COMP 322: Fundamentals of Parallel Programming (Spring

...

2023)

...

Instructor:	Mackale Joyner, DH

2071Head TA:Srdan Milakovic

Co-Instructor:

Zoran Budimlić, DH 3134

Graduate TAs:

Jonathan Sharman

Admin Assistant:Annepha Hurlock, annepha@rice.edu, DH 3122, 713-348-5186Undergraduate TAs:Liam Bonnage, Harrison Brown, Mustafa El-Gamal, Krishna Goel, Ryan Green, Ryan Han, Rishu Harpavat, Namanh Kapur, Tian Lan, Tam Le, Will LeVine, Eva Ma, Hamza Nauman, Rutvik Patel, Aryan Sefidi, Tory Songyang, Jiaqi Wang, Erik Yamada, Yifan Yang

2063	TAs:	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/

class

rice/

jmwfpr1i85n7l4

spring2023/comp322 (Piazza is the preferred medium for all course communications

, but you can also send email to comp322-staff at rice dot edu if needed

)	Cross-listing:	ELEC 323
Lecture location:

Herring Hall 100

Herzstein Amphitheater	Lecture times:	MWF 1:00pm - 1:50pm
Lab locations:

Herring Hall 100

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

Thursday, 4

Mon  3:00pm - 3:50pm (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  handout (Concurrency)

There

...

There are also 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 (

2018

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

07Module 1: Section 1.1Topic 1.1 Lecture, Topic 1.1 Demonstration

09	Lecture 1:

Task Creation and Termination (Async, Finish)

Introduction

worksheet1

lec1-slides

 

 

 

			WS1-solution
	Wed	Jan

09

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 11Lecture 3: Abstract Performance Metrics, Multiprocessor SchedulingModule 1: Section 1.4Topic 1.4 Lecture, Topic 1.4 Demonstrationworksheet3lec3-slides

 

 

2

Mon

Jan 14

Lecture 4:    Parallel Speedup and Amdahl's Law

Module 1: Section 1.5Topic 1.5 Lecture, Topic 1.5 Demonstrationworksheet4 lec4-slides   

 

Wed

Jan 16

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.

1

4

Topic 2.

1

4 Lecture, Topic 2.

1

4 Demonstration

worksheet5

worksheet6

lec5Lecture 6:  Memoization

lec6-slides

Quiz for Unit 1

 

 

Fri

Jan 18

WS6-solution
	Wed	Jan 25	Lecture 7: Futures	Module 1: Section 2.

2

1

Topic 2.

2

1 Lecture , Topic 2.

2

1 Demonstration

worksheet6

worksheet7

lec6

3

Mon

lec7-slides

  

WS7-solution
	Fri	Jan

21

No lecture, School Holiday (Martin Luther King, Jr. Day)

       WedJan 23

Lecture 7: Finish Accumulators

Module 1: Section 2.3Topic 2.3 Lecture, Topic 2.3 Demonstration  worksheet7lec7-slides

Homework 2

Homework 1

 

Fri

Jan 25

Lecture 8: Map Reduce

Module 1: Section 2.4Topic 2.4 Lecture, Topic 2.4 Demonstrationworksheet8lec8-slides

 

Quiz for Unit 1

4

Mon

Jan 28

Lecture 9: 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 Demonstration   worksheet9lec9-slides  

 

Wed

Jan 30

Lecture 10: Java’s Fork/Join LibraryModule 1: Sections 2.7, 2.8Topic 2.7 Lecture, Topic 2.8 Lecture,worksheet10lec10-slidesQuiz for Unit 2 

 

Fri

Feb 01

Lecture 11: Loop-Level Parallelism, Parallel Matrix Multiplication, Iteration Grouping (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

worksheet11lec11-slides  

5

Mon

Feb 04

Lecture 12:  Barrier Synchronization

Module 1: Section 3.4Topic 3.4 Lecture , Topic 3.4 Demonstrationworksheet12lec12-slides   

Wed

Feb 06

Lecture 13: Parallelism in Java Streams, Parallel Prefix Sums

 Topic 3.7 Java Streams, Topic 3.7 Java Streams Demonstrationworksheet13lec13-slides

Homework 3 (includes 2 intermediate checkpoints)

Homework 2

-

Fri

Feb 08

Spring Recess

      

6

Mon

Feb 11

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 Demonstration  worksheet14 lec14-slidesQuiz for Unit 3Quiz for Unit 2

 

Wed

Feb 13

Lecture 15:  Data-Driven Tasks

Module 1: Section 4.5Topic 4.5 Lecture   Topic 4.5 Demonstrationworksheet15lec15-slides  

 

Fri

Feb 15

Lecture 16:

27	Lecture 8:  Async, Finish, Computation Graphs	Module 1: Sections 1.1, 1.2	Topic 1.1 Lecture, Topic 1.1 Demonstration, Topic 1.2 Lecture, Topic 1.2 Demonstration	worksheet8	lec8-slides			WS8-solution
4	Mon	Jan 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 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-slides	Homework 2		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	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. 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

Topic 4.3 Demonstration

worksheet16

worksheet21

lec16

lec21-slides

 Quiz for Unit 3

7

Mon

Feb 18

Lecture 17: Midterm Summary

   lec17-slides  

 

Wed

Feb 20

Midterm Review (interactive Q&A)

      

 

Fri

Feb 22

Lecture 18: Abstract vs. Real Performance

  worksheet18 lec18-slides  Homework 3, Checkpoint-1

8

Mon

Feb 25

Lecture 19: 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 Demonstration,worksheet19lec19-slidesQuiz for Unit 4

 

 

Wed

Feb 27

Lecture 20: Critical sections, Isolated construct, Parallel Spanning Tree algorithm, Atomic variables (start of Module 2)

Module 2: Sections 5.1, 5.2, 5.3, 5.4, 5.6

Topic 5.1 Lecture, Topic 5.1 Demonstration, Topic 5.2 Lecture, Topic 5.2 Demonstration, Topic 5.3 Lecture, Topic 5.3 Demonstration, Topic 5.4 Lecture, Topic 5.4 Demonstration, Topic 5.6 Lecture, Topic 5.6 Demonstration

worksheet20lec20-slides 

 

 

Fri

Mar 01

Lecture 21:  Read-Write Isolation, Review of Phasers

Module 2: Section 5.5Topic 5.5 Lecture, Topic 5.5 Demonstrationworksheet21 lec21-slidesQuiz for Unit 5

Quiz for Unit 4

9

Mon

Mar 04

Lecture 22: Actors

Module 2: 6.1, 6.2Topic 6.1 Lecture ,   Topic 6.1 Demonstration ,   Topic 6.2 Lecture, Topic 6.2

		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

 

 

Wed

WS22-solution

 

 

9	Mon	Mar 06	Lecture 23:

Actors (contd)Module 2: 6.3, 6.4, 6.5, 6.6Topic 6.3 Lecture, Topic 6.3 Demonstration, Topic 6.4 Lecture , Topic 6.4 Demonstration,   Topic 6.5 Lecture, Topic 6.5 Demonstration, Topic 6.6 Lecture, Topic 6.6

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

Quiz for Unit 6

Homework 3

, Checkpoint-2

 

Fri

Mar 08

Lecture 24: Java Threads, Java synchronized statement

(CP 1)	WS23-solution
	Wed	Mar 08	Lecture 24: Confinement & Monitor Pattern. Critical sections Global lock	Module 2:

7

Sections 5.1,

7

5.2

Topic

7

5.1 Lecture, Topic 5.1 Demonstration, Topic

7

5.2 Lecture

worksheet24lec24-slides  Quiz for Unit 5-

M-F

Mar 11 - Mar 15

Spring Break

      

10

Mon

Mar 18

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

Module 2: 7.2Topic 7.2 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

20Topic 7.3 Lecture, Topic 7.4 Lecture

15

Lecture 26: Java Locks, Linearizability of Concurrent Objects

Module 2: 7.3, 7.4

 

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

Homework 4

(includes one intermediate checkpoint)

 

 

 

 

 

 

Homework 3 (all)

WS26-solution
	Wed	Mar 22	Lecture 27:

Safety and Liveness Properties, Java Synchronizers, Dining Philosophers Problem

Java Threads and Locks

Module 2: Sections 7.

5

1, 7.

6

3

Topic 7.

5

1 Lecture, Topic 7.

6

11

Mon

Mar 25

Lecture 28: Message Passing Interface (MPI), (start of Module 3)

 Topic 8.1 Lecture, Topic 8.2 Lecture, Topic 8.3 Lecture,

3 Lecture

worksheet27

lec27-slides

Quiz for Unit 7

Quiz for Unit 6

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

 Wed

11

 

Mon

Mar 27

Lecture 29:

  Message Passing Interface (MPI, contd) Topic 8.4 Lecture, Topic 8.5 Lecture, Topic 8 Demonstration VideoFri

Dining Philosophers Problem

Module 2: Section 7.6

Topic 7.6 Lecture

worksheet29

lec29-slides

Quiz for Unit 8

 

 

 Topic 9.1 Lecture (optional, overlaps with video 2.4), Topic 9.2 Lecture, Topic 9.3 Lecture

WS29-solution
	Wed	Mar 29	Lecture 30:

Distributed Map-Reduce using Hadoop and Spark frameworksApr 01

Task Affinity and locality. Memory hierarchy

worksheet30

lec30-slides

 Quiz for Unit 7

12

Mon

Homework 3 (All)	WS30-solution
	Fri	Mar 31	Lecture 31:

TF-IDF and PageRank Algorithms with Map-Reduce Topic 9.4 Lecture, Topic 9.5 Lecture, Unit 9 Demonstration

 

Wed

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

Quiz for Unit 9

 

  

Homework 4		WS31-solution
12	Mon	Apr 03	Lecture 32:

 Partitioned Global Address Space (PGAS) programming modelsFri

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

worksheet32

lec32-slides

 

Homework 4 Checkpoint-1

 

WS32-solution
	Wed	Apr 05	Lecture 33:

Combining Distribution and Multithreading Lectures 10.1 - 10.5, Unit 10 Demonstration (all videos optional – unit 10 has no quiz)

13

Mon

Apr 08

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

worksheet33

lec33-slides

 

Quiz for Unit 8

 

WS33-solution
	Fri	Apr 07	Lecture 34:

 Task Affinity with Places

N-Body problem, applications and implementations

worksheet34

lec34-slides

 

WS34-solution

 

13

Wed

Mon

Apr 10

Lecture 35: Eureka-style Speculative Task Parallelism

  Fri

worksheet35

lec35-slides

Homework 5

Homework 4 (all)

 

 

WS35-solution
	Wed	Apr 12	Lecture 36:

GPU Computing Apr 15

Scan Pattern. Parallel Prefix Sum

worksheet36

lec36-slides

 

Quiz for Unit 9

14

Mon

WS36-solution
	Fri	Apr 14	Lecture 37:

Algorithms based on

Parallel Prefix

(Scan) operations  worksheet38

Sum applications

worksheet37

lec37-slides

 

 

 

Wed

Apr 17

Lecture 38: Algorithms based on Parallel Prefix (Scan) operations, contd.  

 

Fri

14

Mon

Apr 17

Lecture 38: Overview of other models and frameworks

lec38-slides

 

 

Wed

Apr 19

Lecture 39: Course Review (Lectures

18-38)   lec39-slides 

Homework 5

-                   

19-38)				lec39-slides		Homework 4
	Fri	Apr 21	Lecture 40: Course Review (Lectures 19-38)				lec40-slides

Lab Schedule

Grading, Honor Code Policy, Processes and Procedures

Grading will be based on your performance on five homeworks four homework assignments (weighted 40% in all), two exams (weighted 40% in all), weekly lab exercises (weighted 10% in all), online quizzes (weighted 5% in all), and in-class worksheets (weighted 5% in all).

The purpose of the homeworks homework is to give you practice in solving problems that deepen your understanding of concepts introduced in class. Homeworks are Homework is due on the dates and times specified in the course schedule.  No late submissions (other than those using slip days mentioned below) will be accepted.

The slip day policy for COMP 322 is similar to that of COMP 321. All students will be given 3 slip days to use throughout the semester. When you use a slip day, you will receive up to 24 additional hours to complete the assignment. You may use these slip days in any way you see fit (3 days on one assignment, 1 day each on 3 assignments, etc.). Slip days will be automatically tracked through the Autograder, more details are available later in this document and in the Autograder user guideusing the README.md file. Other than slip days, no extensions will be given unless there are exceptional circumstances (such as severe sickness, not because you have too much other work). Such extensions must be requested and approved by the instructor (via e-mail, phone, or in person) before the due date for the assignment. Last minute requests are likely to be denied.Labs .

Labs must be submitted by the following Wednesday at 4:30pm.  Labs must be checked off by a TA by the following Monday at 11:59pm.

Worksheets should be completed in class for full credit.  For partial credit, a worksheet can be turned in before the start of the class following the one in which the worksheet for distributed, by the deadline listed in Canvas so that solutions to the worksheets can be discussed in the next class.

You will be expected to follow the Honor Code in all homeworks and homework and exams.  The following policies will apply to different work products in the course:

• In-class worksheets: You are free to discuss all aspects of in-class worksheets with your other classmates, the teaching assistants and the professor during the class. You can work in a group and write down the solution that you obtained as a group. If you work on the worksheet outside of class (e.g., due to an absence), then it must be entirely your individual effort, without discussion with any other students.  If you use any material from external sources, you must provide proper attribution.
• Weekly lab assignments: You are free to discuss all aspects of lab assignments with your other classmates, the teaching assistants and the professor during the lab.  However, all code and reports that you submit are expected to be the result of your individual effort. If you work on the lab outside of class (e.g., due to an absence), then it must be entirely your individual effort, without discussion with any other students.  If you use any material from external sources, you must provide proper attribution (as shown here).
• HomeworksHomework: All submitted homeworks are homework is expected to be the result of your individual effort. You are free to discuss course material and approaches to problems with your other classmates, the teaching assistants and the professor, but you should never misrepresent someone else’s work as your own. If you use any material from external sources, you must provide proper attribution.
• Quizzes: Each online quiz will be an open-notes individual test.  The student may consult their course materials and notes when taking the quizzes, but may not consult any other external sources.
• Exams: Each exam will be a closedopen-book, closedopen-notes, and closedopen-computer individual written test, which must be completed within a specified time limit.  No class notes or external materials may be consulted when taking the exams.

...