COMP 322: Fundamentals of Parallel Programming (Spring

...

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.

...

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.

...

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.

...

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)

...

• 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

...

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

 

 

  

 

			WS1-solution
	Wed	Jan

12

11	Lecture 2:  Functional Programming

  worksheet2

worksheet2

lec02

lec2 

-slides

 

 

  

		WS2-solution

Fri

Jan

14

13	Lecture 3: Higher order functions

 

worksheet3

worksheet3

lec3-slides

  

WS3-solution
2	Mon	Jan

17

16	No class: MLK

      

 

  

Wed

Jan

19

18	Lecture 4: Lazy Computation

  

worksheet4

lec4-slides

    

 

		WS4-solution
	Fri	Jan

21

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

 

   

WS6-solution
	Wed	Jan

26

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

lec8-slides

    

		WS8-solution
4	Mon

 

Jan

31

30

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

01	Lecture 10: Event-based programming model

 

  

worksheet10

lec10-slides

     

	Homework 1	WS10-solution
	Fri	Feb

04

03	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

06	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

08	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

10	No class: Spring Recess

 

6

 

Mon

 

Feb 13

Lecture 14: Recursive Task Parallelism

 

worksheet14

    6

Mon

Feb 14

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

lec14-slides			WS14-solution
	Wed	Feb 15	Lecture 15: 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

worksheet15

lec14

lec15-slides

    

 

Wed

Feb 16

Lecture 15: Recursive Task Parallelism 

  worksheet15lec15-slides

 

 

    FriFeb 18

Lecture 16: Accumulation and reduction. Finish accumulators

Module 1: Section 2.3Topic 2.3 Lecture , Topic 2.3 Demonstrationworksheet16 lec16-slidesHomework 3   

7

Mon

Feb 21

Lecture 17: Midterm Review

   lec17-slides    

 

Wed

Feb 23

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

   lec18-slides    

 

Fri

Feb 25 

Lecture 19: 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 Demonstrationworksheet19lec19-slides    

8

Mon

Feb 28

Lecture 20

	Homework 2	WS15-solution
	Fri	Feb 17	Lecture 16: Limitations of Functional parallelism. Abstract vs. real performance. Cutoff Strategy			worksheet16	lec16-slides	Homework 3		WS16-solution
7	Mon	Feb 20	Lecture 17: Midterm Review				lec17-slides
	Wed	Feb 22	Lecture 18: Midterm Review				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

5.6 Demonstration	worksheet20	lec20-slides			WS20-solution
	Wed	Mar 01	Lecture 21:  Atomic variables, Synchronized statements

worksheet20lec20-slides       

 

Wed

Mar 02

Lecture 21: N-Body problem, applications and implementations

  worksheet21lec21-slides    

 

Fri

Mar 04

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

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

 

  

	WS22-solution
9	Mon	Mar

07

06	Lecture 23:

 Locks, Atomic variables

Java Threads and Locks

Module 2: Sections 7.1,

Module 2:

 

7.3

Topic 7.1 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

lec24-slides

WS24-solution

  worksheet24 lec24-slides 

 

   

	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 13

 

Mon

Mar 14

No class: Spring Break

     

 

   WedMar 16

No class: Spring Break

 

 

	Wed

  

 

   

Mar 15	No class: Spring Break

Fri

Mar

18

17	No class: Spring Break

     

 

  

10	Mon	Mar

21

Lecture 26: Java Locks - Soundness and progress guarantees

Module 2: 7.5

20	Lecture 26: N-Body problem, applications and implementations

Topic 7.5 Lecture

 

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

 

   

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 Lecture, Topic 7.5 Lecture,

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

worksheet28

lec28-slides

 

 

 

 

WS28-solution

 

11	Mon	Mar

28

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		Homework 4	WS30-solution
	Fri	Mar 31	Lecture 31

  worksheet29lec29-slides

 

 

  

 

Wed

Mar 30

Lecture 30: Reactor Pattern. Web servers

  worksheet30lec30-slides

 

   

 

Fri

Apr 01

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

  worksheet31lec31-slidesHomework 5

 

  

12

Mon

Apr 04

Lecture 32

: 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

lec31-slides

 

 

  

Homework 5		WS31-solution
12	Mon	Apr 03	Lecture 32

 

Wed

Apr 06

Lecture 33

: Barrier Synchronization with

phasers

Phasers

Module 1: Section 3.4

Topic 3.4

Lecture 

Lecture,

   Topic

Topic 3.4 Demonstration

	worksheet32	lec32-slides			WS32-solution
	Wed	Apr 05	Lecture 33

worksheet33lec33-slides

 

   

 

Fri

Apr 08

Lecture 34

:  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

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

11

10	Lecture 35:

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

Eureka-style Speculative Task Parallelism

worksheet35

lec35-slides

 

 

   

		WS35-solution
	Wed	Apr

13

12	Lecture 36:

Active Object Pattern. Combining Actors with task parallelismModule 2: 6.3, 6.4Topic 6.3 Lecture ,   Topic 6.3 Demonstration ,   Topic 6.4 Lecture, Topic 6.4 Demonstration 

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

    

Lab Schedule

Grading, Honor Code Policy, Processes and Procedures

...

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.

...