Versions Compared

Key

  • This line was added.
  • This line was removed.
  • Formatting was changed.

COMP 322: Fundamentals of Parallel Programming (Spring

...

2023)

 

Tory Songyang, Zishi Wang

Instructor:

Mackale Joyner, DH 2063

Head TAs:Jonathan Cai (hw), Paul Jiang (lab 1pm), William Su (lab 4pm)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/configure-classesrice/spring2020spring2022/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:

Sewell Hall 301TBD

Lecture times:

MWF 1:00pm - 1:50pm

Lab locations:

Sewell Hall 301TBD

Lab times:

Thursday, 1Mon  3:00pm - 13:50pm , ()

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 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

...

There are also a few optional textbooks that 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:

...

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

Lecture Schedule

 

 

Quiz for Unit 1 24Topic 2.1 Lecture, Topic 2.1 Demonstration Future Tasks, Functional Parallelism ("Back to the Future")  29 Map Reduce4 4   Homework 1Quiz for Unit 2 05 Loop-Level Parallelism, Parallel Matrix MultiplicationTopic 3.1 Lecture , Topic 3.1 Demonstration ,  Topic 3.2 Lecture,  Topic 3.2 Demonstration   07 Iteration Grouping (Chunking), Barrier Synchronization Topic 3.3 Lecture , Topic 3.3 Demonstration, Topic 3.4 Lecture  ,   Topic 3.4 Demonstration   10  Parallelism in Java Streams, Parallel Prefix Sums Topic 3.7 Java Streams 37 Java Streams Quiz for Unit 2 12 Iterative Averaging Revisited, SPMD pattern 3 3 , Topic 3.6 Lecture,   Topic 3.6 DemonstrationHomework 2 17 Data-Driven Tasks 45 45 21 Pipeline Parallelism, Signal Statement, Fuzzy Barriers44 44 41  Topic 4.1 Quiz for Unit 3Module 2: Sections 5.1, 5.lec20 FriQuiz for Unit 5lec25-slides Wed 25 Java Locks, Linearizability of

 

 

 

 

  27 27: Safety and Liveness Properties, Java Synchronizers, Dining Philosophers Problem 75 7lec27

Homework 3 (all)

Quiz for Unit 6lec28Apr 03 30 Distributed Map-Reduce using Hadoop and Spark frameworks14Mon 20Algorithms based on (Scan) operationsWed 22Algorithms based on (Scan) operations, contd.Fri 24 Course Review (Lectures 20-38)Homework 5

Week

Day

Date (20202022)

Lecture

Assigned Reading

Assigned Videos (see Canvas site for video links)

In-class Worksheets

Slides

Work Assigned

Work Due

Worksheet Solutions 

1

Mon

Jan 1309

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

Module 1: Section 1.1

Topic 1.1 Lecture, Topic 1.1 Demonstration Introduction

 

 

worksheet1lec1-slidesslides  

 

 

WS1-solution 

 

Wed

Jan 1511

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 1

 Functional Programming

GList.java worksheet2lec02-slides

 

 

WS2-solution 
 FriJan 1713Lecture 3: Abstract Performance Metrics, Multiprocessor SchedulingModule 1: Section 1.4Topic 1.4 Lecture, Topic 1.4 Demonstrationworksheet3lec3-slidesHigher order functions  worksheet3 lec3-slides   

 

 WS3-solution 

2

Mon

Jan 20No lecture, School Holiday (Martin Luther King, Jr. Day)16

No class: MLK

        

 

Wed

Jan 2218

Lecture 4: Parallel Speedup and Amdahl's LawModule 1: Section 1.5Topic 1.5 Lecture, Topic 1.5 DemonstrationLazy Computation

LazyList.java

Lazy.java

 worksheet4lec4-slides  

 

WS4-solution 

 

Fri

Jan

20

Module 1: Section 2.1

Lecture 5:

Java Streams

  worksheet5lec5-slidesHomework 1 WS5-solution 
3MonJan 2723

Lecture 6:   Finish Accumulators Map Reduce with Java Streams

Module 1: Section 2.34Topic 2.3 4 Lecture, Topic 2.3 4 Demonstration  worksheet6lec6-slides

 

 WS6-solution 

 

Wed

Jan

25

Lecture 7:

Futures

Module 1: Section 2.41Topic 2.1 Lecture , Topic 2.1 Demonstrationworksheet7lec7-slides

Homework 2

 

 WS7-solution 

 

Fri

Jan 3127

Lecture 8: Data Races, Functional & Structural Determinism  Computation Graphs, Ideal Parallelism

Module 1: Section Sections 1.2.5, 21.63Topic 1.2 .5 Lecture, Topic 1.2 .5 Demonstration, Topic 21.6 3 Lecture, Topic 21.6 3 Demonstration   worksheet8lec8-slides Quiz for Unit 1 WS8-solution 

4

Mon

 Feb 03

Jan 30 Lecture 9: Java’s Fork/Join LibraryAsync, Finish, Data-Driven Tasks 

Module 1:

Sections 2

Section 1.

7

1,

2

4.

8

5

 

Topic

2

1.

7

1 Lecture, Topic

2.8 Lecture

1.1 Demonstration, Topic 4.5 Lecture, Topic 4.5 Demonstration

worksheet9

lec9-slidesslides   WS9-solution 
 WedFeb 01Lecture 10: Module 1: Sections 3.1, 3.2 Event-based programming model

 

  worksheet10lec10-slides Homework 1WS10-solution 
 FriFeb 03Lecture 11: Module 1: Sections 3.3, 3.4 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 31.74Topic 1.4 Lecture , Topic 1.4 Demonstrationworksheet12lec12-slides  WS12-solution 

 

Wed

Feb

08

Lecture 13:

Parallel Speedup, Critical Path, Amdahl's Law

Module 1: Sections 3Section 1.5, 3.6

Topic

1.5 Lecture , Topic

1.5 Demonstration

worksheet13lec13-slides

Homework 3 (includes 2 intermediate checkpoints)

Quiz for Unit 3

  WS13-solution 

- 

Fri

Feb 1410

No class: Spring Recess

 

        
6

Mon

Feb

13

Lecture 14:

Accumulation and reduction. Finish accumulators

Module 1: Sections 4Section 2.53Topic 2.3 Lecture   Topic 2.3 Demonstrationworksheet14lec14-slides  WS14-solution 

 

Wed

Feb 1915

Lecture 15: Recursive Task Parallelism   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 Demonstration

  worksheet15lec15-slides

 

 

 WS15-solution 
 FriFeb 17

Lecture 16:

Data Races, Functional & Structural Determinism

Module 1: Sections 42.45, 42.16Topic 2.5 Lecture ,  Topic 2.5 Demonstration,  Topic 2.6 Lecture,  Topic 2.6 Demonstrationworksheet16 lec16-slidesQuiz for Unit 4Homework 3Homework 2WS16-solution 

7

Mon

Feb 2420

Lecture 17: Midterm  Midterm Review

   lec17-slides    

 

Wed

Feb 2622

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

  worksheet18lec18lec18-slides   WS18-solution 

 

Fri

Feb 2824 

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

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 27

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 Demonstrationworksheet19worksheet20lec19lec20-slides        Homework 3, Checkpoint-1WS20-solution 

8 

MonWed

Mar 0201

Lecture 20: Parallel Spanning Tree algorithm, 21:  Atomic variables, Synchronized statements

Module 2: Sections 5.

3, 5.

4,

5

7.

5

2

Topic 5.3 Demonstration, Topic 5.4 Lecture, Topic 5.4 Demonstration, Topic 57.5 Lecture, Topic 5.5 Demonstrationworksheet202 Lectureworksheet21lec21-slides  WS21-solution 

 

WedFri

Mar 0403

Lecture 21: Actors

Module 2: 6.1, 6.2

Topic 6.1 Lecture ,   Topic 6.1 Demonstration ,   Topic 6.2 Lecture, Topic 6.2 Demonstration

worksheet21 lec21-slides  

 

 

 

Fri22: Parallel Spanning Tree, other graph algorithms 

  worksheet22lec22-slidesHomework 4

Homework 3

WS22-solution 

9

Mon

Mar 06

Lecture 22: Actors (contd)23: Java Threads and Locks

Module 2: 6Sections 7.31, 6.4, 6.5, 6.67.3

Topic

6

7.

3

1 Lecture, Topic

6

7.3

Demonstration, Topic 6.4 Lecture , Topic 6.4 Demonstration,   Topic 6.5 Lecture, Topic 6.5 Demonstration, Topic 6.6 Lecture, Topic 6.6 Demonstration
worksheet22 lec22-slides 

Quiz for Unit 4

 

9

Mon

Mar 09

Lecture 23: Java Threads, Java synchronized statementLecture

worksheet23 lec23-slides  

 

WS23-solution 

 

Wed

Mar 08

Lecture 24: Java Locks - Soundness and progress guarantees  

Module 2: 7.1, 7.25Topic 7.1 Lecture, Topic 7.2 5 Lecture worksheet23 worksheet24 lec23lec24-slides 

  

WS24-solution 

 

WedFri

Mar 1110Lecture 24:  TBD

 Lecture 25: Dining Philosophers Problem  Module 2: TBD7.6Topic TBD7.6 Lectureworksheet25lec25-slides 

 Homework 3, Checkpoint-2

WS25-solution 
 

Mon

Mar 13

No class: Spring Break

     Quiz for Unit 6

 

  
- M-FWedMar 16 - Mar 2015No class: Spring Break    

 

   

10 

MonFri

Mar 2317

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

Module 2: 7.2Topic 7.2 Lectureworksheet25

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 Homework 4

(includes one intermediate checkpoint)

 

 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 Lectureworksheet27, Topic 6.2 Demonstrationworksheet28lec28-slides Quiz for Unit 7

 

 

 

WS28-solution 

11

Mon

Mar 3027

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

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

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 

 

WedApr 01

Mar 29

 

Lecture 29:  Message Passing Interface (MPI, contd)

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

Quiz for Unit 8

30: Task Affinity and locality. Memory hierarchy 

  worksheet30lec30-slides

 

 WS30-solution 

 

Fri

Mar 31

Lecture

 

Fri

Apr 10

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

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

31:

 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 06

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

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

 

 

 

Wed

Apr 08

TBD

    

 

Homework 4 Checkpoint-1

 

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 03

Lecture 32: Barrier Synchronization with PhasersModule 1: Section 3.4Topic 3.4 Lecture,  Topic 3.4 Demonstrationworksheet32lec32-slides

 

 Quiz for Unit 8

WS32-solution 

13 

MonWed

Apr 1305

 

Lecture 33Combining Distribution and Multithreading

 

:  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

 

 WS33-solution 

 

WedFri

Apr 1507

 

Lecture 34: Task Affinity with Places

 

 Fuzzy Barriers with Phasers

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

Homework 5

Homework 4 (all) 

 

WS34-solution 

 13

FriMon

Apr 1710

Lecture 35: Eureka-style Speculative Task Parallelism 

 

worksheet35lec35-slides

 

Quiz for Unit 9

 

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

 

worksheet36lec36-slides  WS36-solution 
 FriApr 14Lecture 37: Parallel Prefix Sum applications  worksheet37lec37-slides    
14MonApr 17Lecture 38: Overview of other models and frameworks   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  Setuplab0-1 16lab1- - 2 30lab2- No lab this week - Spring Recess-5 27midterm exam 910 09  Java's ForkJoin Framework

Lab #

Date (20202022)

Topic

Handouts

Examples

1

Jan 10

Infrastructure

setup

lab0-handout

lab1-handout

 
2Jan

Async-Finish Parallel Programming with abstract metrics

17Functional Programminglab2-handout 

3

 No lab this week

Jan 24

Java Streams

lab3-handout
 
4Jan 31Futureslab4-handout 

35

Feb 06

Cutoff Strategy and Real World Performance

lab3-handout -

-

 

07

Data-Driven Tasks

lab5-handout 
64

Feb 2014

DDFsAsync / Finish

lab4lab6-handout 
-

Feb

21

No lab this week (

Midterm)

  
7

6

Mar 05

Loop-level Parallelism

lab5-handout lab5-intro

7

Mar 12

Isolated Statement and Atomic Variables

lab6-handout -

-

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

-

Mar 14

No lab this week - (Spring Break)

  
89Mar 2621ActorsConcurrent Listslab7lab9-handout- 
10

Apr 02

Java Threads, Java Locks

lab8Mar 28Actorslab10-handout- 
11

Apr

Message Passing Interface (MPI)

lab9-handout -

04

Loop Parallelism

lab11-handout 

Apache Spark

 

-

 

 

Eureka-style Speculative Task ParallelismApr 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 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 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.

...