COMP 322: Fundamentals of Parallel Programming (Spring

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2023)

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.

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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.

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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.

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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

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There are also a few optional 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:

• Fork-Join Parallelism with a Data-Structures Focus (FJP) by Dan Grossman (Chapter 7 in Topics in Parallel and Distributed Computing)
• Java Concurrency in Practice by Brian Goetz with Tim Peierls, Joshua Bloch, Joseph Bowbeer, David Holmes and Doug Lea
• Principles of Parallel Programming by Calvin Lin and Lawrence Snyder
• The Art of Multiprocessor Programming by Maurice Herlihy and Nir Shavit

Finally, here are some additional resources that may be helpful for you:

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Lecture Schedule

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Week	Day	Date (

2022 

2023)	Lecture	Assigned Reading	Assigned Videos (see Canvas site for video links)	In-class Worksheets	Slides	Work Assigned	Work Due

 

Worksheet Solutions
1	Mon	Jan

10Topic 1.1 Lecture, Topic 1.1 Demonstration

09	Lecture 1:

Task Creation and Termination (Async, Finish)Module 1: Section 1.1

Introduction

worksheet1

lec1-slides

 

 

   

			WS1-solution
	Wed	Jan

12

11	Lecture 2:

Computation Graphs, Ideal ParallelismModule 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 14Lecture 3: Abstract Performance Metrics, Multiprocessor SchedulingModule 1: Section 1.4Topic 1.4 Lecture, Topic 1.4 Demonstrationworksheet3lec3-slides

 

   

2

Mon

Jan 17

Lecture 4: Parallel Speedup and Amdahl's Law

Module 1: Section 1.5Topic 1.5 Lecture, Topic 1.5 Demonstrationworksheet4lec4-slidesQuiz for Unit 1   

 

Wed

Jan 19

Lecture 5: Future Tasks, Functional Parallelism ("Back to the Future")

Functional Programming			worksheet2	lec02-slides			WS2-solution
	Fri	Jan 13	Lecture 3: Higher order functions			worksheet3	lec3-slides			WS3-solution
2	Mon	Jan 16	No class: MLK
	Wed	Jan 18	Lecture 4: Lazy Computation			worksheet4	lec4-slides			WS4-solution
	Fri	Jan 20	Lecture 5: Java Streams			worksheet5	lec5-slides	Homework 1		WS5-solution
3	Mon	Jan 23	Lecture 6: Map Reduce with Java Streams	Module 1: Section 2.4	Topic 2.4 Lecture, Topic 2.4 Demonstration	worksheet6	lec6-slides			WS6-solution
	Wed	Jan 25	Lecture 7: Futures	Module 1: Section 2.1	Topic 2.1 Lecture , Topic 2.1 Demonstration

worksheet5

worksheet7

lec5 

lec7-slides

    

WS7-solution
	Fri	Jan

21

27

Lecture

6

8:  Async, Finish

Accumulators

, Computation Graphs

Module 1:

Section 2.3

Sections 1.1, 1.2

Topic

2

1.

3 Lecture

1 Lecture, Topic 1.1 Demonstration, Topic 1.2 Lecture, Topic 1.

3

2 Demonstration

worksheet6

worksheet8

lec63

lec8-slides

 Quiz for Unit 1  

WS8-solution
4	Mon	Jan

24

30

Lecture

7: Map Reduce

9: Ideal Parallelism, Data-Driven Tasks

Module 1: Section

2

1.3, 4.5

Topic

2

1.

4

3 Lecture, Topic

2

1.

4 Demonstration  worksheet7lec7-slides

 

   

 

Wed

Jan 26

Lecture 8: Data Races, Functional & Structural Determinism

3 Demonstration, Topic 4.5 Lecture, Topic 4.5 Demonstration	worksheet9	lec9-slides			WS9-solution
	Wed	Feb 01	Lecture 10: Event-based programming model			worksheet10	lec10-slides		Homework 1	WS10-solution
	Fri	Feb 03	Lecture 11: GUI programming as an example of event-based, futures/callbacks in GUI programming			worksheet11	lec11-slides	Homework 2		WS11-solution
5	Mon	Feb 06	Lecture 12: Scheduling/executing computation graphs Abstract performance metrics	Module 1: Section

2.5, 2.6

1.4

Topic

2

1.

5

4 Lecture , Topic

2

1.

5 Demonstration, Topic 2.6 Lecture, Topic 2.6 Demonstration   worksheet8lec8-slides

Homework 2

Homework 1  

 

Fri

Jan 28

Lecture 9: Java’s Fork/Join Library

 Topic 2.7 Lecture, Topic 2.7 Demonstration, Topic 2.8 Lecture, Topic 2.8 Demonstrationworksheet9lec9-slidesQuiz for Unit 2   

4

Mon

 

Jan 31          WedFeb 02          FriFeb 04         5

Mon

Feb 07

Lecture 10: Loop-Level Parallelism, Parallel Matrix MultiplicationModule 1: Sections 3.1, 3.2Topic 3.1 Lecture , Topic 3.1 Demonstration ,  Topic 3.2 Lecture,  Topic 3.2 Demonstrationworksheet10lec10-slides    

 

Wed

Feb 09

Lecture 11: Iteration Grouping (Chunking), Barrier Synchronization

Module 1: Sections 3.3, 3.4

Topic 3.3 Lecture , Topic 3.3 Demonstration, Topic 3.4 Lecture  ,   Topic 3.4 Demonstration

worksheet11lec11-slides    

 

Fri

Feb 11

 Lecture 12: Data-Driven Tasks 

 

Module 1: Sections 4.5

Topic 4.5 Lecture   Topic 4.5 Demonstration

worksheet12lec12-slides Quiz for Unit 2  6

Mon

Feb 14

 

        

 

Wed

Feb 16

Lecture 13: Parallelism in Java Streams, Parallel Prefix Sums 

Module 1: Sections 3.7Topic 3.7 Lecture , Topic 3.7 Demonstrationworksheet13lec13-slides

Homework 3 (includes one intermediate checkpoint)

 

Homework 2   FriFeb 18

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 Demonstrationworksheet14 lec14-slidesQuiz for Unit 3   

7

Mon

Feb 21

Lecture 15:  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 Demonstrationworksheet15lec15-slides    

 

Wed

Feb 23

Lecture 16: Midterm Review

   lec16-slides    

 

Fri

Feb 25 

Lecture 17: 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 Demonstrationworksheet17lec17-slides    

8

Mon

Feb 28

Lecture 18: Abstract vs. Real Performance

  worksheet18lec18-slides   Quiz for Unit 4Quiz for Unit 3  

 

Wed

Mar 02

Lecture 19: Critical Sections, Isolated construct (start of Module 2)

Module 2: Sections 5.1, 5.2, 5.6, Topic 5.1 Lecture, Topic 5.1 Demonstration, Topic 5.2 Lecture, Topic 5.2 Demonstration, Topic 5.6 Lecture, Topic 5.6 Demonstrationworksheet19lec19-slides    

 

Fri

Mar 04

Lecture 20: Parallel Spanning Tree algorithm, Atomic variables

Module 2: Sections 5.3, 5.4, 5.5Topic 5.3 Demonstration, Topic 5.4 Lecture, Topic 5.4 Demonstration, Topic 5.5 Lecture, Topic 5.5 Demonstrationworksheet20lec20-slides 

Quiz for Unit 4

  

9

Mon

Mar 07

Lecture 21: Actors

Module 2: 6.1, 6.2

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

worksheet21 lec21-slides Quiz for Unit 5

 

  

 

Wed

Mar 09

Lecture 22: Actors (contd)

Module 2: 6.3, 6.4Topic 6.3 Lecture, Topic 6.3 Demonstration, Topic 6.4 Lecture , Topic 6.4 Demonstrationworksheet22 lec22-slides 

Homework 3, Checkpoint-1

  

 

Fri

Mar 11

      

 

 

   

Mon

Mar 14

Lecture 23: Actors (contd)

Module 2: 6.5, 6.6Topic 6.5 Lecture, Topic 6.5 Demonstration, Topic 6.6 Lecture, Topic 6.6 Demonstrationworksheet23lec23-slides 

Quiz for Unit 5

   WedMar 16 Lecture 24: Java Threads, Java synchronized statementModule 2: 7.1, 7.2Topic 7.1 Lecture, Topic 7.2 Lectureworksheet24lec24-slides

Quiz for Unit 6

   

 

Fri

Mar 18

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

Module 2: 7.1, 7.2Topic 7.1 Lecture, Topic 7.2 Lectureworksheet25lec25-slides  

 

  

10

Mon

Mar 21

Lecture 26: Java Locks

Module 2: 7.3Topic 7.3 Lecture worksheet26lec26-slides Homework 4 (includes one intermediate checkpoint)Homework 3 (all)  

 

Wed

Mar 23

Lecture 27: Linearizability of Concurrent Objects 

Module 2: 7.4Topic 7.4 Lecture worksheet27lec27-slides

 

   

 

Fri

Mar 25

Lecture 28: Safety and Liveness Properties, Java Synchronizers, Dining Philosophers Problem

 Topic 7.5 Lecture, Topic 7.6 Lectureworksheet28lec28-slides

Quiz for Unit 7

 

 

  

11

Mon

Mar 28

Lecture 29: Message Passing Interface (MPI), (start of Module 3)

 Topic 8.1 Lecture, Topic 8.2 Lecture, Topic 8.3 Lectureworksheet29lec29-slides

 

Quiz for Unit 6

  

 

Wed

Mar 30

Lecture 30: Message Passing Interface (MPI, contd)

 Topic 8.4 Lectureworksheet30lec30-slides

 

   

 

Fri

Apr 01

Lecture 31: Message Passing Interface (MPI, contd)

  Topic 8.5 Lecture, Topic 8 Demonstration Videoworksheet31lec31-slidesQuiz for Unit 8

Quiz for Unit 7

  

12

Mon

Apr 04

Lecture 32: Task Affinity with Places

  worksheet32lec32-slides

 

Homework 4 Checkpoint-1

  

 

Wed

Apr 06

Lecture 33: Eureka-style Speculative Task Parallelism

  worksheet33

lec33-slides

 

   

 

Fri

Apr 08

Lecture 34: Algorithms based on Parallel Prefix (Scan) operations

  worksheet34lec34-slides 

Quiz for Unit 8

  

13

Mon

Apr 11

Lecture 35: Algorithms based on Parallel Prefix (Scan) operations cont.  worksheet35lec35-slides

 

 

   WedApr 13Lecture 36: Course Review (Lectures 19-33)   lec36-slides Homework 4 (all)   FriApr 15         14MonApr 18          WedApr 20

4 Demonstration	worksheet12	lec12-slides			WS12-solution
	Wed	Feb 08	Lecture 13: Parallel Speedup, Critical Path, Amdahl's Law	Module 1: Section 1.5	Topic 1.5 Lecture , Topic 1.5 Demonstration	worksheet13	lec13-slides			WS13-solution
	Fri	Feb 10	No class: Spring Recess
6	Mon	Feb 13	Lecture 14: Accumulation and reduction. Finish accumulators	Module 1: Section 2.3	Topic 2.3 Lecture   Topic 2.3 Demonstration	worksheet14	lec14-slides			WS14-solution
	Wed	Feb 15	Lecture 15: Recursive Task Parallelism			worksheet15	lec15-slides		Homework 2	WS15-solution
	Fri	Feb 17	Lecture 16: Data Races, Functional & Structural Determinism	Module 1: Sections 2.5, 2.6	Topic 2.5 Lecture ,  Topic 2.5 Demonstration,  Topic 2.6 Lecture,  Topic 2.6 Demonstration	worksheet16	lec16-slides	Homework 3		WS16-solution
7	Mon	Feb 20	Lecture 17: Midterm Review				lec17-slides
	Wed	Feb 22	Lecture 18: Limitations of Functional parallelism. Abstract vs. real performance. Cutoff Strategy			worksheet18	lec18-slides			WS18-solution
	Fri	Feb 24	Lecture 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,	worksheet19	lec19-slides			WS19-solution
8	Mon	Feb 27	Lecture 20: Confinement & Monitor Pattern. Critical sections Global lock	Module 2: Sections 5.1, 5.2, 5.6	Topic 5.1 Lecture, Topic 5.1 Demonstration, Topic 5.2 Lecture, Topic 5.2 Demonstration, Topic 5.6 Lecture, Topic 5.6 Demonstration	worksheet20	lec20-slides			WS20-solution
	Wed	Mar 01	Lecture 21:  Atomic variables, Synchronized statements	Module 2: Sections 5.4, 7.2	Topic 5.4 Lecture, Topic 5.4 Demonstration, Topic 7.2 Lecture	worksheet21	lec21-slides		Homework 3	WS21-solution
	Fri	Mar 03	Lecture 22: Parallel Spanning Tree, other graph algorithms			worksheet22	lec22-slides	Homework 4		WS22-solution
9	Mon	Mar 06	Lecture 23: Java Threads and Locks	Module 2: Sections 7.1, 7.3	Topic 7.1 Lecture, Topic 7.3 Lecture	worksheet23	lec23-slides			WS23-solution
	Wed	Mar 08	Lecture 24: Java Locks - Soundness and progress guarantees	Module 2: 7.5	Topic 7.5 Lecture	worksheet24	lec24-slides			WS24-solution
	Fri	Mar 10	Lecture 25: Dining Philosophers Problem	Module 2: 7.6	Topic 7.6 Lecture	worksheet25	lec25-slides			WS25-solution
	Mon	Mar 13	No class: Spring Break
	Wed	Mar 15	No class: Spring Break
	Fri	Mar 17	No class: Spring Break
10	Mon	Mar 20	Lecture 26: N-Body problem, applications and implementations			worksheet26	lec26-slides			WS26-solution
	Wed	Mar 22	Lecture 27: Read-Write Locks, Linearizability of Concurrent Objects	Module 2: 7.3, 7.4	Topic 7.3 Lecture, Topic 7.4 Lecture	worksheet27	lec27-slides			WS27-solution
	Fri	Mar 24	Lecture 28: Message-Passing programming model with Actors	Module 2: 6.1, 6.2	Topic 6.1 Lecture, Topic 6.1 Demonstration,   Topic 6.2 Lecture, Topic 6.2 Demonstration	worksheet28	lec28-slides			WS28-solution
11	Mon	Mar 27	Lecture 29: Active Object Pattern. Combining Actors with task parallelism	Module 2: 6.3, 6.4	Topic 6.3 Lecture, Topic 6.3 Demonstration,   Topic 6.4 Lecture, Topic 6.4 Demonstration	worksheet29	lec29-slides			WS29-solution
	Wed	Mar 29	Lecture 30: Task Affinity and locality. Memory hierarchy			worksheet30	lec30-slides		Homework 4	WS30-solution
	Fri	Mar 31	Lecture 31: Data-Parallel Programming model. Loop-Level Parallelism, Loop 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	worksheet31	lec31-slides	Homework 5		WS31-solution
12	Mon	Apr 03	Lecture 32: Barrier Synchronization with Phasers	Module 1: Section 3.4	Topic 3.4 Lecture,  Topic 3.4 Demonstration	worksheet32	lec32-slides			WS32-solution
	Wed	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	worksheet33	lec33-slides			WS33-solution
	Fri	Apr 07	Lecture 34: Fuzzy Barriers with Phasers	Module 1: Section 4.1	Topic 4.1 Lecture, Topic 4.1 Demonstration	worksheet34	lec34-slides			WS34-solution
13	Mon	Apr 10	Lecture 35: Eureka-style Speculative Task Parallelism			worksheet35	lec35-slides			WS35-solution
	Wed	Apr 12	Lecture 36: Scan Pattern. Parallel Prefix Sum			worksheet36	lec36-slides			WS36-solution
	Fri	Apr 14	Lecture 37: Parallel Prefix Sum applications			worksheet37	lec37-slides
14	Mon	Apr 17	Lecture 38: Overview of other models and frameworks				lec38-slides
	Wed	Apr 19	Lecture 39: Course Review (Lectures 19-38)

    

lec39-slides

    

Homework 5
	Fri	Apr

22

21	Lecture 40: Course Review (Lectures 19-38)

   

lec40-slides

    

Lab Schedule

Grading, Honor Code Policy, Processes and Procedures

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Labs must be submitted by the following Monday Wednesday at 114:59pm30pm.  Labs must be checked off by a TA.

Worksheets should be completed by the deadline listed in Canvas before the start of the following class (for full credit) so that solutions to the worksheets can be discussed in the next class.

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