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COMP 322: Fundamentals of Parallel Programming (Spring

...

2023)

 

Graduate 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 NazariCross-listing

Instructor:

Mackale Joyner, DH 2071

Head TA:Abbey Baker

Co-Instructor:

Zoran Budimlić, DH 3081

2063

TAs:

Jonathan Sharman, Srdjan Milakovic

Admin Assistant:Annepha Hurlock, annepha@rice.edu, DH 3122, 713-348-5186Undergraduate 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/rice/classspring2022/j3w0pi8pl9s8scomp322 (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:

Sewall Hall 301TBD

Lecture times:

MWF 1:00pm - 1:50pm

Lab locations:

Sewall Hall 301TBD

Lab times:

Mon  3:00pm - 3:

Thursday, 450pm ()

Tue 4:00pm - 4:50pm ()

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 , MapReduce

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 is no lecture handout for Module 3 (Distribution and Locality).  The instructors will refer you to optional resources to supplement the lecture slides and videos.

There 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:

Lecture Schedule

 

 

Finally, here are some additional resources that may be helpful for you:

Lecture Schedule

 

Topic 1.1 Lecture, Topic 1.1 Demonstration lec1-slides 19 4  Parallel Speedup and Amdahl's Law  24 6 Finish Accumulators3 3   Homework 1Jan 31 9: Java’s Fork/Join Library  05 11:  Barrier Synchronization 3 3 07 12: Parallelism in Java Streams, Parallel Prefix Sums 12 Iterative Averaging Revisited, SPMD pattern 35 Lecture , Topic .5 Demonstration , Topic 3.6 , .6   4 4 42 4. Demonstration, Topic 4.3 Lecture,  Topic 4.3  3 5.6lec22lec24Mon 19Wed 21 Java Locks

Homework 4

(includes one intermediate checkpoint)

 

Homework 3 (all) 23 27: Safety and Liveness Properties, Java Synchronizers, Dining Philosophers Problem 75 7Quiz for Unit 6lec29 30 30 Distributed Map-Reduce using Hadoop and Spark frameworksQuiz for Unit 7lec32 11 35: Eureka-style Speculative Task ParallelismHomework 4 (all) 13 36Algorithms based on (Scan) operationslec36 Wed 18 GPU ComputingFri 20 18Homework 5

WeekWeek

Day

Date (20182022)

Lecture

Assigned Reading

Assigned Videos (see Canvas site for video links)

In-class Worksheets

Slides

Work Assigned

Work Due

Worksheet Solutions 

1

Mon

Jan 0809

Lecture 1: Task Creation and Termination (Async, Finish)

Module 1: Section 1.1worksheet1

Introduction

 

 

worksheet1lec1-slides  

 

 

WS1-solution 

 

Wed

Jan 1011

Lecture 2:  Computation Graphs, Ideal Parallelism

Module 1: Sections 1.2, 1.3Topic 1.2 Lecture, Topic 1.2 Demonstration, Topic 1.3 Lecture, Topic 1.3 Demonstrationworksheet2lec2-slides

Homework 1Functional Programming

GList.java worksheet2lec02-slides

 

 

WS2-solution 
 FriJan 1213Lecture 3: Abstract Performance Metrics, Multiprocessor SchedulingModule 1: Section 1.4Topic 1.4 Lecture, Topic 1.4 Demonstrationworksheet3lec3-slides Higher order functions  worksheet3 lec3-slides   

 

 WS3-solution 

2

Mon

Jan 15No lecture, School Holiday (Martin Luther King, Jr. Day)16

No class: MLK

        

 

Wed

Jan 17

No lecture, Rice closed due to weather

  

18

Lecture 4: Lazy Computation

LazyList.java

Lazy.java

 worksheet4lec4-slides  Quiz for Unit 1WS4-solution 

 

Fri

Jan

20

Lecture

5: Java Streams

 Module 1: Section 1.5Topic 1.5 Lecture, Topic 1.5 Demonstrationworksheet4lec4-slides worksheet5lec5-slidesHomework 1 WS5-solution 
3MonJan 2223

Lecture 5: Future Tasks, Functional Parallelism ("Back to the Future")6: Map Reduce with Java Streams

Module 1: Section 2.14Topic 2.1 4 Lecture, Topic 2.1 4 Demonstration  worksheet5worksheet6lec5lec6-slides

 

 WS6-solution 

 

Wed

Jan

25

Lecture

7:

Futures

Module 1: Section 2.31Topic 2.1 Lecture , Topic 2.1 Demonstrationworksheet6worksheet7lec6lec7-slides

Homework 2

 

 WS7-solution 

 

Fri

Jan 2627

Lecture 78: Memoization, Map Reduce  Computation Graphs, Ideal Parallelism

Module 1: Section 2Sections 1.2 & 2, 1.43Topic 21.2 Lecture, Topic 21.2 Demonstration, Topic 21.4 3 Lecture, Topic 21.4 3 Demonstrationworksheet7worksheet8lec7lec8-slides Quiz for Unit 1 WS8-solution 

4

Mon

 

Jan 2930 Lecture 89: Data Races, Functional & Structural DeterminismAsync, Finish, Data-Driven Tasks 

Module 1:

Sections 2

Section 1.

5

1,

2

4.

6

5

 

Topic

2

1.

5

1 Lecture, Topic

2

1.

5

1 Demonstration, Topic

2

4.

6

5 Lecture, Topic

2

4.

6

5 Demonstration

   

worksheet8

worksheet9

lec8lec9-slidesslides   WS9-solution 
 WedFeb 01Lecture Module 1: Sections 2.7, 2.8Topic 2.7 Lecture, Topic 2.8 Lecture,worksheet9lec9-slidesQuiz for Unit 2 

 

Fri

Feb 02

Lecture 10: 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

worksheet10lec10-slides10: Event-based programming model

 

  worksheet10lec10-slides Homework 1WS10-solution 
 FriFeb 03Lecture 11: GUI programming as an example of event-based,
futures/callbacks in GUI programming
  worksheet11lec11-slidesHomework 2 WS11-solution 
5

Mon

Feb

06

Lecture 12: Scheduling/executing computation graphs
Abstract performance metrics
Module 1: Section 31.4Topic 1.4 Lecture , Topic 1.4 Demonstrationworksheet11 worksheet12lec11lec12-slides  WS12-solution 

 

Wed

Feb

08

Lecture

  worksheet12lec12-slides

  Homework 3 (includes two intermediate checkpoints)  

Homework 2

-

Fri

Feb 09

Spring Recess 13: Parallel Speedup, Critical Path, Amdahl's Law

Module 1: Section 1.5

Topic 1.5 Lecture , Topic 1.5 Demonstration

worksheet13lec13-slides  WS13-solution 

 

Fri

Feb 10

No class: Spring Recess

 

       Quiz for Unit 2 
6

Mon

Feb

13

Lecture 14:

Accumulation and reduction. Finish accumulators

Module 1: Sections 3Section 2.5, 3.6Topic 2.3 Lecture   Topic 2.3 Demonstrationworksheet14lec14-slides  

 

WS14-solution 

 

Wed

Feb 1415

Lecture 15:   Data-Driven Tasks, Point-to-Point Synchronization with Phasers Recursive Task Parallelism  

  worksheet15lec15-slides

 

 

 WS15-solution 
 FriFeb 17

Lecture 16: Data Races, Functional & Structural Determinism

Module 1: Sections 42.5, 4. 2, 4.36Topic 2.5 Lecture ,  Topic 2.5 Demonstration,  Topic 2.6 Lecture,  Topic 2.6 Demonstrationworksheet15 worksheet16 lec15lec16-slidesHomework 3Homework 2WS16-solution 

 7

FriMon

Feb 1620

Lecture 1617: Phasers  Midterm Review

Module 1: Sections 4.2Topic 4.2 Lecture ,   Topic 4.2 Demonstrationworksheet16 lec16   lec17-slides Quiz for Unit 3

7

Mon

Feb 19

Lecture 17: Midterm Summary   

 

Wed

Feb 22

Lecture 18: Limitations of Functional parallelism.
Abstract vs. real performance. Cutoff Strategy

   worksheet18lec17lec18-slides  

 

Wed

Feb 21

Midterm Review (interactive Q&A)

WS18-solution 

 

    

 

Fri

Feb 23

Lecture 18: Abstract vs. Real Performance

  worksheet18 lec18-slides  Homework 3, Checkpoint-1

Fri

Feb 24 

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, worksheet19lec19-slides  WS19-solution 

8

Mon

Feb 2627

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)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        WS20-solution 

 

Wed

Mar 01

Lecture 21:  Atomic variables, Synchronized statements

Module 2: Sections 5.

1

4,

5

7.2

,

Topic 5.4 Lecture, Topic 5.4 Demonstration, 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 Demonstrationworksheet20 lec20-slides  7.2 Lectureworksheet21lec21-slides  WS21-solution 

 

Fri

Mar 0203

Lecture 21:  Read-Write Isolation, Review of Phasers

Module 2: Section 5.5Topic 5.5 Lecture, Topic 5.5 Demonstrationworksheet21 lec21-slides  

Quiz for Unit 422: Parallel Spanning Tree, other graph algorithms 

  worksheet22lec22-slidesHomework 4

Homework 3

WS22-solution 

9

Mon

Mar 0506

Lecture 22: Actors23: Java Threads and Locks

Module 2: 6Sections 7.1, 67.23

Topic

6

7.1 Lecture,

 

Topic

6.1 Demonstration ,   Topic 6.2 Lecture, Topic 6.2 Demonstration
worksheet22

7.3 Lecture

worksheet23 lec23-slides  

 

WS23-solution 

 

Wed

Mar 0708

Lecture 23:  Actors (contd)24: Java Locks - Soundness and progress guarantees  

Module 2: 6.3, 6.4, 67.5, 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 Demonstrationworksheet23 lec23-slides

 

Homework 3, Checkpoint-27.5 Lecture worksheet24 lec24-slides 

 

WS24-solution 

 

Fri

Mar 0910Lecture 24: Java Threads, Java synchronized statement

 Lecture 25: Dining Philosophers Problem  Module 2: 7.1, 7.26Topic 7.1 Lecture, Topic 7.2 Lectureworksheet24 6 Lectureworksheet25lec25-slides  Quiz for Unit 5

 

WS25-

M-F

Mar 12 - Mar 16

solution 
 

Mon

Mar 13

No class: Spring Break

     

 

10

  
 WedMar 15

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

Module 2: 7.2Topic 7.2 Lectureworksheet25 lec25-slides No class: Spring Break    

 

   

 

Fri

Mar 17

No class: Spring Break

     

 

  

10

Mon

Mar

20

Lecture 26:

N-Body problem, applications and implementations 

  worksheet26lec26-slides   WS26-solution 

 

Wed

Mar 22

Lecture 27: Read-Write Locks, Linearizability of Concurrent Objects

Module 2: 7.3, 7.4Topic 7.3 Lecture, Topic 7.4 Lectureworksheet26 worksheet27lec26lec27-slides

 

 WS27-solution 

 

Fri

Mar

24

Lecture

28: Message-Passing programming model with Actors

Module 2: 76.51, 76.62Topic 6.1 Lecture, Topic 6.1 Demonstration,   Topic 6.2 Lectureworksheet27lec27-slides  , Topic 6.2 Demonstrationworksheet28lec28-slides

 

 

 

WS28-solution 

11

Mon

Mar 2627

 

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

29: Active Object Pattern. Combining Actors with task parallelism 

Module 2: 6.3, 6.4Topic 6.3 Lecture, Topic 6.3 Demonstration,   Topic 6.4 Lecture, Topic 6.4 Demonstrationworksheet29lec29-slides

 

 

WS29-solution 

 

Wed

Mar 2829

Lecture 29:  Message Passing Interface (MPI, contd)

 Topic 8.4 Lecture, Topic 8.5 Lecture, Topic 8 Demonstration Videoworksheet29

30: Task Affinity and locality. Memory hierarchy 

  worksheet30lec30-slides

 

 WS30-solution 

 

Fri

Mar

31

Lecture

31:

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

Data-Parallel Programming model. Loop-Level Parallelism, Loop Chunking

Module 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 Demonstrationworksheet31lec31-slidesHomework 5

Homework 4

WS31-solution 

12

Mon

Apr 0203

Lecture 31: TF-IDF and PageRank Algorithms with Map-Reduce Topic 932: Barrier Synchronization with PhasersModule 1: Section 3.4Topic 3.4 Lecture, Topic 9.5 Lecture, Unit 9  Topic 3.4 Demonstrationworksheet31 worksheet32lec31lec32-slides

 

 

WS32-solution 

 

Wed

Apr 0405

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

  worksheet32

33:  Stencil computation. Point-to-point Synchronization with Phasers

Module 1: Section 4.2, 4.3

Topic 4.2 Lecture, Topic 4.2 Demonstration, Topic 4.3 Lecture,  Topic 4.3 Demonstration

worksheet33lec33-slides

 Homework 4 Checkpoint-1

 WS33-solution 

 

Fri

Apr 0607

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 834: Fuzzy Barriers with Phasers

Module 1: Section 4.1Topic 4.1 Lecture, Topic 4.1 Demonstrationworksheet34lec34-slides 

 

WS34-solution 

13

Mon

Apr 0910

Lecture 34: Task Affinity with Places35: Eureka-style Speculative Task Parallelism 

 

worksheet34 worksheet35lec34lec35-slides

 

 

WS35-solution 
 WedApr 12Lecture 36: Scan Pattern. Parallel Prefix Sum 

 

worksheet35worksheet36lec35lec36-slides

Homework 5

  WS36-solution 
 FriApr 14Lecture 37: Parallel Prefix Sum applications  worksheet36worksheet37lec37-slides Quiz for Unit 9

14

Mon

Apr 16

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

  worksheet37lec37-slides  
14MonApr 17Lecture 38: Overview of other models and frameworks  worksheet38 lec38-slides    
 WedApr 19Lecture 39: Course Review (Lectures 19-38)   lec39-slides  -  
  Fri  Apr 21Lecture 40: Course Review (Lectures 19-38)    lec40-slides      Homework 5   

Lab Schedule

0  Setup1 11lab1Jan 25lab2- 22  - Exam-5lab5-6 08Phaserslab6- 15 - 7 22lab710lab10handout Eurekastyle Speculative Task Parallelism 

Lab #

Date (20182022)

Topic

Handouts

Code Examples

1

Jan 10

Infrastructure

setup

lab0-handout

lab1-handout

 
2Jan

Async-Finish Parallel Programming with abstract metrics

17Functional Programminglab2-handout-

2

 

3

Jan 24

Java Streams

lab3-handout
 
4Jan 31Futureslab4-handout 

35

Feb 01

Cutoff Strategy and Real World Performance

lab3

07

Data-Driven Tasks

lab5-handout-  
46

Feb 15

Java's ForkJoin Framework

lab4

14

Async / Finish

lab6-handout -
-

Feb

21

No lab this week

(Midterm

)

  
7

Mar 01

Loop-level Parallelism

 

Feb 28Recursive Task Cutoff Strategylab7-handout 
8Mar 07Java Threadslab8-handout 

-

Mar

14

No lab this week

(Spring Break)

  
9Mar

Isolated Statement and Atomic Variables

21Concurrent Listslab9-handout 
810Mar 2928Actorslab8lab10-handout 
911

Apr 05

Java Threads, Java Locks

lab9

04

Loop Parallelism

lab11-handout 

Apr 12

Message Passing Interface (MPI) 

-

 

10

Apr 19

Apache Spark

lab11-handout

11

No lab this week

  

 

-

    

Apr 18

No lab this week

  

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 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 by the deadline listed in Canvas so that solutions to the worksheets can be discussed 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.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

...