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  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

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

GList.java  

worksheet2

lec02-slides

 

 

WS2-solution 

WS2-solution
	Fri	Jan

14

13	Lecture 3: Higher order functions

  

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

 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

    

		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  worksheet11lec11-slidesHomework 2Homework 1  5

Mon

Feb 07

Lecture 12:

,

futures/callbacks in GUI programming

Scheduling/executing computation graphs

Abstract performance metrics

Module 1: Section 1.4

Topic 1.4 Lecture , Topic 1.4 Demonstration

worksheet12

worksheet11

lec12

lec11-slides

    

 

Wed

Feb 09

Homework 2		WS11-solution
5	Mon	Feb 06	Lecture 12: Abstract performance metrics, Parallel Speedup, Amdahl's Law

Lecture 13: Lightweight task parallelism. Finish/async

Module 1: Section 1.

1

5

Topic 1.

1

5 Lecture , Topic 1.

1

5 Demonstration

worksheet13

worksheet12

lec13

 

Fri

Feb 11

No class: Spring Recess

 

        

lec12-slides

    

		WS12-solution
	Wed	Feb 08	Lecture 13: Accumulation and reduction. Finish accumulators	Module 1: Section 2.3	Topic 2.3 Lecture   Topic 2.3 Demonstration	worksheet13	lec13-slides			WS13-solution
	Fri	Feb 10	No class: Spring Recess

6	Mon	Feb

14

13	Lecture 14:

Parallel Speedup, Critical Path, Amdah's Law

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
	Wed	Feb

16

15	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

17	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

Homework 2  

	WS16-solution
7	Mon	Feb

21 

20	Lecture 17: Midterm Review

   lec17-slides    

lec17-slides
	Wed	Feb

23   

22	Lecture 18:

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

Midterm Review

lec18-slides

    

 

	Fri	Feb

25 

24	Lecture 19:Data

Races, Functional & Structural Determinism

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

Module 1: Sections

2

3.

5

1, 3.2, 3.

6

3

Topic

2

3.

5 Lecture

1 Lecture, Topic 3.1 Demonstration , Topic 3.2 Lecture,  Topic 3.

5

2 Demonstration, Topic

2

3.

6

3 Lecture,

Topic 2

Topic 3.

6

3 Demonstration

worksheet19

lec19-slides

    

		WS19-solution
8	Mon	Feb

28

27	Lecture 20:

Confinement & Monitor Pattern. Critical sections
Global lock

Barrier Synchronization with Phasers

Module

2

1: Sections

5.1, 5.2, 5.6

3.4

Topic

5

3.

1

4 Lecture, Topic

5

3.

1 Demonstration, Topic 5.2 Lecture, Topic 5.2 Demonstration, Topic 5.6 Lecture, Topic 5.6 Demonstration

4 Demonstration

worksheet20

l ec20

lec20-slides

 

WS20-solution

   

	Wed	Mar 01

 

Wed

Mar 02

Lecture 21:

N-Body problem, applications and implementations

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	worksheet21	lec21-slides			WS21-solution
	Fri	Mar 03	Lecture 22: Fuzzy Barriers with Phasers	Module 1: Section 4.1	Topic 4.1 Lecture, Topic 4.1 Demonstration	worksheet22	lec22-slides			WS22-solution
9	Mon	Mar 06	Lecture 23:  Fork/Join programming model. OS Threads. Scheduler Pattern		Topic 2.7 Lecture, Topic 2.7 Demonstration, Topic 2.8 Lecture, Topic 2.8 Demonstration	worksheet23	lec23-slides		Homework 3 (CP 1)	WS23-solution
	Wed	Mar 08	Lecture 24: 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			WS24-solution
	Fri	Mar 10	Lecture 25:  Atomic variables, Synchronized statements	Module 2: Sections 5.4, 7.2	Topic 5.4 Lecture, Topic 5.4 Demonstration, Topic 7.2 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: Parallel Spanning Tree, other graph algorithms			worksheet26	lec26-slides			WS26-solution
	Wed	Mar 22	Lecture 27: Java Threads and Locks	Module 2: Sections 7.1, 7.3	Topic 7.1 Lecture, Topic 7.3 Lecture	worksheet27	lec27-slides		Homework 3 (CP 2)	WS27-solution
	Fri	Mar 24	Lecture 28: Java Locks - Soundness and progress guarantees	Module 2: Section 7.5	Topic 7.5 Lecture	worksheet28	lec28-slides			WS28-solution
11	Mon	Mar 27	Lecture 29:  Dining Philosophers Problem	Module 2: Section 7.6	Topic 7.6 Lecture	worksheet29	lec29-slides			WS29-solution
	Wed	Mar 29	Lecture 30: Read-Write Locks, Linearizability of Concurrent Objects	Module 2: Sections 7.3, 7.4	Topic 7.3 Lecture, Topic 7.4 Lecture	worksheet30	lec30-slides			WS30-solution
	Fri	Mar 31	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			WS31-solution
12	Mon	Apr 03	No class					Homework 4	Homework 3 (All)
	Wed	Apr 05	Lecture 32: 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			WS32-solution
	Fri	Apr 07	Lecture 33: Task Affinity and locality. Memory hierarchy			worksheet33	lec33-slides			WS33-solution
13	Mon	Apr 10	Lecture 34: Eureka-style Speculative Task Parallelism			worksheet34	lec34-slides			WS34-solution
	Wed	Apr 12	Lecture 35: Scan Pattern. Parallel Prefix Sum			worksheet35	lec35-slides		Homework 4 (CP 1)	WS35-solution
	Fri	Apr 14	Lecture 36: Parallel Prefix Sum applications			worksheet36	lec36-slides			WS36-solution
14	Mon	Apr 17	Lecture 37: Overview of other models and frameworks				lec37-slides
	Wed	Apr 19	Lecture 38: Course Review (Lectures 19-34)				lec38-slides		Homework 4 (All)
	Fri	Apr 21	Lecture 39: Course Review (Lectures 19-34)				lec39-slides

  worksheet21lec21-slides    

 

Fri

Mar 04

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,  worksheet22lec22-slidesHomework 4

Homework 3

  

9

Mon

Mar 07

Lecture 23:  Locks, Atomic variables

Module 2: 7.3

Topic 7.3 Lecture

worksheet23 lec23-slides  

 

  

 

Wed

Mar 09

Lecture 24: Parallel Spanning Tree, other graph algorithms

  worksheet24 lec24-slides 

 

  

 

Fri

Mar 11

 Lecture 25: Linearizability of Concurrent ObjectsModule 2: 7.4Topic 7.4 Lectureworksheet25lec25-slides 

 

   

Mon

Mar 14

No class: Spring Break

     

 

   WedMar 16No class: Spring Break    

 

   

 

Fri

Mar 18

No class: Spring Break

     

 

  

10

Mon

Mar 21

Lecture 26: Java Locks - Soundness and progress guarantees

Module 2: 7.5Topic 7.5 Lecture worksheet26lec26-slides     

 

Wed

Mar 23

Lecture 27: Dining Philosophers Problem

Module 2: 7.6Topic 7.4 Lecture Topic 7.6 Lecture worksheet27lec27-slides

 

   

 

Fri

Mar 25

Lecture 28: Read-Write Pattern. Read-Write Locks. Fairness & starvation

Module 2: 7.3, 7.5Topic 7.3 Lecture, Topic 7.5 Lecture, worksheet28lec28-slides

 

 

 

  

11

Mon

Mar 28

Lecture 29: Task Affinity and locality. Memory hierarchy

  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

Homework 4

  

12

Mon

Apr 04

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 Demonstrationworksheet32lec32-slides

 

 

  

 

Wed

Apr 06

Lecture 33: Barrier Synchronization with phasers

Module 1: Section 3.4

Topic 3.4 Lecture ,   Topic 3.4 Demonstration

worksheet33lec33-slides

 

   

 

Fri

Apr 08

Lecture 34:  Stencil computation. 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 Demonstrationworksheet34lec34-slides 

 

  

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

 

 

   WedApr 13Lecture 36: 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

worksheet36lec36-slides     FriApr 15Lecture 37: Eureka-style Speculative Task Parallelism  worksheet37lec37-slides    14MonApr 18Lecture 38: Overview of other models and frameworks   lec38-slides     WedApr 20Lecture 39: Course Review (Lectures 19-38)   lec39-slides     FriApr 22Lecture 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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