COMP 322: Fundamentals of Parallel Programming (Spring

...

2023)

...

Instructor:	Mackale Joyner, DH 2063	TAs:

Elian Ahmar, Timothy Goh, Kelly Park, Tucker Reinhardt, Mantej Singh, Minh Vu, Thanh Vu, Robert Walsh, Frederick Wang, Xincheng Wang, Yidi Wang  Admin Assistant:Annepha Hurlock, annepha@rice.edu, DH 3122, 713-348-5186  

Mohamed Abead, Chase Hartsell, Taha Hasan, Harrison Huang, Jerry Jiang, Jasmine Lee, Michelle Lee, Hung Nguyen, Quang Nguyen, Ryan Ramos, Oscar Reynozo, Delaney Schultz, Tina Wen, Raiyan Zannat, Kailin Zhang
Piazza site:	https://piazza.com/rice/

spring2021

spring2023/comp322 (Piazza is the preferred medium for all course communications)	Cross-listing:	ELEC 323
Lecture location:

Fully Online

Herzstein Amphitheater	Lecture times:	MWF 1:

30pm

00pm -

2

1:

25pm

50pm
Lab locations:

Fully Online

Mon (Herzstein Amp), Tue (Keck 100)	Lab times:

Tu 1

Mon  3:

30pm

00pm -

2

3:

25pm (TV, MS, TG, RW)Th 4:50pm - 5:45pm (XW, TR, KP, YW, FW

50pm (Raiyan, Oscar, Mohamed, Ryan, Michelle, Taha) Tue 4:00pm - 4:50pm (Tina, Delaney, Chase, Hung, Jerry, Kailin, Jasmine)

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 (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.

...

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.

...

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

...

There are 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:

...

Lecture Schedule

...

Week	Day	Date (

2021 

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

25Topic 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

 

 

  

slides

WS1-solution

 

	Wed	Jan

27

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

 

  

Functional Programming

worksheet2

lec02-slides

WS2-solution

 

	Fri	Jan

29

13	Lecture 3:

Abstract Performance Metrics, Multiprocessor SchedulingModule 1: Section 1.4Topic 1.4 Lecture, Topic 1.4 Demonstration

Higher order functions

worksheet3

worksheet3

lec3-

slides

 

   

2

Mon

slides			WS3-solution
2	Mon	Jan 16	No class: MLK
	Wed	Jan 18

Feb 01

Lecture 4:

Parallel Speedup and Amdahl's LawModule 1: Section 1.5Topic 1.5 Lecture, Topic 1.5 Demonstrationworksheet4lec4-slidesQuiz for Unit 1   

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

 

Wed

Feb 03

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

1: Section 2.

1

4

Topic 2.

1

4 Lecture, Topic 2.

1

4 Demonstration

worksheet5

worksheet6

lec5

lec6-slides

 

WS6-solution

   

 

Fri

Feb 05

	Wed	Jan 25	Lecture 7: Futures

Lecture 6:   Finish Accumulators

Module 1: Section 2.

3

1

Topic 2.

3

1 Lecture , Topic 2.

3

1 Demonstration

worksheet6

worksheet7

lec6

lec7-slides

 Quiz for Unit 1  

		WS7-solution
	Fri	Jan 27	Lecture 8:  Async, Finish, Computation Graphs

3MonFeb 08Lecture 7: Map Reduce

Module 1:

Section 2.4

Sections 1.1, 1.2

Topic

2

1.

4 Lecture

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

4

2 Demonstration

  worksheet7

worksheet8

lec7

lec8-slides

 

WS8-solution

 

4

 

Mon

 Lecture 8: Data Races, Functional & Structural Determinism

Jan 30

Wed

Feb 10

Lecture 9: Ideal Parallelism, Data-Driven Tasks

Module 1: Section

2

1.

5

3,

2

4.

6

5

Topic

2

1.

5

3 Lecture, Topic

2

1.

5

3 Demonstration, Topic

2

4.

6

5 Lecture, Topic

2

4.

6

5 Demonstration

   worksheet8

worksheet9

lec8

lec9-

slides

Homework 2

Homework 1  

slides			WS9-solution
	Wed	Feb 01	Lecture 10: Event-based programming model			worksheet10	lec10-slides		Homework 1	WS10-solution

 

	Fri	Feb

12

03

Lecture

9: Java’s Fork/Join Library

11: GUI programming, Scheduling/executing computation graphs

Module 1:

Sections 2.7, 2.8

Section 1.4

Topic

2

1.

7

4 Lecture , Topic

2

1.

8 Lecture

4 Demonstration

worksheet9

worksheet11

lec9

lec11-slides

Quiz for Unit

Homework 2

   

	WS11-solution
5

4

Mon

Feb

15

06

Lecture

10: Loop-Level Parallelism, Parallel Matrix Multiplication

12: Abstract performance metrics, Parallel Speedup, Amdahl's Law

Module 1:

Sections 3.

Section 1

, 3

.

2

5

Topic

3

1.

1

5 Lecture , Topic

3.

1

Demonstration ,  Topic 3.2 Lecture,  Topic 3.2

.5 Demonstration

worksheet10

worksheet12

lec10 

lec12-slides

    

WS12-solution
	Wed	Feb

17Spring "Sprinkle" Day (no class)        

 

Fri

Feb 19

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 13	Lecture 14: Data Races, Functional & Structural Determinism

Lecture 11: Iteration Grouping (Chunking), Barrier Synchronization

Module 1: Sections

3

2.

3

5,

3

2.

4

6

Topic

3

2.

3

5 Lecture ,  Topic

3

2.

3

5 Demonstration,  Topic

3

2.

4

6 Lecture

 

,  Topic

3

2.

4

6 Demonstration

worksheet11

worksheet14

lec11

lec14-slides

 Quiz for Unit 2  

5

Mon

Feb 22

Lecture 12:  Parallelism in Java Streams, Parallel Prefix Sums

Module 1: Section 3.7Topic Topic 3.7 Java Streams, Topic 3.7 Java Streams Demonstrationworksheet12lec12-slides     

Wed

Feb 24

Lecture 13: 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 Demonstrationworksheet13lec13-slides

Homework 3 (includes one intermediate checkpoint)

Quiz for Unit 3

Homework 2  

 

Fri

Feb 26

Lecture 14: Data-Driven Tasks 

Module 1: Sections 4.5Topic 4.5 Lecture   Topic 4.5 Demonstrationworksheet14 lec14-slides    6MonMar 01Spring "Sprinkle" Day (no class)        

 

Wed

Mar 03

		WS14-solution
	Wed	Feb 15	Lecture 15: Limitations of Functional parallelism. Abstract vs. real performance. Cutoff Strategy			worksheet15	lec15-slides		Homework 2	WS15-solution
	Fri	Feb 17	Lecture 16: Recursive Task Parallelism			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
	Fri	Feb 24	Lecture 19: 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	worksheet19	lec19-slides			WS19-solution
8	Mon	Feb 27	Lecture 20: Barrier Synchronization with Phasers	Module 1: Sections 3.4	Topic 3.4 Lecture, Topic 3.4 Demonstration	worksheet20	lec20-slides			WS20-solution
	Wed	Mar 01	Lecture 21:Stencil computation.

Lecture 15:  

Point-to-point Synchronization with Phasers

Module 1:

Section

Sections 4.2, 4.3

Topic 4.2 Lecture,

 

Topic 4.2 Demonstration, Topic 4.3 Lecture,

 Topic

Topic 4.3 Demonstration

worksheet15

worksheet21

lec15 

lec21-slides

    

WS21-solution
	Fri	Mar

05

03

Lecture

16: Pipeline Parallelism, Signal Statement,

22: Fuzzy Barriers with Phasers

Module 1:

Sections 4.4, 4

Section 4.1

Topic

Topic 4.

4 Lecture ,   Topic 4.4 Demonstration, Topic 4.1 Lecture,  Topic

1 Lecture, Topic 4.1 Demonstration

worksheet16

worksheet22

lec16

lec22-slides

Quiz for Unit 4Quiz for Unit 3  

		WS22-solution
9

7

	Mon	Mar

08

06

Lecture

17

23:

 Midterm Review   lec17-slides    

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

10

08

Lecture

18: Abstract vs. Real Performance  worksheet18 lec18-slides     

 

Fri

Mar 12

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

24: 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 Demonstrationworksheet19lec19-slides    

8

Mon

Mar 15

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 

Homework 3, Checkpoint-1

  

 

Wed

Mar 17

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  

 

  

 

Fri

Mar 19

Lecture 22: Actors (contd)

Module 2: 6.3, 6.4, 6.5Topic 6.3 Lecture, Topic 6.3 Demonstration, Topic 6.4 Lecture , Topic 6.4 Demonstration,   Topic 6.5 Lecture, Topic 6.5 Demonstration worksheet22 lec22-slides 

Quiz for Unit 4

  

9

Mon

Mar 22

Lecture 23: Actors (contd)

Module 2: 6.6Topic 6.6 Lecture, Topic 6.6 Demonstration lec23-slidesQuiz for Unit 5

 

 

   

Wed

Mar 24

Lecture 24: Java Threads, Java synchronized statement

Module 2: 7.1, 7.2Topic 7.1 Lecture, Topic 7.2 Lecture lec24-slides

 

 

    FriMar 26Spring "Sprinkle" Day (no class)        

10

Mon

Mar 29

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

Module 2: 7.1

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

7

5.

1 Lecture

4 Lecture, Topic 5.4 Demonstration, Topic 7.2

Lecture

Lecture

 

worksheet25

lec25-slides

WS25-solution

 

	Mon

 

 

Mar 13

No class: Spring Break

 

	Wed	Mar

31

15

Lecture 26: Java Threads (exercise)

   lec26-handout  Homework 3 (all)  

 

Fri

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

Apr 02

Lecture 27: Java Threads and Locks

Module 2: Sections 7.1, 7.3

Topic 7.1 Lecture, Topic 7.3 Lecture

 

worksheet27

lec27-slides

Quiz for Unit 6

Quiz for Unit 5  

11

Mon

Apr 05

Lecture 28: Linearizability of Concurrent Objects

	Homework 3 (CP 2)	WS27-solution
	Fri	Mar 24	Lecture 28: Java Locks - Soundness and progress guarantees	Module 2: Section 7.

4

5

Topic 7.

4

5 Lecture

 

worksheet28

lec28-slides

Homework 4 (includes one intermediate checkpoint)

 

 

  

 

Wed

		WS28-solution
11	Mon	Mar 27

Apr 07

Lecture 29:

  Java Locks (exercise)  

Dining Philosophers Problem

Module 2: Section 7.6

Topic 7.6 Lecture

worksheet29

 

lec29-

handout

slides

 

WS29-solution

 

  

 

Fri

	Wed	Mar 29

Apr 09

Lecture 30:

Safety and Liveness Properties, Java Synchronizers, Dining Philosophers Problem

Read-Write Locks, Linearizability of Concurrent Objects

Module 2: Sections 7.

5

3, 7.

6

4

Topic 7.

5

3 Lecture, Topic 7.

6 Lecture lec30-slides

Quiz for Unit 7

Quiz for Unit 6

  

4 Lecture	worksheet30	lec30-slides			WS30-solution
	Fri	Mar 31

12

Mon

Apr 12

Lecture 31: Message

Passing Interface (MPI), (start of Module 3)

-Passing programming model with Actors

Module 2: Sections 6.1, 6.2

Topic 6.1 Lecture, Topic 6.1 Demonstration,   Topic 6

 Topic 8.1 Lecture, Topic 8

.2 Lecture, Topic

8

6.

3 Lecture

2 Demonstration

 

worksheet31

lec31-slides

 

   

			WS31-solution
12	Mon	Apr 03	No class					Homework 4	Homework 3 (All)

 

	Wed	Apr

14

05	Lecture 32:

 Message Passing Interface (MPI, contd) Topic 8.4 Lecture  lec32-slides 

 

  

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

 

Fri

Apr 16

Lecture 33: Message Passing Interface (MPI, contd)

 Topic 8.5 Lecture, Topic 8 Demonstration Video lec33-slides

 

Homework 4 Checkpoint-1

  

13

Mon

Apr 19

Lecture 34: Task Affinity with Places

   

lec34-slides

  Quiz for Unit 8

Quiz for Unit 7  

 

Wed

Apr 21

Lecture 35

: Eureka-style Speculative Task Parallelism

   lec35-slides 

 

  

		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

 

Fri

Apr 24

Lecture 36: Algorithms based on Parallel Prefix (Scan) operations   lec36-slides

 

 

  

14

Mon

Apr 26

TBD

     

 

   WedApr 28

Lecture 38: Course Review (Lectures 19

-34)   lec38-slides Homework 4 (all)   FriApr 30TBD

-34)

    Quiz for Unit 8 

	lec38-slides		Homework 4 (All)
	Fri	Apr 21	Lecture 39: Course Review (Lectures 19-34)				lec39-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.

...