COMP 322: Fundamentals of Parallel Programming (Spring

...

2024)

...

Instructor:	Mackale Joyner, DH

2071Head TA:Abbey Baker

Co-Instructor:

Zoran Budimlić, DH 3081

Graduate

2063

TAs:

Jonathan Sharman, Srdjan Milakovic

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

Ashok Sankaran, Austin Bae, Avery Whitaker, Aydin Zanager, Eduard Danalache, Frank Chen, Hamza Nauman, Harrison Brown, Jahid Adam, Jeemin Sim, Kitty Cai, Madison Lewis, Ryan Han, Teju Manchenella, Victor Gonzalez, Victoria Nazari

Piazza site:

Haotian Dang, Andrew Ondara, Stefan Boskovic, Huzaifa Ali, Raahim Absar
Piazza site:	https:

https:

//piazza.com/rice/

class

spring2024/

j3w0pi8pl9s8s

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

Cross

-listing:	ELEC 323
Lecture location:

Sewall Hall 301

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

	Mon (Brockman 101) Tue (Herzstein Amp)

Sewall Hall 301

	Lab times:

Thursday,

Mon  3:00pm - 3:50pm (SB, HA, AO) Tue   4:00pm - 4:

50pm

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.

...

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 a few optional textbooks that we will 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 (

2018Module 1: Section 1.1Topic 1.1 Lecture, Topic 1.1 Demonstration

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 08	Lecture 1:

Task Creation and Termination (Async, Finish)

Introduction

worksheet1

lec1-slides

 

 

WS1-solution

 

	Wed	Jan 10	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 12	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

 

		WS3-solution
2	Mon	Jan 15	No

lecture, School Holiday (Martin Luther King, Jr. Day)

class: MLK
	Wed	Jan 17	Lecture 4: Lazy Computation			worksheet4	lec4-slides			WS4-solution
	Fri	Jan 19	Lecture 5: Java Streams			worksheet5	lec5-slides	Homework 1		WS5-solution
3	Mon	Jan 22	Lecture 6: Map Reduce with Java Streams

      

 

Wed

Jan 17

Lecture 4:   Parallel Speedup and Amdahl's Law

Module 1: Section 1.5Topic 1.5 Lecture, Topic 1.5 Demonstrationworksheet4lec4-slides

Quiz for Unit 1

 

 

Fri

Jan 19

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
	Wed

3

Mon

Jan

22

24

Lecture

6

7:

Memoization

Futures

Module 1: Section 2.

2

1

Topic 2.

2

1 Lecture , Topic 2.

2

1 Demonstration

worksheet6

worksheet7

lec6

lec7-slides

   

		WS7-solution
	Fri

Wed

Jan

24

26

Lecture

7

8:

Finish Accumulators

Async, Finish, Computation Graphs

Module 1:

Section 2.3

Sections 1.1, 1.2

Topic

2

1.

3

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

3

2 Demonstration

  worksheet7

worksheet8

lec7

lec8-slides

Homework 2

Homework 1

 

Fri

Jan 26

		WS8-solution
4	Mon	Jan 29	Lecture 9: Ideal Parallelism, Data-Driven Tasks

Lecture 8: Map Reduce

Module 1: Section

2

1.3, 4.5

Topic

2

1.

4

3 Lecture, Topic

2

1.

4 Demonstrationworksheet8lec8-slides

 

Quiz for Unit 1

4

Mon

Jan 29

Lecture 9: Data Races, Functional & Structural Determinism

Module 1: Sections 2.5, 2.6Topic 2

3 Demonstration, Topic 4.5 Lecture, Topic

2

4.5

Demonstration, Topic 2.6 Lecture, Topic 2.6

Demonstration

   

worksheet9

lec9-slides

  

WS9-solution
	Wed	Jan 31	Lecture 10:

Java’s Fork/Join LibraryModule 1: Sections 2.7, 2.8Topic 2.7 Lecture, Topic 2.8 Lecture,

Event-based programming model

worksheet10

lec10-slides

  

Homework 1

WS10-solution

 

	Fri	Feb 02	Lecture 11:

Loop-Level Parallelism, Parallel Matrix Multiplication, Iteration Grouping (Chunking)

GUI programming, Scheduling/executing computation graphs

Module 1:

Sections 3.

Section 1

, 3.2, 3.3

.4

Topic

3

1.

1

4 Lecture , Topic

3.

1

Demonstration , Topic 3.2 Lecture, Topic 3.2 Demonstration, Topic 3.3 Lecture , Topic 3.3

.4 Demonstration

worksheet11

lec11-slides

 

Homework 2

WS11-solution

 

5	Mon	Feb 05	Lecture 12:

  Barrier Synchronization

Abstract performance metrics, Parallel Speedup, Amdahl's Law

Module

Module

1: Section

3

1.

4

5

Topic

3

1.

4

5 Lecture , Topic

3

1.

4  

5 Demonstration

worksheet12

lec12-slides

     

WS12-solution
	Wed	Feb 07	Lecture 13:

Parallelism in Java Streams, Parallel Prefix Sums

Accumulation and reduction. Finish accumulators

Module 1: Section 2.3

Topic 2.3 Lecture   Topic 2.3 Demonstration

worksheet13

lec13-slides

 Homework 3 (includes two intermediate checkpoints)  Homework 2

WS13-solution

-Quiz for Unit 2

	Fri	Feb 09	No class: Spring Recess

     

6	Mon	Feb 12	Lecture 14:

Iterative Averaging Revisited, SPMD pattern

Data Races, Functional & Structural Determinism

Module 1: Sections

3

2.5,

3

2.6

Topic

3

2.5 Lecture ,  Topic

3

2.5 Demonstration,  Topic

3

2.6 Lecture,  Topic

3

2.6 Demonstration

 

worksheet14

lec14-slides

  

 

Topic 5.4 Lecture, Topic 5.4 Demonstration,

		WS14-solution
	Wed	Feb 14	Lecture 15:

  Data-Driven Tasks, Point-to-Point Synchronization with PhasersModule 1: Sections 4.5, 4.2, 4.3Topic 4.5 Lecture   Topic 4.5 Demonstration, Topic 4.2 Lecture ,   Topic 4.2 Demonstration, Topic 4.3 Lecture,  Topic 4.3 Demonstrationworksheet15 lec15-slides   

 

Fri

Feb 16

Lecture 16: Phasers Review

Module 1: Sections 4.2Topic 4.2 Lecture ,   Topic 4.2 Demonstrationworksheet16 lec16-slides  Quiz for Unit 3

7

Mon

Feb 19

Lecture 17: Midterm Summary

   lec17-slides  

 

Wed

Feb 21

Midterm Review (interactive Q&A)

    Exam 1 held during lab time (3:00pm - 6:00pm), scope of exam limited to lectures 1-16  

 

Fri

Feb 23

Lecture 18: Abstract vs. Real Performance

  worksheet18 lec18-slides  Homework 3, Checkpoint-1

8

Mon

Feb 26

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,worksheet19 lec19-slides  

 

 

Wed

Feb 28

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

Limitations of Functional parallelism. Abstract vs. real performance. Cutoff Strategy			worksheet15	lec15-slides		Homework 2	WS15-solution
	Fri	Feb 16	Lecture 16: Recursive Task Parallelism			worksheet16	lec16-slides	Homework 3		WS16-solution
7	Mon	Feb 19	Lecture 17: Midterm Review				lec17-slides
	Wed	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

worksheet20

worksheet24

lec20

lec24-slides

 

WS24-solution

 

	Fri

 

Fri

Mar

02

08

Lecture 21

Lecture 25:

Read-Write Isolation, Review of Phasers

Atomic variables, Synchronized statements

Module 2:

Section

Sections 5.4, 7.

5

2

Topic 5.

5

4 Lecture, Topic 5.

5 Demonstrationworksheet21 lec21-slides  

Quiz for Unit 4

4 Demonstration, Topic 7.2 Lecture

worksheet25

lec25-slides

WS25-solution

9

	Mon	Mar

05

11

Lecture 22: Actors

Module 2: 6.1, 6.2

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

Topic 6

.1 Lecture,

 

Topic

6.1 Demonstration ,   Topic 6.2 Lecture, Topic 6.2 Demonstrationworksheet22 lec22-slides

 

 

 

7.3 Lecture

worksheet26

lec26-slides

WS26-solution

 

	Wed	Mar

07

20

Lecture

23

27:

  Actors (contd)

Read-Write Locks,  Soundness and progress guarantees

Module 2:

6

Section 7.3

, 6.4, 6.5, 6.6

Topic

6

7.3 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 Demonstrationworksheet23 lec23-slides

 

Homework 3, Checkpoint-2

7.5 Lecture

worksheet27

lec27-slides

Homework 3 (CP 2)

WS27-solution

 

	Fri	Mar

09

22

Lecture

24: Java Threads, Java synchronized statementModule 2: 7.1, 7.2Topic 7.1 Lecture, Topic 7.2 Lectureworksheet24 lec24-slides   Quiz for Unit 5-

M-F

Mar 12 - Mar 16

Spring Break

      

10

Mon

Mar 19

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

Module 2: 7.2Topic 7.2 Lectureworksheet25 lec25-slides  

 

 

 

Wed

Mar 21

Lecture 26: Java Locks, Linearizability of Concurrent Objects

Module 2: 7.3, 7.4Topic 7.3 Lecture, Topic 7.4 Lectureworksheet26 lec26-slides

 

Homework 4

(includes one intermediate checkpoint)

 

Homework 3 (all)

 

Fri

Mar 23

Lecture 27: Safety and Liveness Properties, Java Synchronizers, Dining Philosophers Problem

Module 2: 7.5, 7.6Topic 7.5 Lecture, Topic 7.6 Lectureworksheet27lec27-slides  

Quiz for Unit 6

11

Mon

Mar 26

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

 Topic 8.1 Lecture, Topic 8.2 Lecture, Topic 8.3 Lecture,worksheet28

lec28-slides

  

 

Wed

Mar 28

Lecture 29:  Message Passing Interface (MPI, contd)

 Topic 8.4 Lecture, Topic 8.5 Lecture, Topic 8 Demonstration Videoworksheet29 lec29-slides

 

 

 

Fri

Mar 30

Lecture 30: Distributed Map-Reduce using Hadoop and Spark frameworks

 Topic 9.1 Lecture (optional, overlaps with video 2.4), Topic 9.2 Lecture, Topic 9.3 Lectureworksheet30 lec30-slides  Quiz for Unit 7

12

Mon

Apr 02

Lecture 31: TF-IDF and PageRank Algorithms with Map-Reduce

 Topic 9.4 Lecture, Topic 9.5 Lecture, Unit 9 Demonstrationworksheet31 lec31-slides  

 

 

Wed

Apr 04

Lecture 32: Partitioned Global Address Space (PGAS) programming models

  worksheet32 lec32-slides

 

Homework 4 Checkpoint-1

 

Fri

Apr 06

Lecture 33: Combining Distribution and Multithreading

 Lectures 10.1 - 10.5, Unit 10 Demonstration (all videos optional – unit 10 has no quiz)worksheet33 lec33-slides

 

Quiz for Unit 8

13

Mon

Apr 09

Lecture 34: Task Affinity with Places

  worksheet34 lec34-slides  

 

Wed

Apr 11

Lecture 35: Eureka-style Speculative Task Parallelism

  worksheet35lec35-slides

Homework 5

Homework 4 (all)

 

Fri

Apr 13

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

  worksheet36

lec36-slides

 

Quiz for Unit 9

14

Mon

Apr 16

Lecture 37: Algorithms based on Parallel Prefix (Scan) operations, contd.

  worksheet37lec37-slides 

 

 

Wed

Apr 18

Lecture 38: GPU Computing  worksheet38lec38-slides

 

 

 

Fri

Apr 20

Lecture 39: Course Review (Lectures 18-38)

   lec39-slides 

Homework 5

-                   

Lab Schedule

Grading, Honor Code Policy, Processes and Procedures

Lab Schedule

Grading, Honor Code Policy, Processes and Procedures

Grading will be based on your performance on four homework assignments Grading will be based on your performance on five homeworks (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 must be checked off by a TA submitted by the following Monday at 11:59pm.3pm.  Labs must be checked off by a TA.

Worksheets should be completed by the deadline listed in Canvas 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, 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.

 Graded homeworks will be returned to you via email, and exams as marked-up hardcopies. If you believe we have made an error in grading your homework or exam, please bring the matter to our

attention within 7 daysFor grade disputes, please send an email to the course instructors within 7 days of receiving your grade. The email subject should include COMP 322 and the assignment. Please provide enough information in the email so that the instructor does not need to perform a checkout of your code.

Accommodations for Students with Special Needs

...