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Instructors:

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 

 

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)

Lecture times:

MWF 1:00pm - 1:50pm

Lab locations:

Keck 100 (online 1st 2 weeks)

Lab times:

Mon  3:00pm - 3:50pm (Austin, Claire)

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

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.

...

lec33

Week

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 10

Lecture 1: Introduction

 

 

worksheet1lec1-slidesslides  

 

 

 
WS1-solution 

 

Wed

Jan 12

Lecture 2:  Functional Programming

 GList.java worksheet2lec2lec02-slides

 

 

 
WS2-solution 
 FriJan 14Lecture 3: Higher order functions  worksheet3worksheet3 lec3-slidesslides   

 

 WS3-solution 

2

Mon

Jan 17

No class: MLK

        

 

Wed

Jan 19

Lecture 4: Lazy Computation  

LazyList.java

Lazy.java

 worksheet4lec4-slides   WS4-solution 

 

Fri

Jan 21

Lecture 5: Java Streams

  worksheet5lec5-slidesHomework 1  WS5-solution 
3MonJan 24

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

Lecture 7: Futures

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

 

  WS7-solution 

 

Fri

Jan 28

Lecture 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 31 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 02Lecture 10: Event-based programming model

 

  worksheet10lec10-slides   WS10-solution 
 FriFeb 04Lecture 11: GUI programming as an example of event-based,
futures/callbacks in GUI programming
  worksheet11lec11-slidesHomework 2  Homework 1WS11-solution 
5

Mon

Feb 07

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 09

Lecture 13: Lightweight task parallelism. Finish/async Parallel Speedup, Critical Path, Amdahl's Law

Module 1: Section 1.15

Topic 1.1 5 Lecture , Topic 1.1 5 Demonstration

worksheet13lec13-slides   WS13-solution 

 

Fri

Feb 11

No class: Spring Recess

 

        
6

Mon

Feb 14

Lecture 14: Parallel Speedup, Critical Path, Amdah's Law Accumulation and reduction. Finish accumulators

Module 1: Section 12.53Topic 12.5 3 Lecture   Topic 12.5 3 Demonstrationworksheet14lec14-slides   WS14-solution 

 

Wed

Feb 16

Lecture 15: Recursive Task Parallelism  

  worksheet15lec15-slides

 

 

  WS15-solution 
 FriFeb 18

Lecture 16: Accumulation and reduction. Finish accumulatorsData Races, Functional & Structural Determinism

Module 1: Section Sections 2.35, 2.6Topic 2.3 5 Lecture ,  Topic 2.3 Demonstration5 Demonstration,  Topic 2.6 Lecture,  Topic 2.6 Demonstrationworksheet16 lec16-slidesHomework 3  Homework 2WS16-solution 

7

Mon

Feb 21

Lecture 17: Midterm Review

   lec17-slides    

 

Wed

Feb 23

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

   worksheet18lec18-slides   WS18-solution 

 

Fri

Feb 25 Topic 2.5

Lecture 19: Data Races, Functional & Structural Determinism

Module 1: Sections 2.5, 2.6

Fork/Join programming model. OS Threads. Scheduler Pattern 

 Topic 2.7 Lecture, Topic 2.5 7 Demonstration, Topic 2.6 8 Lecture, Topic 2.6 8 Demonstration, worksheet19lec19-slides   WS19-solution 

8

Mon

Feb 28

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 Demonstrationworksheet20lec20lec20-slides         WS20-solution 

 

Wed

Mar 02

 

Lecture 21: N-Body problem, applications and implementations

 

  Atomic variables, Synchronized statements

Module 2: Sections 5.4, 7.2

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

 

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, 

Parallel Spanning Tree, other graph algorithms 

  worksheet22lec22-slidesHomework 4

 

 

Homework 3

WS22-solution 

9

Mon

Mar 07

Lecture 23:  Locks, Atomic variables 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 09

Lecture 24: Parallel Spanning Tree, other graph algorithms

  

Java Locks - Soundness and progress guarantees  

Module 2: 7.5Topic 7.5 Lecture worksheet24 lec24-slides 

 

 
WS24-solution 

 

Fri

Mar 11

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

 

 
WS25-solution 
 

Mon

Mar 14

No class: Spring Break

     

 

  
 WedMar 16No class: Spring Break    

 

   

 

Fri

Mar 18

No class: Spring Break

     

 

  

10

Mon

Mar 21

Topic 7.5 Lecture

Lecture 26: Java Locks - Soundness and progress guarantees

Module 2: 7.5

N-Body problem, applications and implementations 

  worksheet26lec26-slides    WS26-solution 

 

Wed

Mar 23

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 25

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

 

 

 

 
WS28-solution 

11

Mon

Mar 28

 

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 30

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

  worksheet30lec30-slides

 

  WS30-solution 

 

Fri

Apr 01

12

Mon

Apr 04

Lecture 32:

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

  worksheet31lec31-slidesHomework 5

 

  

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-slides Homework 5

 

 

Homework 4

WS31-solution 

 12

WedMon

Apr 0604

Lecture 3332: Barrier Synchronization with phasersPhasersModule 1: Section 3.4Topic 3.4 Lecture Lecture,    Topic  Topic 3.4 Demonstrationworksheet33worksheet32lec32-slides

 

 

 
WS32-solution 

 

FriWed

Apr 0806

Lecture 3433:  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

worksheet34worksheet33lec34lec33-slides

 

 WS33-solution 

 13

Fri

Mon

Apr 1108

Lecture

35: Message-Passing programming model with Actors
Module 2: 6.1, 6.2

Topic 634: Fuzzy Barriers with Phasers

Module 1: Section 4.1Topic 4.1 Lecture,   Topic 64.1 Demonstration ,   Topic 6.2 Lecture, Topic 6.2 Demonstrationworksheet34lec34-slides 

 

WS34-solution 

13

Mon

Apr 11

Lecture 35: Eureka-style Speculative Task Parallelism 

 

worksheet35lec35-slides

 

 

 
WS35-solution 
 WedApr 13Lecture 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   WS36-solution 
 FriApr 15Lecture 37: Eureka-style Speculative Task Parallelism Parallel Prefix Sum applications  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  Homework 5  

Lab Schedule

Lab #

Date (2022)

Topic

Handouts

Examples

1

Jan 10

Infrastructure setup

lab0-handout

lab1-handout

 
-2Jan 17  Functional Programminglab2-handout 

-3

Jan 24

 

Java Streams

lab3-handout
 
-4Jan 31 Futures lab4-handout 

-5

Feb 07

 

 

Data-Driven Tasks

lab5-handout 
-6

Feb 14

 

 

Async / Finish

lab6-handout 
-

Feb 21 

No lab this week (Midterm)

  
-7Feb 28  Recursive Task Cutoff Strategylab7-handout 
-8Mar 07  Java Threadslab8-handout 

-

Mar 14

No lab this week (Spring Break)

  
-9Mar 21  Concurrent Listslab9-handout 
-10Mar 28 Actors lab10-handout 
-11

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.

...