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COMP 322: Fundamentals of Parallel Programming (Spring

...

2024)

 


Instructors

Instructor:

Mackale Joyner, DH 2063

Zoran Budimlić, DH 3003

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 

 

Alison Qiu, Haotian Dang, Andrew Ondara, Stefan Boskovic, Huzaifa Ali, Raahim Absar

Piazza site:

https://

Piazza site:

https://

piazza.com/rice/

spring2022

spring2024/comp322 (Piazza is the preferred medium for all course communications)

Cross-listing:

ELEC 323

Lecture location:

Herzstein Amphitheater (online 1st 2 weeks)

TBD

Lecture times:

MWF 1:00pm - 1:50pm

Lab locations:

Keck 100 (online 1st 2 weeks

Mon (TBD)

Tue (TBD)

Lab times:

Mon  3:00pm - 3:50pm

(Austin)

Wed 4:30pm - 5:20pm (Claire, Hunena, Mantej, Yidi, Victor, Rose)

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:

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.

...

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. 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.

...

Lecture Schedule

 

 



Week

Day

Date (

2022 

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

10

08

Lecture 1: Introduction

  



worksheet1lec1-
slides
slides  

 

 

  

 



WS1-solution


Wed

Jan

12

10

Lecture 2:  Functional Programming

 

 


worksheet2
lec2 
lec02-slides

 

 

  



WS2-solution

FriJan
14
12Lecture 3: Higher order functions
 


worksheet3 
 worksheet3
lec3-
slides 
slides   
  



WS3-solution

2

Mon

Jan

17

15

No class: MLK

       

 

 










Wed

Jan

19

17

Lecture 4: Lazy Computation
 
 



worksheet4lec4-slides
    


WS4-solution


Fri

Jan 19

 

Fri

Jan 21

Lecture 5: Java Streams

   



worksheet5lec5-slidesHomework 1
  

WS5-solution
3MonJan
24 
22

Lecture 6: Map Reduce with Java Streams

Module 1: Section 2.4Topic 2.4 Lecture, Topic 2.4 Demonstration  worksheet6lec6-slides

 

   



WS6-solution


Wed

Jan

26

24

Lecture 7: Futures

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

 

   

 



WS7-solution


Fri

Jan

28

26

Lecture 8:  Async, Finish, Computation Graphs

, Ideal Parallelism

Module 1: Sections 1.
2
1, 1.
3
2Topic 1.
2
1 Lecture, Topic 1.
2
1 Demonstration, Topic 1.
3
2 Lecture, Topic 1.
3
2 Demonstrationworksheet8
lec8 
lec8-slides
   


WS8-solution

4

Mon

 


Jan
31
29 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
  
Jan 31Lecture
 WedFeb 02Lecture
10: Event-based programming model

 

  




worksheet10lec10-slides
     

Homework 1WS10-solution

FriFeb
04  worksheet11
02Lecture 11: GUI programming
as an example of event-based,
futures/callbacks in GUI programming
, Scheduling/executing computation graphs

Module 1: Section 1.4Topic 1.4 Lecture , Topic 1.4 Demonstrationworksheet11
lec11-slidesHomework 2
   

WS11-solution
5

Mon

Feb

07

05

Lecture 12:
Scheduling/executing computation graphs
Abstract performance metrics, Parallel Speedup, Amdahl's Law Module 1: Section 1.
4
5Topic 1.
4
5 Lecture , Topic 1.
4
5 Demonstrationworksheet12lec12-slides
    

 



WS12-solution


Wed

Feb

09

07

Lecture 13:

Lightweight task parallelism

Accumulation and reduction. Finish

/async

accumulators

Module 1: Section
1
2.
1
3

Topic

1

2.

1

3 Lecture

,

  Topic

1

2.

1

 

3 Demonstration

worksheet13lec13-slides 
   

WS13-solution


Fri

Feb

11

09

No class: Spring Recess

 

        










6

Mon

Feb

14

12

Lecture 14:

Parallel Speedup, Critical Path, Amdah's Law

Data Races, Functional & Structural Determinism

Module 1:
Section 1
Sections 2.5, 2.6Topic
1
2.5 Lecture ,  Topic
1 
2.5 Demonstration,  Topic 2.6 Lecture,  Topic 2.6 Demonstrationworksheet14lec14-slides
    


WS14-solution


Wed

Feb
16
14

Lecture 15:

Recursive Task Parallelism   

Limitations of Functional parallelism.
Abstract vs. real performance. Cutoff Strategy



worksheet15lec15-slides

 

 

   



Homework 2WS15-solution
 


FriFeb
18
16

Lecture 16:

Accumulation and reduction. Finish accumulatorsModule 1: Section 2.3Topic 2.3 Lecture , Topic 2.3 Demonstration 

Recursive Task Parallelism  



worksheet16 lec16-slidesHomework 3
  

WS16-solution

7

Mon

Feb

21

19

Lecture 17: Midterm Review

   lec17-slides    

 

Wed

Feb 23

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

  



lec17-slides




Wed

Feb 21

Lecture 18: Midterm Review

 




lec18-slides
  
  

 






Fri

Feb
25 
23 

Lecture 19:Data

Races, Functional & Structural Determinism

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

Module 1: Sections
2
3.
5
1, 3.2, 3.
6
3Topic
2
3.
5 Lecture
1 Lecture, Topic 3.1 Demonstration , Topic 3.2 Lecture,  Topic 3.
5
2 Demonstration, Topic
2
3.
6
3 Lecture,
Topic 2
 Topic 3.
6
3 Demonstrationworksheet19lec19-slides
    


WS19-solution

8

Mon

Feb

28

26

Lecture 20:

Confinement & Monitor Pattern. Critical sections
Global lock

Barrier Synchronization with Phasers

Module
2
1: Sections
5.1, 5.2, 5.6
3.4 Topic
5
3.
1
4 Lecture, Topic
5.1 Demonstration, Topic 5.2 Lecture, Topic 5.2 Demonstration, Topic 5.6 Lecture, Topic 5.6 Demonstration
3.4 Demonstrationworksheet20
lec20 
lec20-slides      
    


WS20-solution


Wed

Mar 02

Feb 28

Lecture 21:

N-Body problem, applications and implementations

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 Demonstrationworksheet21lec21-slides

WS21-solution


Fri

Mar 01

Lecture 22: Fuzzy Barriers with Phasers

Module 1: Section 4.1 Topic 4.1 Lecture, Topic 4.1 Demonstrationworksheet22lec22-slides


WS22-solution

9

Mon

Mar 04

Lecture 23:  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

Homework 3 (CP 1)

WS23-solution


Wed

Mar 06

Lecture 24: Confinement & Monitor Pattern. Critical sections
Global lock

Module 2: Sections 5.1, 5.2Topic 5.1 Lecture, Topic 5.1 Demonstration, Topic 5.2 Lecture, Topic 5.2 Demonstration, Topic 5.6 Lecture, Topic 5.6 Demonstrationworksheet24 lec24-slides


WS24-solution


Fri

Mar 08

 Lecture 25:  Atomic variables, Synchronized statementsModule 2: Sections 5.4, 7.2Topic 5.4 Lecture, Topic 5.4 Demonstration, Topic 7.2 Lecture worksheet25lec25-slides


WS25-solution

Mon

Mar 11

No class: Spring Break


 






WedMar 13No class: Spring Break








Fri

Mar 15

No class: Spring Break









10

Mon

Mar 18

Lecture 26: Parallel Spanning Tree, other graph algorithms



worksheet26lec26-slides

WS26-solution


Wed

Mar 20

Lecture 27: Java Threads and Locks

Module 2: Sections 7.1, 7.3Topic 7.1 Lecture, Topic 7.3 Lectureworksheet27lec27-slides


Homework 3 (CP 2)WS27-solution


Fri

Mar 22

Lecture 28: Java Locks - Soundness and progress guarantees

Module 2: Section 7.5Topic 7.5 Lectureworksheet28lec28-slides




WS28-solution

11

Mon

Mar 25

Lecture 29:  Dining Philosophers Problem

Module 2: Section 7.6Topic 7.6 Lectureworksheet29lec29-slides



WS29-solution


Wed

Mar 27

Lecture 30: Read-Write Locks, Linearizability of Concurrent Objects

Module 2: Sections 7.3, 7.4Topic 7.3 Lecture, Topic 7.4 Lectureworksheet30lec30-slides



WS30-solution


Fri

Mar 29

Lecture 31: Message-Passing programming model with Actors

Module 2: Sections 6.1, 6.2Topic 6.1 Lecture, Topic 6.1 Demonstration,   Topic 6.2 Lecture, Topic 6.2 Demonstrationworksheet31lec31-slides


WS31-solution

12

Mon

Apr 01

Lecture 32: Active Object Pattern. Combining Actors with task parallelismModule 2: Sections 6.3, 6.4Topic 6.3 Lecture, Topic 6.3 Demonstration,   Topic 6.4 Lecture, Topic 6.4 Demonstrationworksheet32lec32-slides

Homework 4

Homework 3 (All)

WS32-solution


Wed

Apr 03

Lecture 33: Task Affinity and locality. Memory hierarchy



worksheet33lec33-slides



WS33-solution


Fri

Apr 05

Lecture 34: Eureka-style Speculative Task Parallelism

 
worksheet34lec34-slides


WS34-solution

13

Mon

Apr 08

No class: Solar Eclipse









WedApr 10Lecture 35: Scan Pattern. Parallel Prefix Sum


worksheet35lec35-slides
Homework 4 (CP 1)WS35-solution

FriApr 12Lecture 36: Parallel Prefix Sum applications

worksheet36lec36-slides

WS36-solution
14MonApr 15Lecture 37: Overview of other models and frameworks


lec37-slides




WedApr 17Lecture 38: Course Review (Lectures 19-34)
 
lec38-slides
Homework 4 (All)


FriApr 19Lecture 39: Course Review (Lectures 19-34)


lec39-slides
  worksheet21lec21-slides    

 

Fri

Mar 04

Lecture 22: Fork/Join programming model. OS Threads. Scheduler Pattern

Module 2: Sections 2.7, 2.8Topic 2.7 Lecture, Topic 2.7 Demonstration, Topic 2.8 Lecture, Topic 2.8 Demonstration, worksheet22lec22-slidesHomework 4

 

  

9

Mon

Mar 07

Lecture 23: Locks, Atomic variables

Module 2: 7.3

Topic 7.3 Lecture

worksheet23 lec23-slides  

 

  

 

Wed

Mar 09

Lecture 24: Parallel Spanning Tree, other graph algorithms

  worksheet24 lec24-slides 

 

  

 

Fri

Mar 11

 Lecture 25: Linearizability of Concurrent ObjectsModule 2: 7.4Topic 7.4 Lectureworksheet25lec25-slides 

 

   

Mon

Mar 14

No class: Spring Break

     

 

   WedMar 16No class: Spring Break    

 

   

 

Fri

Mar 18

No class: Spring Break

     

 

  

10

Mon

Mar 21

Lecture 26: Java Locks - Soundness and progress guarantees

Module 2: 7.5Topic 7.5 Lecture worksheet26lec26-slides     

 

Wed

Mar 23

Lecture 27: Dining Philosophers Problem

Module 2: 7.6Topic 7.4 Lecture Topic 7.6 Lectureworksheet27lec27-slides

 

   

 

Fri

Mar 25

Lecture 28: Read-Write Pattern. Read-Write Locks. Fairness & starvation

Module 2: 7.3, 7.5Topic 7.3 Lecture, Topic 7.5 Lecture, worksheet28lec28-slides

 

 

 

  

11

Mon

Mar 28

Lecture 29: Task Affinity and locality. Memory hierarchy

  worksheet29lec29-slides

 

 

  

 

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-slidesHomework 5

 

  

12

Mon

Apr 04

Lecture 32: Data-Parallel Programming model. Loop-Level Parallelism, Loop ChunkingModule 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 Demonstrationworksheet32lec32-slides

 

 

  

 

Wed

Apr 06

Lecture 33: Barrier Synchronization with phasers

Module 1: Section 3.4

Topic 3.4 Lecture ,   Topic 3.4 Demonstration

worksheet33lec33-slides

 

   

 

Fri

Apr 08

Lecture 34:  Stencil computation. 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 Demonstrationworksheet34lec34-slides 

 

  

13

Mon

Apr 11

Lecture 35: Message-Passing programming model with ActorsModule 2: 6.1, 6.2

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

worksheet35lec35-slides

 

 

   WedApr 13Lecture 36: Active Object Pattern. Combining Actors with task parallelismModule 2: 6.3, 6.4

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

worksheet36lec36-slides     FriApr 15Lecture 37: Eureka-style Speculative Task Parallelism  worksheet37lec37-slides    14MonApr 18Lecture 38: Overview of other models and frameworks   lec38-slides     WedApr 20Lecture 39: Course Review (Lectures 19-38)   lec39-slides     FriApr 22Lecture 40: Course Review (Lectures 19-38)   lec40-slides    





Lab Schedule

Lab #

Date (

2022

2023)

Topic

Handouts

Examples

1

Jan

10

08

Infrastructure setup

lab0-handout

lab1-handout

 

-Jan
17
15No lab this week (MLK)

2Jan 22Functional Programminglab2-handout

3

Jan 29

Futures

lab3-handout

4Feb 05Data-Driven Taskslab4-handout

5

Feb 12

Async / Finish

lab5-handout
-Feb 19No lab this week (Midterm Exam)

6

Feb 26

Loop Parallelism 

lab6-handoutimage kernels
7Mar 04Recursive Task Cutoff Strategylab7-handout
-Mar 11
   

-

Jan 24

 

 -Jan 31   

-

Feb 07

 

  -

Feb 14

 

  -

Feb 21

 

  -Feb 28   -Mar 07   

-

Mar 14
No lab this week (Spring Break)
  


-Mar
21
18
   -Mar 28   -

Apr 04

 

  

-

Apr 11

 

  

-

Apr 18

 

 
Java Threadslab8-handout
8Mar 25Concurrent Listslab9-handout
9Apr 01Actorslab10-handout
10

Apr 08

No lab this week (Solar Eclipse)



-

Apr 15

No lab this week

 



Grading, Honor Code Policy, Processes and Procedures

...

Labs must be submitted by the following Monday at 11:59pm3pm.  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.

...