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COMP 322: Fundamentals of Parallel Programming (Spring

...

2023)

 


Instructors

Instructor:

Mackale Joyner, DH 2063

Zoran Budimlić, DH 1038

TAs:
Adrienne Li, Austin Hushower, Claire Xu, Diep Hoang, Hunena Badat, Maki Yu, Mantej Singh, Rose Zhang, Victor Song, Yidi Wang Admin Assistant:Annepha Hurlock, annepha@rice.edu, DH 3122, 713-348-5186  
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/

spring2022

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

Cross-listing:

ELEC 323

Lecture location:

Herzstein Amphitheater

(online 1st 2 weeks)

Lecture times:

MWF 1:00pm - 1:50pm

Lab locations:

Mon (Herzstein Amp), Tue (Keck 100

(online 1st 2 weeks

)

Lab times:

Mon  3:00pm - 3:50pm (Raiyan, Oscar, Mohamed, Ryan, Michelle, Taha, Jasmine)

Wed

Tue 4:

30pm

00pm -

5

4:

20pm (

50pm (Tina, Delaney, Chase, Hung, Jerry, Kailin)

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

...



Week

Week

 

Day

Date (

2022

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

10Topic 1.1 Lecture, Topic 1.1 Demonstration

09

Lecture 1:

Task Creation and Termination (Async, Finish)Module 1: Section 1.1

Introduction



worksheet1lec1-slides

 

 

  
  



WS1-solution
 


Wed

Jan

12

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

 

  

Functional Programming



worksheet2lec02-slides



WS2-solution
 


FriJan
14
13Lecture 3:
Abstract Performance Metrics, Multiprocessor SchedulingModule 1: Section 1.4Topic 1.4 Lecture, Topic 1.4 Demonstrationworksheet3
Higher order functions

worksheet3 lec3-slides
 
 
 
 



WS3-solution

2

Mon

Jan

17

16

No class: MLK










Wed

Jan 18

Lecture 4:
Parallel Speedup and Amdahl's LawModule 1: Section 1.5Topic 1.5 Lecture, Topic 1.5 Demonstrationworksheet4lec4-slidesQuiz for Unit 1   

 

Wed

Jan 19

Lazy Computation



worksheet4lec4-slides

WS4-solution


Fri

Jan 20

Lecture 5: Java Streams



worksheet5lec5-slidesHomework 1
WS5-solution
3MonJan 23

Lecture 6: Map Reduce with Java Streams

Lecture 5: Future Tasks, Functional Parallelism ("Back to the Future")

Module 1: Section 2.
1
4Topic 2.
1
4 Lecture, Topic 2.
1
4 Demonstration  
worksheet5
worksheet6
lec5
lec6-slides
    



WS6-solution


Wed

Jan 25

Lecture 7: Futures

 

Fri

Jan 21

Lecture 6:   Finish Accumulators

Module 1: Section 2.
3
1Topic 2.
3
1 Lecture , Topic 2.
3
1 Demonstration
worksheet6
worksheet7
lec6
lec7-slides
 Quiz for Unit 1  



WS7-solution


Fri

Jan 27

Lecture 8:  Async, Finish, Computation Graphs

3MonJan 24Lecture 7: Map Reduce

Module 1:
Section 2.4
Sections 1.1, 1.2Topic
2
1.
4 Lecture
1 Lecture, Topic 1.1 Demonstration, Topic 1.2 Lecture, Topic 1.
4
2 Demonstration
  
worksheet7
worksheet8
lec7
lec8-slides
 


WS8-solution
 

4

 
Mon

 Lecture 8: Data Races, Functional & Structural Determinism


Jan 30 

Wed

Jan 26

Lecture 9: Ideal Parallelism, Data-Driven Tasks 

Module 1: Section

2

1.

5

3,

2

4.

6

5


Topic

2

1.

5

3 Lecture, Topic

2

1.

5

3 Demonstration, Topic

2

4.

6

5 Lecture, Topic

2

4.

6

5 Demonstration

   

worksheet8

worksheet9

lec8
lec9-
slides

Homework 2

Homework 1  
slides 

WS9-solution

WedFeb 01Lecture 10: 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 1.4Topic 1.4 Lecture , Topic 1.4 Demonstrationworksheet12lec12-slides

WS12-solution


Wed

Feb 08

Lecture 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










6

Mon

Feb 13

Lecture 14: Accumulation and reduction. Finish accumulators

Module 1: Section 2.3Topic 2.3 Lecture   Topic 2.3 Demonstrationworksheet14lec14-slides

WS14-solution


Wed

Feb 15

Lecture 15: Recursive Task Parallelism  



worksheet15lec15-slides



Homework 2WS15-solution

FriFeb 17

Lecture 16: 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 Demonstrationworksheet16 lec16-slidesHomework 3
WS16-solution

7

Mon

Feb 20

Lecture 17: Midterm Review




lec17-slides




Wed

Feb 22

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



worksheet18lec18-slides

WS18-solution


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 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 Demonstrationworksheet20lec20-slides      

WS20-solution


Wed

Mar 01

Lecture 21:  Atomic variables, Synchronized statements

Module 2: Sections 5.4, 7.2

Topic 5.4 Lecture, Topic 5.4 Demonstration, Topic 7.2 Lectureworksheet21lec21-slides
Homework 3WS21-solution


Fri

Mar 03

Lecture 22: Parallel Spanning Tree, other graph algorithms 


 worksheet22lec22-slidesHomework 4


WS22-solution

9

Mon

Mar 06

Lecture 23: Java Threads and Locks

Module 2: Sections 7.1, 7.3

Topic 7.1 Lecture, Topic 7.3 Lecture

worksheet23 lec23-slides


WS23-solution


Wed

Mar 08

Lecture 24: Java Locks - Soundness and progress guarantees  

Module 2: 7.5Topic 7.5 Lecture worksheet24 lec24-slides


WS24-solution


Fri

Mar 10

 Lecture 25: Dining Philosophers Problem  Module 2: 7.6Topic 7.6 Lectureworksheet25lec25-slides


WS25-solution

Mon

Mar 13

No class: Spring Break

 







WedMar 15No 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 Lectureworksheet27lec27-slides



WS27-solution


Fri

Mar 24

Lecture 28: Message-Passing programming model with Actors

Module 2: 6.1, 6.2Topic 6.1 Lecture, Topic 6.1 Demonstration,   Topic 6.2 Lecture, Topic 6.2 Demonstration worksheet28lec28-slides




WS28-solution

11

Mon

Mar 27

Lecture 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 29

Lecture 30: Task Affinity and locality. Memory hierarchy 



worksheet30lec30-slides


Homework 4WS30-solution


Fri

Mar 31

Lecture 31

 

Fri

Jan 28

Lecture 9: Java’s Fork/Join Library

 Topic 2.7 Lecture, Topic 2.7 Demonstration, Topic 2.8 Lecture, Topic 2.8 Demonstrationworksheet9lec9-slidesQuiz for Unit 2   

4

Mon

 

Jan 31          WedFeb 02          FriFeb 04         5

Mon

Feb 07

Lecture 10: Loop-Level Parallelism, Parallel Matrix MultiplicationModule 1: Sections 3.1, 3.2Topic 3.1 Lecture , Topic 3.1 Demonstration ,  Topic 3.2 Lecture,  Topic 3.2 Demonstrationworksheet10lec10-slides    

 

Wed

Feb 09

Lecture 11: Iteration Grouping (Chunking), Barrier Synchronization

Module 1: Sections 3.3, 3.4

Topic 3.3 Lecture , Topic 3.3 Demonstration, Topic 3.4 Lecture  ,   Topic 3.4 Demonstration

worksheet11lec11-slides    

 

Fri

Feb 11

 Lecture 12: Data-Driven Tasks 

 

Module 1: Sections 4.5

Topic 4.5 Lecture   Topic 4.5 Demonstration

worksheet12lec12-slides Quiz for Unit 2  6

Mon

Feb 14

 

        

 

Wed

Feb 16

Lecture 13: Parallelism in Java Streams, Parallel Prefix Sums 

Module 1: Sections 3.7Topic 3.7 Lecture , Topic 3.7 Demonstrationworksheet13lec13-slides

Homework 3 (includes one intermediate checkpoint)

 

Homework 2   FriFeb 18

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 Demonstrationworksheet14 lec14-slidesQuiz for Unit 3   

7

Mon

Feb 21

Lecture 15:  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 Demonstrationworksheet15lec15-slides    

 

Wed

Feb 23

Lecture 16: Midterm Review

   lec16-slides    

 

Fri

Feb 25 

Lecture 17: 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 Demonstrationworksheet17lec17-slides    

8

Mon

Feb 28

Lecture 18: Abstract vs. Real Performance

  worksheet18lec18-slides   Quiz for Unit 4Quiz for Unit 3  

 

Wed

Mar 02

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

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 Demonstrationworksheet19lec19-slides    

 

Fri

Mar 04

Lecture 20: Parallel Spanning Tree algorithm, Atomic variables

Module 2: Sections 5.3, 5.4, 5.5Topic 5.3 Demonstration, Topic 5.4 Lecture, Topic 5.4 Demonstration, Topic 5.5 Lecture, Topic 5.5 Demonstrationworksheet20lec20-slides 

Quiz for Unit 4

  

9

Mon

Mar 07

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 Quiz for Unit 5

 

  

 

Wed

Mar 09

Lecture 22: Actors (contd)

Module 2: 6.3, 6.4Topic 6.3 Lecture, Topic 6.3 Demonstration, Topic 6.4 Lecture , Topic 6.4 Demonstrationworksheet22 lec22-slides 

Homework 3, Checkpoint-1

  

 

Fri

Mar 11

      

 

 

   

Mon

Mar 14

Lecture 23: Actors (contd)

Module 2: 6.5, 6.6Topic 6.5 Lecture, Topic 6.5 Demonstration, Topic 6.6 Lecture, Topic 6.6 Demonstrationworksheet23lec23-slides 

Quiz for Unit 5

   WedMar 16 Lecture 24: Java Threads, Java synchronized statementModule 2: 7.1, 7.2Topic 7.1 Lecture, Topic 7.2 Lectureworksheet24lec24-slides

Quiz for Unit 6

   

 

Fri

Mar 18

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

Module 2: 7.1, 7.2Topic 7.1 Lecture, Topic 7.2 Lectureworksheet25lec25-slides  

 

  

10

Mon

Mar 21

Lecture 26: Java Locks

Module 2: 7.3Topic 7.3 Lecture worksheet26lec26-slides Homework 4 (includes one intermediate checkpoint)Homework 3 (all)  

 

Wed

Mar 23

Lecture 27: Linearizability of Concurrent Objects 

Module 2: 7.4Topic 7.4 Lecture worksheet27lec27-slides

 

   

 

Fri

Mar 25

Lecture 28: Safety and Liveness Properties, Java Synchronizers, Dining Philosophers Problem

 Topic 7.5 Lecture, Topic 7.6 Lectureworksheet28lec28-slides

Quiz for Unit 7

 

 

  

11

Mon

Mar 28

Lecture 29: Task Affinity and locality. Memory hierarchy

  worksheet29lec29-slides

 

Quiz for Unit 6

  

 

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-slidesQuiz for Unit 8

Quiz for Unit 7

  

12

Mon

Apr 04

Lecture 32

: 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 Demonstration
worksheet32
worksheet31
lec32
lec31-slides

 

Homework 4 Checkpoint-1

  
Homework 5


WS31-solution

12

Mon

Apr 03

Lecture 32

 

Wed

Apr 06

Lecture 33
: Barrier Synchronization with
phasers
PhasersModule 1: Section 3.4Topic 3.4
Lecture 
Lecture,
   Topic
 Topic 3.4 Demonstration
worksheet33
worksheet32
lec33
lec32-slides

 

   

 

Fri

Apr 08



WS32-solution


Wed

Apr 05

Lecture 33

Lecture 34

:  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

worksheet34
worksheet33
lec34
lec33-slides
 

Quiz for Unit 8

  



WS33-solution


Fri

Apr 07

Lecture 34: Fuzzy Barriers with Phasers

Module 1: Section 4.1Topic 4

13

Mon

Apr 11

Lecture 35: Message-Passing programming model with ActorsModule 2: 6.1, 6.2Topic 6
.1 Lecture,
 
Topic
6
4.1 Demonstration
,   Topic 6.2 Lecture, Topic 6.2 Demonstration
worksheet34lec34-slides


WS34-solution

13

Mon

Apr 10

Lecture 35: Eureka-style Speculative Task Parallelism


worksheet35lec35-slides

 

 

   



WS35-solution

WedApr
13
12Lecture 36:
Active Object Module 2: 6.3, 6.4

Topic 6.3 Lecture ,   Topic 6.3 Demonstration ,   Topic 6.4 Lecture, Topic 6.4 Demonstration

Scan Pattern.
Combining Actors with task parallelism
Parallel Prefix Sum


worksheet36lec36-
slides Homework 4 (all)  
slides

WS36-solution
 


FriApr
15 
14Lecture 37:
Eureka-style Speculative Task Parallelism 
Parallel Prefix Sum applications

worksheet37lec37-slides
    




14MonApr
18
17Lecture 38: Overview of other models and frameworks
   



lec38-slides
     





WedApr
20
19Lecture 39: Course Review (Lectures 19-38)
    



lec39-slides
    

Homework 5


FriApr
22
21Lecture 40: Course Review (Lectures 19-38)
   lec40-slides    

Lab Schedule




lec40-slides



Lab Schedule

Lab #

Date (2023)

Topic

Handouts

Examples

1

Jan 09

Infrastructure setup

lab0-handout

lab1-handout


-Jan 16No lab this week (MLK)

2Jan 23Functional Programminglab2-handout

3

Jan 30

Futures

lab3-handout

-Feb 06

Lab #

Date (2021)

Topic

Handouts

Examples

0 Infrastructure Setuplab0-handout 

1

Jan 10

Async-Finish Parallel Programming with abstract metrics

lab1-handout
 -Jan 17   

2

Jan 24

Futures

lab2-handout
 -Jan 31   

3

Feb 07

Cutoff Strategy and Real World Performance

lab3-handout  4

Feb 14

DDFs

lab4-handout  -Feb 21
No lab this week (
Midterm exam
Spring Recess)
 


4

 

Feb 13

Data-

Feb 28   5Mar 07Loop-level Parallelism

Driven Tasks

lab4-handout
-Feb 20No lab this week (Midterm Exam)

5

Feb 27

Async / Finish

lab5-handout
lab5-intro

6Mar
14Isolated Statement and Atomic Variables
06Recursive Task Cutoff Strategylab6-handout
 

-Mar
21
13No lab this week (Spring Break)
   


7Mar
28
20Java Threads
, Java Locks
lab7-handout
 

8

Apr 04

Mar 27Concurrent Lists
Actors
lab8-handout
 

-

Apr 11

Message Passing Interface (MPI)

  

-

Apr 18

Apache Spark

  

-

 

Eureka-style Speculative Task Parallelism

  - 

Java's ForkJoin Framework

 

9Apr 03Actorslab9-handout
10

Apr 10

Loop Parallelism

lab10-handout

-

Apr 17

No lab this week

 



Grading, Honor Code Policy, Processes and Procedures

...

Labs must be submitted by the following Monday Wednesday at 114:59pm30pm.  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.

...