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

Piazza site:

https://piazza.com/rice/spring2024

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

 

Piazza site:

https://piazza.com/rice/spring2022

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

Cross-listing:

ELEC 323

Lecture location:

Herzstein

Amphitheater (online 1st 2 weeks)

Amp

Lecture times:

MWF 1:00pm - 1:50pm

Lab locations:

Keck 100 (online 1st 2 weeks

Mon (Brockman 101)

Tue (Herzstein Amp)

Lab times:

Mon  3:00pm - 3:50pm (

Austin

SB, HA, AO)

Wed

Tue   4:00pm - 4:

30pm - 5:20pm (Claire, Hunena, Mantej, Yidi, Victor, Rose, Adrienne, Diep, Maki

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:

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



Wed

Jan 10

Jan 12

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 17

Lecture 4: Lazy Computation



worksheet4lec4-slides

WS4-solution

 

Wed

Jan 19

Lecture 4: Lazy Computation  worksheet4lec4-slides     


Fri

Jan

21

19

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 24

 

Wed

Jan 26

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
Feb 02
Jan 31Lecture 10: Event-based programming model

 

  




worksheet10lec10-slides
     

Homework 1WS10-solution

FriFeb
04
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 Demonstration
   
worksheet11lec11-slidesHomework 2
Homework 1 

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

       6

Mon

Feb 14

Lecture 14: Parallel Speedup, Critical Path, Amdah's Law

Feb 12

Lecture 14: 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
worksheet14lec14-slides     


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
Homework 2 

WS16-solution

7

Mon

Feb

21

19

Lecture 17: Midterm Review

   




lec17-slides
  
  

 






Wed

Feb

23   

21

Lecture 18:

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

Midterm Review




lec18-slides
    

 






Fri

Feb
25 
23 

Lecture 19:

Data Races, Functional & Structural DeterminismModule 1: Sections 2.5, 2.6

 Fork/Join programming model. OS Threads. Scheduler Pattern


Topic 2.
5
7 Lecture, Topic 2.
5
7 Demonstration, Topic 2.
6
8 Lecture, Topic 2.
6  
8 Demonstrationworksheet19lec19-slides
   


WS19-solution

8

Mon

Feb
28
26 

Lecture 20:

Confinement & Monitor Pattern. Critical sections
Global lock
Module 2: Sections 5.1, 5.2, 5.6 Topic 5

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

Module 1: Sections 3.1, 3.2, 3.3Topic 3.1 Lecture, Topic
5
3.1 Demonstration , Topic
5
3.2 Lecture,
Topic 5
 Topic 3.2 Demonstration, Topic
5
3.
6
3 Lecture,
Topic 5
 Topic 3.
6
3 Demonstrationworksheet20
lec20
lec20-slides
  
 
    


WS20-solution


Wed

Feb 28

 

Wed

Mar 02

Lecture 21:

N-Body problem, applications and implementations  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,  

Barrier Synchronization with Phasers

Module 1: Sections 3.4 Topic 3.4 Lecture, Topic 3.4 Demonstrationworksheet21    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 Demonstrationworksheet22lec22-slides
Homework 4

Homework 3

 


WS22-solution

9

Mon

Mar

07

04

Lecture 23:

 Locks, Atomic variables

Fuzzy Barriers with Phasers

Module
2
1:
7
Section 4.
3
1 Topic 4.1 Lecture, Topic 4.1 Demonstration
Topic 7.3 Lecture
worksheet23lec23-slides
 

 

  

Homework 3 (CP 1)

WS23-solution
 


Wed

Mar

09

06

Lecture 24:

Parallel Spanning Tree, other graph algorithms

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 Demonstration
  
worksheet24 lec24-slides
 

 

  

 



WS24-solution


Fri

Mar

11

08

 Lecture 25:
Linearizability of Concurrent Objects
 Atomic variables, Synchronized statementsModule 2: Sections 5.4, 7.
4
2Topic
7
5.4 Lecture, Topic 5.4 Demonstration, Topic 7.2 Lecture worksheet25
lec25 
lec25-slides
 

 

  


WS25-solution

Mon

Mar
14 WedMar 16
11

No class: Spring Break


 
    

 

  







WedMar 13
No class: Spring Break
    

 

    









Fri

Mar

18

15

No class: Spring Break

    

 

 

  








10

Mon

Mar

21

18

Lecture 26: Java

Locks - Soundness and progress guarantees

Threads and Locks

Module 2: Sections 7.1, 7.3
Module 2: 7.5
Topic 7.
5  
1 Lecture, Topic 7.3 Lectureworksheet26lec26-slides
    


WS26-solution


Wed

Mar

23

20

Lecture 27:

Dining Philosophers Problem

Read-Write Locks,  Soundness and progress guarantees

Module 2: Section 7.
6
3Topic 7.
4
3 Lecture, Topic 7.
6
5 Lectureworksheet27lec27-slides

 

   


Homework 3 (CP 2)WS27-solution
 


Fri

Mar

25

22

Lecture 28:

Read-Write Pattern. Read-Write Locks. Fairness & starvationModule 2: 7.3, 7.5

Dining Philosophers Problem


Topic 7.
3 Lecture, Topic 7.5 Lecture,
6 Lectureworksheet28lec28-slides

 

 

 

  




WS28-solution

11

Mon

Mar

28  

25

Lecture 29:

Task Affinity and locality. Memory hierarchy

 Linearizability of Concurrent Objects

Module 2: Sections 7.4Topic 7.4 Lecture
 
worksheet29lec29-slides

 

 

  



WS29-solution


Wed

Mar

30

27

Lecture 30:

Reactor Pattern. Web servers 

 Parallel Spanning Tree, other graph algorithms

 
worksheet30lec30-slides
 



WS30-solution
 


Fri

  

 

Fri

Apr 01

Lecture 31: Scan Pattern. Parallel Prefix Sum, uses and algorithms

   

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
Homework 5

Homework 4

 


WS31-solution

12

Mon

Apr

04

01

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,
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
Topic 3.3 Lecture,  Topic 3.3
Demonstrationworksheet32lec32-slides

 

 

  

Homework 4

Homework 3 (All)

WS32-solution
 


Wed

Apr

06

03

Lecture 33:

Barrier Synchronization with phasersModule 1: Section 3.4

Topic 3.4 Lecture ,   Topic 3.4 Demonstration

 

Task Affinity and locality. Memory hierarchy



worksheet33lec33-slides

 

   



WS33-solution


Fri

Apr

08

05

Lecture 34:

  Stencil computation. Point-to-point Synchronization with PhasersModule 1: Section 4.2, 4.3Topic 4.2 Lecture ,   Topic 4.2 Demonstration, Topic 4.3 Lecture,  Topic 4.3 Demonstration

Eureka-style Speculative Task Parallelism

 
worksheet34lec34-slides
 


WS34-solution
 

13

 

Mon

 

Apr 08

No class: Solar Eclipse









WedApr 10

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

 

 

  
Scan Pattern. Parallel Prefix Sum


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

FriApr 12Lecture 36: Parallel Prefix Sum applications

worksheet36lec36-slides

WS36-solution
 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
18
15Lecture
38lec38
37: Overview of other models and frameworks
   



lec37-slides
   
  





WedApr
20
17Lecture
39
38: Course Review (Lectures 19-
38lec39
34)
 
  

lec38-slides
     

Homework 4 (All)


FriApr
22
19Lecture
40
39: Course Review (Lectures 19-
38lec40
34)
   



lec39-slides
 Homework 5  





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

-

Feb 12

No lab this week



-Feb 19No lab this week (Midterm Exam)

5

Feb 26

Loop Parallelism 

lab5-handoutimage kernels
6Mar 04Recursive Task Cutoff Strategylab6-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)
  


7Mar
21
18
   -Mar 28   -

Apr 04

 

 
Java Threadslab7-handout
8Mar 25Concurrent Listslab8-handout
9Apr 01Actorslab9-handout
 

-

Apr

11

08

No lab this week (Solar Eclipse)

 

  



-

Apr

18

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.

...