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COMP 322: Fundamentals of Parallel Programming (Spring

...

2022)

 

InstructorInstructors:

Mackale Joyner, DH 2063

Zoran Budimlić, DH 3003

TAs:Elian Ahmar, Timothy Goh, Kelly Park, Tucker Reinhardt, Mantej Singh, Minh Vu, Thanh Vu, Robert Walsh, Frederick Wang, Xincheng Wang, Yidi Wang  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 

 

Piazza site:

https://piazza.com/rice/spring2021spring2022/comp322 (Piazza is the preferred medium for all course communications)

Cross-listing:

ELEC 323

Lecture location:

Fully OnlineHerzstein Amphitheater (online 1st 2 weeks)

Lecture times:

MWF 1:30pm 00pm - 21:25pm50pm

Lab locations:

Fully OnlineKeck 100 (online 1st 2 weeks)

Lab times:

Tu 1Mon  3:30pm 00pm - 23:25pm 50pm (TV, MS, TG, RWAustin, Claire)

Th Wed 4:50pm 30pm - 5:45pm 20pm (XWHunena, TRMantej, KPYidi, YWVictor, FW, EARose, Adrienne, Diep, Maki)

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

...

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:

 

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

Lecture Schedule

 

 

WedFeb 03Topic 2.1 Lecture, Topic 2.1 Demonstration Future Tasks, Functional Parallelism ("Back to the Future")   3Mon Map Reduce4 4    Spring "Sprinkle" Day (no class)lec11lec15 lec17Module 2lec20lec22Spring "Sprinkle" Day (no class) 29 23: Actors (contd)lec23 31  24: Java Threads, Java synchronized statement1 2 Apr 02 25: Java Threads, Java synchronized statement (contd), wait/notify 7 7.2 Lecturelec25lec27 09 28: Safety and Liveness Properties, Java Synchronizers, Dining Philosophers Problemlec29lec30 21 33: Eureka-style Speculative Task Parallelismlec33  23 34Algorithms based on (Scan) operationsQuiz for Unit 8 26 35: TBD

Week

Day

Date (20212022)

Lecture

Assigned Reading

Assigned Videos (see Canvas site for video links)

In-class Worksheets

Slides

Work Assigned

Work Due

 Worksheet Solutions 

1

Mon

Jan 2510

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 2712

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 2914Lecture 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

Feb 01

Lecture 4: Parallel Speedup and Amdahl's Law

Module 1: Section 1.5Topic 1.5 Lecture, Topic 1.5 Demonstration

Jan 17

No class: MLK

        

 

Wed

Jan 19

Lecture 4: Lazy Computation

LazyList.java

Lazy.java

 worksheet4lec4-slidesQuiz for Unit 1  WS4-solution 

 

Fri

Jan 21

Module 1: Section 2.1

Lecture 5:

Java Streams

  worksheet5lec5-slidesHomework 1 WS5-solution 
3FriMonFeb 05Jan 24

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

 

Quiz for Unit 1 WS6-solution 

 

Wed

Jan 26

Feb 08

Lecture 7:

Futures

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

 

 WS7-solution 

 

WedFri

Feb 10Jan 28

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-slidesHomework 2 Homework 1 WS8-solution 

4

Mon

 

Fri

Feb 12

Jan 31 Lecture 9: Java’s Fork/Join LibraryAsync, Finish, Data-Driven Tasks 

Module 1: Section 1.1, 4.5

 

Topic

2

1.

7

1 Lecture, Topic

2

1.

7

1 Demonstration, Topic

2

4.

8

5 Lecture, Topic

2

4.

8

5 Demonstration

worksheet9

lec9-slidesslides Quiz for Unit 2   

comp322-s21-lab6.pdf4

Mon

 

Feb 15No class (weather)        
 WedFeb 17WS9-solution 
 WedFeb 02Lecture 10: Event-based programming model

 

   worksheet10 lec10-slides  WS10-solution 
 FriFeb 19No class (weather) 04Lecture 11: GUI programming as an example of event-based,
futures/callbacks in GUI programming
   worksheet11   lec11-slidesHomework 2Homework 1WS11-solution 
5

Mon

Feb 2207

Lecture 10: Loop-Level Parallelism, Parallel Matrix Multiplication12: Scheduling/executing computation graphs
Abstract performance metrics
Module 1: Sections 3.Section 1, 3.24Topic 31.1 4 Lecture , Topic 3. 1 Demonstration ,  Topic 3.2 Lecture,  Topic 3.2 .4 Demonstrationworksheet10worksheet12lec10lec12-slides   WS12-solution 

 

Wed

Feb 2409

Lecture 11: Iteration Grouping (Chunking), Barrier Synchronization 13: Parallel Speedup, Critical Path, Amdahl's Law

Module 1: Sections 3.3, 3.4Section 1.5

Topic 31.3 5 Lecture , Topic 31.3 Demonstration, Topic 3.4 Lecture  ,   Topic 3.4 Demonstration

worksheet11

5 Demonstration

worksheet13lec13-slides   WS13-solution 

 

Fri

Feb 26 Lecture 12: Data-Driven Tasks11

No class: Spring Recess

 

 

Module 1: Sections 4.5

Topic 4.5 Lecture   Topic 4.5 Demonstration

worksheet12lec12-slides Quiz for Unit 2       
6

Mon

Mar 01

Spring "Sprinkle" Day (no class)

    

Feb 14

Lecture 14: Accumulation and reduction. Finish accumulators

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

 

Wed

Mar
03Feb 16

Lecture 1315: 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 

Recursive Task Parallelism  

  worksheet15lec15-slides

 

 

 WS15-solution 
 FriMar 05Feb 18

Lecture 14: Iterative Averaging Revisited, SPMD pattern16: Data Races, Functional & Structural Determinism

Module 1: Sections 32.5, 32.6Topic 32.5 Lecture ,  Topic 32.5 Demonstration,  Topic 32.6 Lecture,  Topic 32.6 Demonstrationworksheet14 worksheet16 lec14lec16-slidesQuiz for Unit Homework 3  Homework 2WS16-solution 

7

MonMar

08Feb 21

Lecture 1517 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 Demonstrationworksheet15

Midterm Review

   lec17-slides    

 

WedMar

10Feb 23

Lecture 16: Midterm Review

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

  lec16worksheet18lec18-slides   WS18-solution 

 

Fri

Mar
12 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 Demonstrationworksheet17

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

MonMar 15

Feb 28

 

Wed

Mar 17

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

Lecture 18: Abstract vs. Real Performance

  worksheet18lec18-slides   Quiz for Unit 4Quiz for Unit 3  

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        WS20-solution 

 

FriWed

Mar 1902

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 

Quiz for Unit 4

 WS21-solution 

 

Fri

Mar 04

Lecture 22: Parallel Spanning Tree, other graph algorithms 

  worksheet22lec22-slidesHomework 4

Homework 3

WS22-solution 

9

Mon

Mar 2207

Lecture 21: Actors23: Java Threads and Locks

Module 2: 6Sections 7.1, 67.23

Topic 67.1 Lecture,   Topic 6.1 Demonstration ,   Topic 6.2 Lecture, Topic 6.2 Demonstration

worksheet21 lec21-slides Quiz for Unit 5

7.3 Lecture

worksheet23 lec23-slides  

 

WS23-solution 

 

Wed

Mar 2409

Lecture 22: Actors (contd)24: Java Locks - Soundness and progress guarantees  

Module 2: 6.3, 6.47.5Topic 67.3 Lecture, Topic 6.3 Demonstration, Topic 6.4 Lecture , Topic 6.4 Demonstrationworksheet22 5 Lecture worksheet24 lec24-slides 

 

WS24-solution 

 

Fri

Mar 11

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

Homework 3, Checkpoint-1 

WS25-solution 
 

Mon

Mar 14

No class: Spring Break

 

Fri

Mar 26

    

 

  
 WedMar 16No class: Spring Break    

 

   

 

Fri

Mar 18

No class: Spring Break

     

 

  

10

Mon

Mar

21

Lecture

Module 2: 6.5, 6.6Topic 6.5 Lecture, Topic 6.5 Demonstration, Topic 6.6 Lecture, Topic 6.6 Demonstrationworksheet23

26: N-Body problem, applications and implementations 

  worksheet26lec26-slides  

Quiz for Unit 5

 WS26-solution 

 

Wed

Mar

23

Lecture

27: Read-Write Locks, Linearizability of Concurrent Objects

Module 2: 7.13, 7.24Topic 7.3 Lecture, Topic 7.4 Lectureworksheet24worksheet27lec24lec27-slides

Quiz for Unit 6

 

 WS27-solution 

 

Fri

Mar 25

Lecture

28: Message-Passing programming model with Actors

Module 2: 76.1, 76.2Topic 6.1 Lecture, Topic worksheet256.1 Demonstration,   Topic 6.2 Lecture, Topic 6.2 Demonstrationworksheet28lec28-slides

 

 

 

WS28-solution 

11

MonApr

05Mar 28

Lecture 26: Java Locks29: Active Object Pattern. Combining Actors with task parallelism 

Module 2: 76.3, 6.4Topic 76.3 Lectureworksheet26lec26-slides Homework 4 (includes one intermediate checkpoint)Homework 3 (all) , Topic 6.3 Demonstration,   Topic 6.4 Lecture, Topic 6.4 Demonstrationworksheet29lec29-slides

 

 

WS29-solution 

 

WedApr 07

Mar 30

Lecture 27: Linearizability of Concurrent Objects 

Module 2: 7.4Topic 7.4 Lecture worksheet27

30: Task Affinity and locality. Memory hierarchy 

  worksheet30lec30-slides

 

  WS30-solution 

 

Fri

Apr

01

Lecture

 Topic 7.5 Lecture, Topic 7.6 Lectureworksheet28lec28-slides

Quiz for Unit 7

 

 

  

31: 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 1204

Lecture 29: Message Passing Interface (MPI), (start of Module 3) Topic 8.1 Lecture, Topic 8.2 Lecture, Topic 8.3 Lectureworksheet2932: Barrier Synchronization with PhasersModule 1: Section 3.4Topic 3.4 Lecture,  Topic 3.4 Demonstrationworksheet32lec32-slides

 

Quiz for Unit 6

 

WS32-solution 

 

Wed

Apr 1406

Lecture 30: Message Passing Interface (MPI, contd)

 Topic 8.4 Lectureworksheet30

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

 

  WS33-solution 

 

Fri

Apr 1608

Lecture 31: Message Passing Interface (MPI, contd)

  Topic 8.5 Lecture, Topic 8 Demonstration Videoworksheet31lec31-slidesQuiz for Unit 8

Quiz for Unit 7

 

34: Fuzzy Barriers with Phasers

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

 

WS34-solution 

13

Mon

Apr 1911

Lecture 32: Task Affinity with Places35: Eureka-style Speculative Task Parallelism 

 

worksheet32worksheet35lec32lec35-slides

 

Homework 4 Checkpoint-1 

WS35-solution 
 

 

WedApr 13Lecture 36: Scan Pattern. Parallel Prefix Sum 

 

worksheet33worksheet36lec36-slides  WS36-solution 
 FriApr 15Lecture 37: Parallel Prefix Sum applications  worksheet34worksheet37lec34lec37-slides    
14MonApr 18Lecture 38: Overview of other models and frameworks   lec35lec38-slides    
 WedApr 2820Lecture 36: TBD39: Course Review (Lectures 19-38)   lec36lec39-slides Homework 4 (all)   
 FriApr 3022Lecture 3740: Course Review (Lectures 19-3438)   lec37lec40-slides  Homework 5  

Lab Schedule

0  Setup1 26lab1- 2Feb 09lab2- 168-Eureka-style Speculative Task ParallelismJava's ForkJoin Framework

Lab #

Date (20212022)

Topic

Handouts

Examples

1

Jan 10

Infrastructure

setup

lab0-handout

lab1-handout

 
2Jan

Async-Finish Parallel Programming with abstract metrics

17Functional Programminglab2-handout 

3

Feb 02No lab this week

Jan 24

Java Streams

lab3-handout
 
4Jan 31Futureslab4-handout 

5

Feb

No lab this week (classes cancelled)  

3

Feb 23

Cutoff Strategy and Real World Performance

lab3

07

Data-Driven Tasks

lab5-handout 
46

Mar 02

DDFs

lab4

Feb 14

Async / Finish

lab6-handout  
-

Mar 09Feb 21

No lab this week (Midterm exam)

  
-7Mar 16No lab this week (Spring "Sprinkle" Day)  
5Mar 23Loop-level Parallelismlab5-handoutlab5-intro

6

Mar 30

Isolated Statement and Atomic Variables

lab6Feb 28Recursive Task Cutoff Strategylab7-handout 
8Mar 07Java Threadslab8-handout 

-

Apr 06

Mar 14

No lab this week (Spring

"Sprinkle" Day

Break)

  
79Apr 13Java Threads, Java Lockslab7Mar 21Concurrent Listslab9-handout 
10Apr 20Mar 28Actorslab8lab10-handout 
11

 

Message Passing Interface (MPI)

  

-

 

Apache Spark

  

-

 

Apr 04

Loop Parallelism

lab11-handout 

-

Apr 11

No lab this week

  

-

 

Apr 18

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.

...