COMP 322: Fundamentals of Parallel Programming (Spring

...

2024)

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:

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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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. 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  handout (Concurrency)

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:

• 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

Lecture Schedule

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Week	Day	Date (

2022 

2024)	Lecture	Assigned Reading	Assigned Videos (see Canvas site for video links)	In-class Worksheets	Slides	Work Assigned	Work Due

 

Worksheet Solutions
1	Mon	Jan

10

08	Lecture 1: Introduction

 

 

worksheet1

lec1-slides

 

WS1-solution

 

	Wed

  

Jan 10

 

 

Wed

Jan 12

Lecture 2:  Functional Programming

 

Lecture 2:  Functional Programming

worksheet2

lec2

lec02-slides

 

 

   

		WS2-solution
	Fri	Jan

14

12	Lecture 3: Higher order functions

 

worksheet3

worksheet3

lec3-slides

 

 

		WS3-solution
2	Mon	Jan

17

15	No class: MLK

 

	Wed

       

Jan 17

Lecture 4: Lazy Computation

worksheet4

lec4-slides

WS4-solution

 

Wed

Jan 19

Lecture 4: Lazy Computation  worksheet4lec4-slides     

	Fri	Jan

21

19	Lecture 5: Java Streams

   

worksheet5

lec5-slides

Homework 1

  

WS5-solution
3	Mon	Jan

24

22

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 24

 

Wed

Jan 26

Lecture 7: Futures

Module 1: Section 2.1

Topic 2.1 Lecture , Topic 2.1 Demonstration

worksheet7

lec7-slides

 

   

 

		WS7-solution
	Fri	Jan

28

26	Lecture 8:  Async, Finish, Computation Graphs

, Ideal Parallelism

Module 1: Sections 1.

2

1, 1.

3

2

Topic 1.

2

1 Lecture, Topic 1.

2

1 Demonstration, Topic 1.

3

2 Lecture, Topic 1.

3

2 Demonstration

worksheet8

lec8

lec8-slides

    

		WS8-solution
4	Mon

 

Jan

31

29

Lecture 9: Ideal Parallelism, Data-Driven Tasks

Module 1: Section 1.3, 4.5

 

Topic 1.3 Lecture, Topic 1.3 Demonstration, Topic 4.5 Lecture

 

, Topic 4.5 Demonstration

worksheet9

lec9-

slides  

slides

   

WS9-solution
	Wed

Feb 02

Jan 31

Lecture 10: Event-based programming model

 

  

worksheet10

lec10-slides

     

	Homework 1	WS10-solution
	Fri	Feb

04

02	Lecture 11: GUI programming

as an example of event-based,
futures/callbacks in GUI programming

, Scheduling/executing computation graphs

Module 1: Section 1.4

Topic 1.4 Lecture , Topic 1.4 Demonstration

   

worksheet11

lec11-slides

Homework 2

  

WS11-solution
5	Mon	Feb

07

05	Lecture 12:

Scheduling/executing computation graphs

Abstract performance metrics, Parallel Speedup, Amdahl's Law

Module 1: Section 1.

4

5

Topic 1.

4

5 Lecture , Topic 1.

4

5 Demonstration

worksheet12

lec12-slides

    

 

		WS12-solution
	Wed	Feb

09

07	Lecture 13:

Lightweight task parallelism

Accumulation and reduction. Finish

/async

accumulators

Module 1: Section

1

2.

1

3

Topic

1

2.

1

3 Lecture

,

Topic

1

2.

1

 

3 Demonstration

worksheet13

lec13-slides

   

	WS13-solution

Fri

Feb

11

09	No class: Spring Recess

 

6

 

Mon

       6

Mon

Feb 14

Lecture 14: Parallel Speedup, Critical Path, Amdah's Law

Feb 12

Lecture 14: Data Races, Functional & Structural Determinism

Module 1:

Section 1

Sections 2.5, 2.6

Topic

1

2.5 Lecture ,  Topic

1

2.5 Demonstration

,  Topic 2.6 Lecture,  Topic 2.6 Demonstration

worksheet14

lec14-slides

WS14-solution

worksheet14lec14-slides     

	Wed	Feb

16

14	Lecture 15:

Recursive Task Parallelism   

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

worksheet15

lec15-slides

 

 

    

	Homework 2	WS15-solution
	Fri	Feb

18

16	Lecture 16:

Accumulation and reduction. Finish accumulatorsModule 1: Section 2.3Topic 2.3 Lecture , Topic 2.3 Demonstration 

Recursive Task Parallelism

worksheet16

lec16-slides

Homework 3

  

WS16-solution
7	Mon	Feb

21

19	Lecture 17: Midterm Review

   

lec17-slides

    

 

	Wed	Feb

23   lec18-slides    

 

21	Lecture 18:

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

Midterm Review				lec18-slides

Fri

Feb

25 

23	Lecture 19:

Data Races, Functional & Structural DeterminismModule 1: Sections 2.5, 2.6

Fork/Join programming model. OS Threads. Scheduler Pattern

Topic 2.

5

7 Lecture, Topic 2.

5

7 Demonstration, Topic 2.

6

8 Lecture, Topic 2.

6  

8 Demonstration

worksheet19

lec19-slides

   

WS19-solution
8	Mon	Feb

28

26	Lecture 20:

Confinement & Monitor Pattern. Critical sections
Global lockModule 2: Sections 5.1, 5.2, 5.6 Topic 5

Data-Parallel Programming model. Loop-Level Parallelism, Loop Chunking

Module 1: Sections 3.1, 3.2, 3.3

Topic 3.1 Lecture, Topic

5

3.1 Demonstration , Topic

5

3.2 Lecture,

Topic 5

Topic 3.2 Demonstration, Topic

5

3.

6

3 Lecture,

Topic 5

Topic 3.

6

3 Demonstration

worksheet20

lec20

lec20-slides

      

		WS20-solution
	Wed	Feb 28

 

Wed

Mar 02  worksheet21

Lecture 21:

N-Body problem, applications and implementations

Barrier Synchronization with Phasers

Module 1: Sections 3.4

Topic 3.4 Lecture, Topic 3.4 Demonstration

worksheet21

lec21-slides

    

 

		WS21-solution
	Fri	Mar

04

01	Lecture 22:

Fork/Join programming model. OS Threads. Scheduler Pattern Module 2: Sections 2.7, 2.8Topic 2.7 Lecture, Topic 2.7 Demonstration, Topic 2.8 Lecture, Topic 2.8 Demonstration,  

Stencil computation. Point-to-point Synchronization with Phasers

Module 1: Sections 4.2, 4.3

Topic 4.2 Lecture, Topic 4.2 Demonstration, Topic 4.3 Lecture, Topic 4.3 Demonstration

worksheet22

lec22-slides

Homework 4

 

 

WS22-solution
9	Mon	Mar

07

04	Lecture 23:

 Locks, Atomic variables

Fuzzy Barriers with Phasers

Module

2

1:

7

Section 4.

3

1	Topic 4.1 Lecture, Topic 4.1 Demonstration

Topic 7.3 Lecture

 

worksheet23

lec23-slides

 

 

  

	Homework 3 (CP 1)	WS23-solution

Wed

Mar

09  

06	Lecture 24:

Parallel Spanning Tree, other graph algorithms

Confinement & Monitor Pattern. Critical sections Global lock

Module 2: Sections 5.1, 5.2

Topic 5.1 Lecture, Topic 5.1 Demonstration, Topic 5.2 Lecture, Topic 5.2 Demonstration, Topic 5.6 Lecture, Topic 5.6 Demonstration

worksheet24

lec24-slides

 

 

  

 

Fri

		WS24-solution
	Fri	Mar 08

Mar 11

Lecture 25:

Linearizability of Concurrent Objects

Atomic variables, Synchronized statements

Module 2: Sections 5.4, 7.

4

2

Topic

7

5.4 Lecture, Topic 5.4 Demonstration, Topic 7.2 Lecture

worksheet25

lec25 

lec25-slides

 

 

  

		WS25-solution

Mon

Mar

14 WedMar 16

11

No class: Spring Break

    

 

  

	Wed	Mar 13

No class: Spring Break

    

 

    

	Fri	Mar

18

15	No class: Spring Break

     

 

  

10	Mon	Mar

21

18	Lecture 26: Java

Locks - Soundness and progress guarantees

Threads and Locks

Module 2: Sections 7.1, 7.

5

3

Topic 7.

5  

1 Lecture, Topic 7.3 Lecture

worksheet26

lec26-slides

    

WS26-solution
	Wed	Mar

23

20	Lecture 27:

Dining Philosophers Problem

Read-Write Locks,  Soundness and progress guarantees

Module 2: Section 7.

6

3

Topic 7.

4

3 Lecture, Topic 7.

6

5 Lecture

worksheet27

lec27-slides

 

   

Homework 3 (CP 2)

WS27-solution

 

	Fri	Mar

25

22	Lecture 28:

Read-Write Pattern. Read-Write Locks. Fairness & starvationModule 2: 7.3, 7.5

Dining Philosophers Problem

Topic 7.

3 Lecture, Topic 7.5 Lecture,

6 Lecture

worksheet28

lec28-slides

 

 

 

  

		WS28-solution
11	Mon	Mar

28

25	Lecture 29:

Task Affinity and locality. Memory hierarchy

Linearizability of Concurrent Objects

Module 2: Sections 7.4

Topic 7.4 Lecture

   

worksheet29

lec29-slides

 

 

  

WS29-solution
	Wed	Mar

30

27	Lecture 30:

Reactor Pattern. Web servers 

Parallel Spanning Tree, other graph algorithms

worksheet30

lec30-slides

 

WS30-solution

 

	Fri

  

 

Fri

Apr 01

Lecture 31: Scan Pattern. Parallel Prefix Sum, uses and algorithms

   

Mar 29

Lecture 31: Message-Passing programming model with Actors

Module 2: Sections 6.1, 6.2

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

worksheet31

lec31-slides

Homework 5

 

 

WS31-solution
12	Mon	Apr

04

01	Lecture 32:

Data-Parallel Programming model. Loop-Level Parallelism, Loop ChunkingModule 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 Demonstration

 

Active Object Pattern. Combining Actors with task parallelism

Module 2: Sections 6.3, 6.4

Topic 6.3 Lecture, Topic 6.3 Demonstration,   Topic 6.4 Lecture, Topic 6.4 Demonstration

worksheet32

lec32-slides

 

 

  

Homework 4	Homework 3 (All)	WS32-solution

Wed

Apr

06

03	Lecture 33:

Barrier Synchronization with phasersModule 1: Section 3.4

Topic 3.4 Lecture ,   Topic 3.4 Demonstration

 

Task Affinity and locality. Memory hierarchy

worksheet33

lec33-slides

 

   

WS33-solution
	Fri	Apr

08

05	Lecture 34:

  Stencil computation. Point-to-point Synchronization with PhasersModule 1: Section 4.2, 4.3Topic 4.2 Lecture ,   Topic 4.2 Demonstration, Topic 4.3 Lecture,  Topic 4.3 Demonstration

Eureka-style Speculative Task Parallelism

worksheet34

lec34-slides

 

WS34-solution

 

13

 

Mon

 

Apr 08	No class: Solar Eclipse
	Wed	Apr 10

13

Mon

Apr 11

Lecture 35:

Message-Passing programming model with ActorsModule 2: 6.1, 6.2

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

worksheet35lec35-slides

 

 

  

Scan Pattern. Parallel Prefix Sum			worksheet35	lec35-slides		Homework 4 (CP 1)	WS35-solution
	Fri	Apr 12	Lecture 36: Parallel Prefix Sum applications			worksheet36	lec36-slides			WS36-solution

 WedApr 13Lecture 36: Active Object Pattern. Combining Actors with task parallelismModule 2: 6.3, 6.4

Topic 6.3 Lecture ,   Topic 6.3 Demonstration ,   Topic 6.4 Lecture, Topic 6.4 Demonstration

worksheet36lec36-slides     FriApr 15Lecture 37: Eureka-style Speculative Task Parallelism  worksheet37lec37-slides    

14	Mon	Apr

18

15

Lecture

38lec38

37: Overview of other models and frameworks

   

lec37-slides

     

	Wed	Apr

20

17

Lecture

39

38: Course Review (Lectures 19-

38lec39

34)

  

lec38-slides

     

	Homework 4 (All)
	Fri	Apr

22

19

Lecture

40

39: Course Review (Lectures 19-

38

34)

   lec40

lec39-slides

    

Lab Schedule

Grading, Honor Code Policy, Processes and Procedures

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Labs must be submitted by the following Monday at 11:59pm3pm.  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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