COMP 322: Fundamentals of Parallel Programming (Spring

...

2023)

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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, EA

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.

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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.

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• Module 1 handout (Parallelism)
• Module 2 handout (Concurrency)

There

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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:

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Lecture Schedule

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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

 

 

  

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

 

   FriJan 29Lecture 3: Abstract Performance Metrics, Multiprocessor SchedulingModule 1: Section 1.4Topic 1.4 Lecture, Topic 1.4 Demonstrationworksheet3lec3-slides

 

   

2

Mon

Feb 01

Lecture 4: Parallel Speedup and Amdahl's Law

Module 1: Section 1.5Topic 1.5 Lecture, Topic 1.5 Demonstrationworksheet4lec4-slidesQuiz for Unit 1   

 

Wed

Feb 03

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

Functional Programming			worksheet2	lec02-slides			WS2-solution
	Fri	Jan 13	Lecture 3: Higher order functions			worksheet3	lec3-slides			WS3-solution
2	Mon	Jan 16	No class: MLK
	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 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 25	Lecture 7: Futures

Module 1: Section 2.1

Topic 2.1 Lecture , Topic 2.1 Demonstration

worksheet5

worksheet7

lec5 

lec7-slides

    

WS7-solution
	Fri

Feb 05

Jan 27

Lecture

6

8:  Async, Finish

Accumulators

, Computation Graphs

Module 1:

Section 2.3

Sections 1.1, 1.2

Topic

2

1.

3 Lecture

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

3

2 Demonstration

worksheet6

worksheet8

lec6

lec8-slides

 Quiz for Unit 1  

		WS8-solution
4

3

	Mon

Feb 08

Jan 30

Lecture

7: Map Reduce

9: Ideal Parallelism, Data-Driven Tasks

Module 1: Section

2

1.3, 4.5

Topic

2

1.

4

3 Lecture, Topic

2.4 Demonstration  worksheet7lec7-slides

 

   

 

Wed

Feb 10

Lecture 8: Data Races, Functional & Structural Determinism

Module 1: Section 2.5, 2.6Topic 2.5 Lecture, Topic 2.5 Demonstration, Topic 2.6 Lecture, Topic 2.6 Demonstration   worksheet8

1.3 Demonstration, Topic 4.5 Lecture, Topic 4.5 Demonstration	worksheet9	lec9-slides			WS9-solution
	Wed	Feb 01	Lecture 10: Event-based programming model			worksheet10	lec10-slides		Homework 1	WS10-solution
	Fri	Feb 03	Lecture 11: GUI programming, Scheduling/executing computation graphs	Module 1: Section 1.4	Topic 1.4 Lecture , Topic 1.4 Demonstration	worksheet11	lec11

lec8

-slides

Homework 2

Homework 1  

	WS11-solution
5	Mon	Feb 06	Lecture 12: Abstract performance metrics, Parallel Speedup, Amdahl's Law	Module 1: Section 1.5	Topic 1.5 Lecture , Topic 1.5 Demonstration	worksheet12	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 13	Lecture 14: Data Races, Functional & Structural Determinism	Module 1: Sections 2.5, 2.6	Topic 2.5 Lecture ,  Topic 2.5 Demonstration,  Topic 2.6 Lecture,  Topic 2.6 Demonstration	worksheet14	lec14-slides			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

 

Fri

Feb 12

Lecture 9: Java’s Fork/Join Library

 Topic 2.7 Lecture, Topic 2.7 Demonstration, Topic 2.8 Lecture, Topic 2.8 Demonstrationworksheet9lec9-slidesQuiz for Unit 2   

4

Mon

 

Feb 15No class (weather)         WedFeb 17Spring "Sprinkle" Day (no class)         FriFeb 19No class (weather)        5

Mon

Feb 22

Lecture 10: Loop-Level Parallelism, Parallel Matrix Multiplication

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.

2

3 Demonstration

worksheet10

worksheet19

lec10

lec19-slides

 

WS19-solution

   

8	Mon

 

Wed

Feb

24

27

Lecture

11: Iteration Grouping (Chunking),

20: Barrier Synchronization with Phasers

Module 1: Sections 3.

3, 3.4Topic 3.3 Lecture , Topic 3.3 Demonstration,

4

Topic 3.4 Lecture

 

,

 

Topic 3.4 Demonstration

worksheet11

worksheet20

lec11

lec20-slides

 

WS20-solution

 

	Wed

Fri

Feb 26

Mar 01

Lecture 21:Stencil computation. Point-to-point Synchronization with Phasers

 Lecture 12: Data-Driven Tasks 

 

Module 1: Sections 4.2, 4.

5

3	Topic 4.2 Lecture, Topic 4.2 Demonstration,

Topic 4.

5

3 Lecture

 

, Topic 4.

5

3 Demonstration

worksheet12

worksheet21

lec12

lec21-slides

 Quiz for Unit 2  6

Mon

Mar 01

Spring "Sprinkle" Day (no class)

        

 

Wed

Mar 03

Lecture 13: Parallelism in Java Streams, Parallel Prefix Sums 

Module 1: Sections 3.7Topic 3.7 Lecture , Topic 3.7 Demonstrationworksheet13

		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

lec13

-slides

Homework 3 (

includes one intermediate checkpoint

CP 1)

 

Homework 2  

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

 FriMar 05

Lecture 14: 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 Demonstrationworksheet14 lec14-slidesQuiz for Unit 3   

7

Mon

Mar 08

Lecture 15:  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 Demonstrationworksheet15lec15-slides    

	Wed	Mar

10

15

Lecture 16: Midterm Review

   lec16-slides    

 

Fri

Mar 12 

Lecture 17: Pipeline Parallelism, Signal Statement, Fuzzy Barriers

Module 1: Sections 4.4, 4.1Topic 4.4 Lecture ,   Topic 4.4 Demonstration, Topic 4.1 Lecture,  Topic 4.1 Demonstrationworksheet17lec17-slides    

8

Mon

Mar 15

Lecture 18: Abstract vs. Real Performance

  worksheet18lec18-slides   Quiz for Unit 4Quiz for Unit 3  

 

Wed

Mar 17

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

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    

 

Fri

Mar 19

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 

Quiz for Unit 4

  

9

Mon

Mar 22

Lecture 21: Actors

Module 2:

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: Task Affinity and locality. Memory hierarchy			worksheet30	lec30-slides			WS30-solution
	Fri	Mar 31	Lecture 31: Read-Write Locks, Linearizability of Concurrent Objects	Module 2: Sections 7.3, 7.4	Topic 7.3 Lecture, Topic 7.4 Lecture	worksheet31	lec31-slides	Homework 4		WS31-solution
12	Mon	Apr 03	Lecture 32: 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

worksheet21

worksheet32

lec21

lec32-slides

Quiz for Unit 5

 

  

Homework 3 (All)

WS32-solution

 

	Wed

Mar 24

Apr 05

Lecture

22: Actors (contd)

33:  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 Demonstrationworksheet22 lec22-slides 

Homework 3, Checkpoint-1

  

 

Fri

Mar 26

Spring "Sprinkle" Day (no class)

 

     

 

 

  10

Mon

Mar 29

Lecture 23: Actors (contd)

Module 2: 6.5, 6.6Topic 6.5 Lecture, Topic 6.5 Demonstration, Topic 6.6 Lecture, Topic 6.6 Demonstrationworksheet23lec23-slides 

Quiz for Unit 5

   WedMar 31 Lecture 24: Java Threads, Java synchronized statementModule 2: 7.1, 7.2Topic 7.1 Lecture, Topic 7.2 Lectureworksheet24lec24-slides

Quiz for Unit 6

   

 

Fri

Apr 02

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

Module 2: 7.1, 7.2Topic 7.1 Lecture, Topic 7.2 Lectureworksheet25lec25-slides  

 

  

11

Mon

Apr 05

Lecture 26: Java Locks

Module 2: 7.3Topic 7.3 Lecture worksheet26lec26-slides Homework 4 (includes one intermediate checkpoint)Homework 3 (all)  

 

Wed

Apr 07

Lecture 27: Linearizability of Concurrent Objects 

Module 2: 7.4Topic 7.4 Lecture worksheet27lec27-slides

 

   

 

Fri

Apr 09

Lecture 28: Safety and Liveness Properties, Java Synchronizers, Dining Philosophers Problem

 Topic 7.5 Lecture, Topic 7.6 Lectureworksheet28lec28-slides

Quiz for Unit 7

 

 

  

12

Mon

Apr 12

Lecture 29: Message Passing Interface (MPI), (start of Module 3)

 Topic 8.1 Lecture, Topic 8.2 Lecture, Topic 8.3 Lectureworksheet29lec29-slides

 

Quiz for Unit 6

  

 

Wed

Apr 14

Lecture 30: Message Passing Interface (MPI, contd)

 Topic 8.4 Lectureworksheet30lec30-slides

 

   

 

Fri

Apr 16

Lecture 31: Message Passing Interface (MPI, contd)

  Topic 8.5 Lecture, Topic 8 Demonstration Videoworksheet31lec31-slidesQuiz for Unit 8

Quiz for Unit 7

  

13

Mon

Apr 19

Lecture 32: Task Affinity with Places

  worksheet32lec32-slides

 

Homework 4 Checkpoint-1

  

 

Wed

Apr 21

Lecture 33: Eureka-style Speculative Task Parallelism

  worksheet33

lec33-slides

 

   

 

Fri

Apr 23

Lecture 34: Algorithms based on Parallel Prefix (Scan) operations

  worksheet34lec34-slides 

Quiz for Unit 8

  

14

Mon

Apr 26

Lecture 35: Algorithms based on Parallel Prefix (Scan) operations cont.  worksheet35lec35-slides

 

 

   WedApr 28Lecture 36: Course Review (Lectures 19-33)   lec36-slides Homework 4 (all)   FriApr 30Lecture 37: Course Review (Lectures 19-33)   lec37-slides    

Lab Schedule

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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