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COMP 322: Fundamentals of Parallel Programming (Spring

...

2023)

 

InstructorsInstructor:

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/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)TBD

Lab times:

Mon  3:00pm - 3:50pm ()

Wed Tue 4:30pm 00pm - 54:20pm 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 , MapReduce

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.

...

  • Module 1 handout (Parallelism)
  • Module 2 handout (Concurrency)There is no lecture handout for Module 3 (Distribution and Locality).  The instructor will refer you to optional resources to supplement the lecture slides and videos.

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:

Lecture Schedule

 

 

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

Lecture Schedule

 

 

Topic 1.1 Lecture, Topic 1.1 Demonstration lec1-slideslec5lec8 lec11lec15Topic 6.1 Lecture ,   Topic 6.1 Demonstration ,   Topic 6.2 Quiz for Unit 6

WeekWeek

Day

Date (2022)

Lecture

Assigned Reading

Assigned Videos (see Canvas site for video links)

In-class Worksheets

Slides

Work Assigned

Work Due

 Worksheet Solutions 

1

Mon

Jan 1009

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

Module 1: Section 1.1worksheet1

Introduction

 

 

worksheet1lec1-slides  

 

 

 
WS1-solution 

 

Wed

Jan 1211

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 1Functional Programming

GList.java worksheet2lec02-slides

 

 

WS2-solution 
 FriJan 1413Lecture 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 1716

Lecture 4: Parallel Speedup and Amdahl's Law

Module 1: Section 1.5Topic 1.5 Lecture, Topic 1.5 Demonstrationworksheet4lec4-slidesQuiz for Unit 1

No class: MLK

        

 

Wed

Jan 1918

Lecture 5: Future Tasks, Functional Parallelism ("Back to the Future")Module 1: Section 2.1Topic 2.1 Lecture, Topic 2.1 Demonstrationworksheet54: Lazy Computation

LazyList.java

Lazy.java

 worksheet4lec4-slides   WS4-solution 

 

Fri

Jan 2120

Lecture 65: Java Streams

  Finish AccumulatorsModule 1: Section 2.3Topic 2.3 Lecture, Topic 2.3 Demonstrationworksheet6lec6-slides Quiz for Unit 1  worksheet5lec5-slidesHomework 1 WS5-solution 
3MonJan 2423

Lecture 76: Map Reduce with Java Streams

Module 1: Section 2.4Topic 2.4 Lecture, Topic 2.4 Demonstration  worksheet7worksheet6lec7lec6-slides

 

  WS6-solution 

 

Wed

Jan 2625

Lecture 8: Data Races, Functional & Structural Determinism7: Futures

Module 1: Section 2.5, 2.61Topic 2.5 1 Lecture , Topic 2.5 Demonstration, Topic 2.6 Lecture, Topic 2.6 Demonstration   worksheet81 Demonstrationworksheet7lec7-slides

Homework 2 

Homework 1  WS7-solution 

 

Fri

Jan 2827

 

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 

8:  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 Demonstrationworksheet8lec8-slides  WS8-solution 

4

Mon

 

Jan 3130    Lecture 9: Async, Finish, Data-Driven Tasks 

Module 1: Section 1.1, 4.5

 

Topic 1.1 Lecture, Topic 1.1 Demonstration, Topic 4.5 Lecture, Topic 4.5 Demonstration

worksheet9

lec9-slides    WS9-solution 
 WedFeb 0201Lecture 10: Event-based programming model

 

   worksheet10 lec10-slides   Homework 1WS10-solution 
 FriFeb 04  03Lecture 11: GUI programming as an example of event-based,
futures/callbacks in GUI programming
    worksheet11lec11-slidesHomework 2  WS11-solution 
5

Mon

Feb 0706

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 31.1 Demonstration ,  Topic 3.2 Lecture,  Topic 3.2 Demonstrationworksheet10lec104 Demonstrationworksheet12lec12-slides   WS12-solution 

 

Wed

Feb 0908

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 11 Lecture 12: Data-Driven Tasks10

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

Feb 14

 

    

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 1615 

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 
 FriFeb 1817

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

Mon

Feb 2120

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    

 

Wed

Feb 2322

Lecture 16: Midterm Review18: Limitations of Functional parallelism.
Abstract vs. real performance. Cutoff Strategy

   worksheet18lec16lec18-slides   WS18-solution 

 

Fri

Feb 25 24 

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)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 Demonstrationworksheet19worksheet20lec19lec20-slides         WS20-solution 

 

FriWed

Mar 0401

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 5.5 Lecture, Topic 5.5 Demonstrationworksheet20lec20-slides 

Quiz for Unit 4

 7.2 Lectureworksheet21lec21-slides  WS21-solution 

 

Fri

Mar 03

Lecture 22: Parallel Spanning Tree, other graph algorithms 

  worksheet22lec22-slidesHomework 4

Homework 3

WS22-solution 

9

Mon

Mar 0706

Lecture 21: Actors23: Java Threads and Locks

Module 2: 6Sections 7.1, 6.27.3

Topic 7.1 Lecture, Topic 67.2 Demonstration3 Lecture

worksheet21 worksheet23 lec21lec23-slides Quiz for Unit 5 

 

 
WS23-solution 

 

Wed

Mar 0908

 

Lecture 24: Parallel Spanning Tree, other graph algorithms

 

Java Locks - Soundness and progress guarantees  

Module 2: 7.5Topic 7.5 Lecture worksheet24 lec24-slides 

 

Homework 3, Checkpoint-1

 WS24-solution 

 

Fri

Mar 1110

 Lecture 25: Linearizability of Concurrent Objects Dining Philosophers Problem  Module 2: 7.46Topic 7.4 6 Lectureworksheet25lec25-slidesQuiz for Unit 6

Quiz for Unit 5

  

 

WS25-solution 
 

Mon

Mar 1413

No class: Spring Break

     

 

  
 WedMar 1615No class: Spring Break    

 

   

 

Fri

Mar 1817

Lecture 25: Java Threads, Java synchronized statement, wait/notify

Module 2: 7.1, 7.2Topic 7.4 Lecture

No class: Spring Break

     

 

  

10

Mon

Mar 2120

Topic 7.5 Lecture

Lecture 26: Java Locks - Soundness and progress guarantees

Module 2: 7.3

N-Body problem, applications and implementations 

  worksheet26lec26-slides Homework 4 (includes one intermediate checkpoint)Homework 3 (all)   WS26-solution 

 

Wed

Mar 2322

Lecture 27: Dining Philosophers Problem Read-Write Locks, Linearizability of Concurrent Objects

Module 2: 7.63, 7.4Topic 7.4 3 Lecture, Topic 7.6 4 Lectureworksheet27lec27-slides

 

  WS27-solution 

 

Fri

Mar 2524

Lecture 28: Read-Write Pattern. Read-Write Locks. Fairness & starvation Message-Passing programming model with Actors

Module 2: 76.31, 76.52Topic 76.3 1 Lecture, Topic 7.5 Lecture, 6.1 Demonstration,   Topic 6.2 Lecture, Topic 6.2 Demonstrationworksheet28lec28-slides

Quiz for Unit 7 

 

 

 
WS28-solution 

11

Mon

Mar 2827

 

Lecture 29: Task Affinity and locality. Memory hierarchy

 

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 3029

Lecture 30: Reactor Pattern. Web servers Task Affinity and locality. Memory hierarchy 

  worksheet30lec30-slides

 

   

 

Fri

Apr 01

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

  worksheet31lec31-slidesQuiz for Unit 8Quiz for Unit 7WS30-solution 

 12

FriMon

Mar 31

Apr 04

Lecture

32

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 Demonstrationworksheet32worksheet31lec32lec31-slidesHomework 5

Homework 4

WS31-solution 

Homework 4 Checkpoint-1

 

12

Mon

Apr 03

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

 

 

WS32-solution 

 

Wed

Apr 0605

Lecture 33: Barrier   Stencil computation. Point-to-point Synchronization with phasersPhasers

Module 1: Section 3Section 4.2, 4.3

Topic 4.2 Lecture, Topic 34.4 Lecture ,   Topic 3.4 2 Demonstration, Topic 4.3 Lecture,  Topic 4.3 Demonstration

worksheet33lec33-slides

 

  WS33-solution 

 

Fri

Apr 0807

Lecture 34:  Stencil computation. Point-to-point Synchronization  Fuzzy Barriers with Phasers

Module 1: Section 4.2, 4.3Section 4.1Topic 4.2 1 Lecture,   Topic 4.2 Demonstration, Topic 4.3 Lecture,  Topic 4.3 Demonstration1 Demonstrationworksheet34lec34-slides 

Quiz for Unit 8

 

 

WS34-solution 

13

Mon

Apr 1110

Lecture 35: Message-Passing programming model with ActorsModule 2: 6.1, 6.2Topic 6.1 Lecture ,   Topic 6.1 Demonstration ,   Topic 6.2 Lecture, Topic 6.2 DemonstrationEureka-style Speculative Task Parallelism 

 

worksheet35lec35-slides

 

 

 
WS35-solution 
 WedApr 1312Lecture 36: Active Object Scan Pattern. Combining Actors with task parallelismModule 2: 6.3, 6.4Topic 6.3 Lecture ,   Topic 6.3 Demonstration ,   Topic 6.4 Lecture, Topic 6.4 DemonstrationParallel Prefix Sum 

 

worksheet36lec36-slides Homework 4 (all)  WS36-solution 
 FriApr 1514Lecture 37: Eureka-style Speculative Task Parallelism Parallel Prefix Sum applications  worksheet37lec37-slides    
14MonApr 1817Lecture 38: Overview of other models and frameworks   lec38-slides    
 WedApr 2019Lecture 39: Course Review (Lectures 19-38)   lec39-slides    
 FriApr 2221Lecture 40: Course Review (Lectures 19-38)   lec40-slides  Homework 5  

Lab Schedule

0  Setup 24Futures- 31 3 07lab3- 28lab5lab5-intro- - 18 - Java's ForkJoin Framework

Lab #

Date (20212023)

Topic

Handouts

Examples

1

Jan 09

Infrastructure

setup

lab0-handout

 

1

Jan 10

Async-Finish Parallel Programming with abstract metrics

lab1-handout

 
-Jan 17 16No lab this week (MLK)  
2Jan 23Functional Programminglab2-handout 

3

Jan

30

 

Java Streams

lab3-handout
 
4Feb

Cutoff Strategy and Real World Performance

06Futureslab4-handout 
4

5

Feb 14

DDFs

lab4-handout 

13

Data-Driven Tasks

lab5-handout 
-Feb 2120No lab this week (Midterm exam)  
6

Feb

   5Mar 07Loop-level Parallelism

27

Async / Finish

lab6-handout 
67Mar 14

Isolated Statement and Atomic Variables

lab606Recursive Task Cutoff Strategylab7-handout 
-Mar 21 13No lab this week (Spring Break)  
78Mar 2820Java Threads, Java Lockslab7-handout 8

Apr 04

Actors

lab8-handout 
9

Apr 11

Message Passing Interface (MPI)

Mar 27Concurrent Listslab9-handout 
10Apr 03

Apache Spark

Actorslab10-handout 
11

 

Eureka-style Speculative Task Parallelism

Apr 10

Loop Parallelism

lab11-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.

...