slip days mentioned below) will be accepted.

As in 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 use. 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.  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 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, so that solutions to the worksheets can be discussed.

You will be expected to follow the Honor Code in all homeworks and exams.  All submitted homeworks are expected to be the result of your individual effort. You are free to discuss course material and approaches to homework 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 (as shown here).  Exams 1 and 2 test your individual understanding and knowledge of the material. Exams are closed-book, and collaboration on exams is strictly forbidden. Finally, it is also your responsibility to protect your homeworks and exams from unauthorized access. 

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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