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:

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 (Concurrency)

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• 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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worksheet1 solution 

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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

10

09	Lecture 1: Introduction

 

 

worksheet1

lec1-

slides  

 

 

  

 

Wed

Jan 12

slides			WS1-solution
	Wed	Jan 11	Lecture 2:  Functional Programming

GList.java  

worksheet2

lec02-slides

 

 

  

WS2-solution
	Fri	Jan

14

13	Lecture 3: Higher order functions

 

worksheet3

worksheet3 

lec3-

slides 

slides

   

WS3-solution
2	Mon	Jan

17

16	No class: MLK

 

 

Wed

Jan 19

Lecture 4: Lazy Computation   worksheet4lec4-slides    

 

Fri

Jan 21

	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

24

23

Lecture 6: Map Reduce with Java Streams

Module 1: Section 2.4

Topic 2.4 Lecture, Topic 2.4 Demonstration

worksheet6

lec6-slides

 

 

Wed

Jan 26

WS6-solution

    

Wed

Jan 25

Lecture 7: Futures

Module 1: Section 2.1

Topic 2.1 Lecture , Topic 2.1 Demonstration

worksheet7

lec7-slides

 

   

WS7-solution
	Fri	Jan

28

27	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

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		WS8-solution
4	Mon

 

Jan

31

30	Lecture 9:

Data

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

    

WS9-solution
	Wed	Feb

02

01	Lecture 10: Event-based programming model

 

   

worksheet10

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Homework 1	WS10-solution
	Fri	Feb

04

03	Lecture 11: GUI programming as an example of event-based, futures/callbacks in GUI programming

   

worksheet11

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Homework 2

Homework 1 

WS11-solution
5	Mon	Feb

07 

06

Lecture 12: Scheduling/executing computation graphs Abstract performance metrics

Module 1: Section 1.4

Topic 1.4 Lecture , Topic 1.4 Demonstration

worksheet12

lec12-slides

    

WS12-solution
	Wed	Feb

09

08	Lecture 13:

Lightweight task parallelism. Finish/async

Parallel Speedup, Critical Path, Amdahl's Law

Module 1: Section 1.

1

5

Topic 1.

1

5 Lecture , Topic 1.

1

 

5 Demonstration

worksheet13

lec13-slides

   

WS13-solution
	Fri	Feb

11

10	No class: Spring Recess

 Feb 14

6

        6

Mon

Mon

Feb 13

Lecture 14:

Parallel Speedup, Critical Path, Amdah's Law

Accumulation and reduction. Finish accumulators

Module 1: Section

1

2.

5

3

Topic

1

2.

5

3 Lecture   Topic

1

2.

5

 

3 Demonstration

worksheet14

lec14-slides

    

WS14-solution
	Wed	Feb

16worksheet15

15	Lecture 15: Recursive Task Parallelism

   

worksheet15

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Homework 2	WS15-solution
	Fri	Feb

18

17	Lecture 16:

Accumulation and reduction. Finish accumulators

Data Races, Functional & Structural Determinism

Module 1:

Section

Sections 2.5, 2.

3

6

Topic 2.

3

5 Lecture ,  Topic 2.5 Demonstration,  Topic 2.6 Lecture,  Topic 2.

3  

6 Demonstration

worksheet16

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Homework 3

Homework 2 

WS16-solution
7	Mon	Feb

21

20	Lecture 17: Midterm Review

   

lec17-slides

     

	Wed	Feb

23

22	Lecture 18: Limitations of Functional parallelism. Abstract vs. real performance. Cutoff Strategy

 

worksheet18

   

lec18-slides

    

WS18-solution
	Fri	Feb

25 Module 1: Sections 2.5, 2.6

24	Lecture 19:

Data Races, Functional & Structural Determinism

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

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

l ec20

 

lec20-slides

    

WS20-solution
	Wed	Mar

02

01	Lecture 21:

N-Body problem, applications and implementations   worksheet21lec21-slides    

 

Fri

Mar 04

Lecture 22: Fork/Join programming model. OS Threads. Scheduler Pattern

Atomic variables, Synchronized statements

Module 2: Sections

2

5.4, 7

,

.2

.8

Topic

2

5.

7

4 Lecture, Topic

2

5.

7

4 Demonstration, Topic

2.8 Lecture, Topic 2.8 Demonstration, 

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

Homework 3

 

WS22-solution
9	Mon	Mar

07

06	Lecture 23:

 Locks, Atomic variables

Java Threads and Locks

Module 2: Sections 7.1, 7.3

Topic 7.

3 Lecture  

1 Lecture, Topic 7.3 Lecture

worksheet23

lec23-slides

 

 

  

WS23-solution
	Wed	Mar

09

08	Lecture 24:

Parallel Spanning Tree, other graph algorithms   

Java Locks - Soundness and progress guarantees

Module 2: 7.5

Topic 7.5 Lecture

worksheet24

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		WS24-solution
	Fri	Mar

11

10	Lecture 25:

Linearizability of Concurrent Objects

Dining Philosophers Problem

Module 2: 7.

4

6

Topic 7.

4  

6 Lecture

worksheet25

lec25-slides

 

 

  

WS25-solution
	Mon	Mar

14

13	No class: Spring Break

     

 

  

Wed

Mar

16

15	No class: Spring Break

   

 

 

 

   

Fri

Mar

1810

17	No class: Spring Break

     

 

  

10	Mon	Mar

21Module 2: 7.5Topic 7.5 Lecture

20	Lecture 26:

Java Locks - Soundness and progress guarantees

 

N-Body problem, applications and implementations

worksheet26

lec26-slides

    

WS26-solution
	Wed	Mar

23

22	Lecture 27:

Dining Philosophers Problem

Read-Write Locks, Linearizability of Concurrent Objects

Module 2: 7.3, 7.

6

4

Topic 7.

4

3 Lecture, Topic 7.

6  

4 Lecture

worksheet27

lec27-slides

 

   

WS27-solution
	Fri	Mar

25

24	Lecture 28:

Read-Write Pattern. Read-Write Locks. Fairness & starvation

Message-Passing programming model with Actors

Module 2:

7

6.

3

1,

7

6.

5

2

Topic

7

6.

3

1 Lecture, Topic

7.5 Lecture,  

6.1 Demonstration,   Topic 6.2 Lecture, Topic 6.2 Demonstration

worksheet28

lec28-slides

 

 

 

 

WS28-solution
11	Mon	Mar

28  worksheet29lec29-slides

 

 

  

 

Wed

Mar 30

Lecture 30: Reactor Pattern. Web servers

 

27	Lecture 29:

Task Affinity and locality. Memory hierarchy

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

 

   

 

Fri

Apr 01

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

  worksheet31lec31-slidesHomework 5

Homework 4

  

12

Mon

Apr 04

Lecture 32:

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

worksheet32

worksheet31

lec32

 

Wed

Apr 06

Lecture 33

lec31-slides

 

 

  

Homework 5		WS31-solution
12	Mon	Apr 03	Lecture 32: Barrier Synchronization with

phasers

Phasers

Module 1: Section 3.4

Topic 3.4

Lecture 

Lecture,

   Topic

Topic 3.4 Demonstration

worksheet33

worksheet32

lec33Lecture 34

lec32-slides

 

   

 

Fri

Apr 08

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

worksheet34

worksheet33

lec34

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WS33-solution

 

  

13

Mon

Apr 11

Lecture 35: Message-Passing programming model with ActorsModule 2: 6.1, 6.2Topic 6

	Fri	Apr 07	Lecture 34: Fuzzy Barriers with Phasers	Module 1: Section 4.1	Topic 4.1 Lecture,

 

Topic

6

4.1

Demonstration ,   Topic 6.2 Lecture, Topic 6.2

Demonstration

worksheet35

worksheet34

lec35

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WS34-solution

 

13

 

Mon

  WedApr 13Lecture 36: 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 Demonstration 

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

15 

14	Lecture 37:

Eureka-style Speculative Task Parallelism 

Parallel Prefix Sum applications

worksheet37

lec37-slides

    

14	Mon	Apr

18

17	Lecture 38: Overview of other models and frameworks

   

lec38-slides

     

	Wed	Apr

20

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

 Homework 5  

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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