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COMP 322: Fundamentals of Parallel Programming (Spring

...

2024)

Instructors

Instructor:	Mackale Joyner, DH 2063

Zoran Budimlić, DH 3003

	TAs:

Adrienne Li, Austin Hushower, Claire Xu, Diep Hoang, Hunena Badat, Maki Yu, Mantej Singh, Rose Zhang, Victor Song, Yidi Wang

Haotian Dang, Andrew Ondara, Stefan Boskovic, Huzaifa Ali, Raahim Absar
Piazza site:	https://piazza.com/rice/spring2024

Admin Assistant:Annepha Hurlock, annepha@rice.edu, DH 3122, 713-348-5186

Piazza site:

https://piazza.com/rice/spring2022

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

Amphitheater (online 1st 2 weeks)

Lab locations:
Amp	Lecture times:	MWF 1:00pm - 1:50pm

Keck 100 (online 1st 2 weeks

Mon (Brockman 101)

Tue (Herzstein Amp)

Lab times:

Mon 3:00pm - 3:50pm (SB, HA, AO)

Wed

Tue 4:00pm - 4:

30pm - 5:20pm (Claire, Hunena, Mantej

50pm (RA, HD)

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.

...

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

There

...

There are also a few optional 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:

Slides titled "MPI-based Approaches for Java" by Bryan Carpenter

Lecture Schedule

Week

Week	Day	Date (

2022

2024)	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

08	Lecture 1: Introduction

worksheet1

lec1-

slides

WS1-solution


	Wed	Jan

12

10	Lecture 2: Functional Programming

worksheet2

lec2

lec02-slides

		WS2-solution
	Fri	Jan

14

12	Lecture 3: Higher order functions

worksheet3

lec3-

slides

WS3-solution
2	Mon	Jan

17

15	No class: MLK


	Wed	Jan

19

17	Lecture 4: Lazy Computation

worksheet4

lec4-slides

WS4-solution
	Fri	Jan

21

19	Lecture 5: Java Streams

worksheet5

lec5-slides

Homework 1

	WS5-solution
3	Mon	Jan

24

22

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

24

Lecture 7: Futures

Module 1: Section 2.1

Topic 2.1 Lecture , Topic 2.1 Demonstration

worksheet7

lec7-slides

WS7-solution
	Fri	Jan

28

26	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

29

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	Jan 31

WedFeb 02

Lecture 10: Event-based programming model

worksheet10

lec10-slides

	Homework 1	WS10-solution
	Fri	Feb

04

02	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

05	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

07	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

09	No class: Spring Recess


6

Mon

6

Mon

Feb 14

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

Feb 12

Lecture 14: 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

14	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

16	Lecture 16:

Accumulation and reduction. Finish accumulatorsModule 1: Section 2.3Topic 2.3 Lecture , Topic 2.3 Demonstrationworksheet16

Recursive Task Parallelism

worksheet16

lec16-slides

Homework 3

WS16-solution
7	Mon	Feb

21

19	Lecture 17: Midterm Review

lec17-slides


	Wed

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: 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 Demonstrationworksheet20lec20-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 2.7, 2.8Topic 2.7 Lecture, Topic 2.7 Demonstration, Topic 2.8 Lecture, Topic 2.8 Demonstration, worksheet22lec22-slidesHomework 4

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 Lectureworksheet27lec27-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

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 parallelismModule 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

Lab Schedule

Feb 21	Lecture 18: Midterm Review				lec18-slides
	Fri	Feb 23	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 26	Lecture 20: 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	worksheet20	lec20-slides			WS20-solution
	Wed	Feb 28	Lecture 21: Barrier Synchronization with Phasers	Module 1: Sections 3.4	Topic 3.4 Lecture, Topic 3.4 Demonstration	worksheet21	lec21-slides			WS21-solution
	Fri	Mar 01	Lecture 22: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	worksheet22	lec22-slides			WS22-solution
9	Mon	Mar 04	Lecture 23: Fuzzy Barriers with Phasers	Module 1: Section 4.1	Topic 4.1 Lecture, Topic 4.1 Demonstration	worksheet23	lec23-slides		Homework 3 (CP 1)	WS23-solution
	Wed	Mar 06	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 08	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 11	No class: Spring Break
	Wed	Mar 13	No class: Spring Break
	Fri	Mar 15	No class: Spring Break
10	Mon	Mar 18	Lecture 26: Java Threads and Locks	Module 2: Sections 7.1, 7.3	Topic 7.1 Lecture, Topic 7.3 Lecture	worksheet26	lec26-slides			WS26-solution
	Wed	Mar 20	Lecture 27: Read-Write Locks, Soundness and progress guarantees	Module 2: Section 7.3	Topic 7.3 Lecture, Topic 7.5 Lecture	worksheet27	lec27-slides		Homework 3 (CP 2)	WS27-solution
	Fri	Mar 22	Lecture 28: Dining Philosophers Problem		Topic 7.6 Lecture	worksheet28	lec28-slides			WS28-solution
11	Mon	Mar 25	Lecture 29: Linearizability of Concurrent Objects	Module 2: Sections 7.4	Topic 7.4 Lecture	worksheet29	lec29-slides			WS29-solution
	Wed	Mar 27	Lecture 30: Parallel Spanning Tree, other graph algorithms			worksheet30	lec30-slides			WS30-solution
	Fri	Mar 29	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 01	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	Homework 4	Homework 3 (All)	WS32-solution
	Wed	Apr 03	Lecture 33: Task Affinity and locality. Memory hierarchy			worksheet33	lec33-slides			WS33-solution
	Fri	Apr 05	Lecture 34: Eureka-style Speculative Task Parallelism			worksheet34	lec34-slides			WS34-solution
13	Mon	Apr 08	No class: Solar Eclipse
	Wed	Apr 10	Lecture 35: Scan Pattern. Parallel Prefix Sum			worksheet35	lec35-slides		Homework 4 (CP 1)	WS35-solution
	Fri	Apr 12	Lecture 36: Parallel Prefix Sum applications			worksheet36	lec36-slides			WS36-solution
14	Mon	Apr 15	Lecture 37: Overview of other models and frameworks				lec37-slides
	Wed	Apr 17	Lecture 38: Course Review (Lectures 19-34)				lec38-slides		Homework 4 (All)
	Fri	Apr 19	Lecture 39: Course Review (Lectures 19-34)				lec39-slides

Lab Schedule

Lab #	Date (2023

Lab #

Date (2022

)	Topic	Handouts	Examples
1	Jan

10

08

Infrastructure setup

lab0-handout

lab1-handout


-	Jan

17

15	No lab this week (MLK)
2	Jan 22	Functional Programming	lab2-handout
3	Jan 29	Futures	lab3-handout
4	Feb 05	Data-Driven Tasks	lab4-handout
-	Feb 12	No lab this week
-	Feb 19	No lab this week (Midterm Exam)
5	Feb 26	Loop Parallelism	lab5-handout	image kernels
6	Mar 04	Recursive Task Cutoff Strategy	lab6-handout
-	Mar 11

-

Jan 24

-Jan 31

-

Feb 07

-

Feb 14

-

Feb 21

-Feb 28 -Mar 07

-

Mar 14-

No lab this week (Spring Break)

7	Mar

21

18

-Mar 28 -

Apr 04

Java Threads	lab7-handout
8	Mar 25	Concurrent Lists	lab8-handout
9	Apr 01	Actors	lab9-handout


-	Apr

11

08	No lab this week (Solar Eclipse)


-	Apr

18

15	No lab this week

Grading, Honor Code Policy, Processes and Procedures

...

Labs must be submitted by the following Monday at 11:59pm3pm. 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.

...

Child pages

Versions Compared

Old Version 1718

New Version Current

Key

COMP 322: Fundamentals of Parallel Programming (Spring

2024)

Course Syllabus

Lecture Schedule

Lab Schedule

Lab Schedule

Grading, Honor Code Policy, Processes and Procedures

Child pages

Page History

Versions Compared

Old Version 1718

New Version Current

Key

COMP 322: Fundamentals of Parallel Programming (Spring

2024)

Course Syllabus

Lecture Schedule

Lab Schedule

Lab Schedule

Grading, Honor Code Policy, Processes and Procedures