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COMP 322: Fundamentals of Parallel Programming (Spring 2022)

 

Instructors:

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 

 

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 ()

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

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.

Course Objectives

The primary goal of COMP 322 is to introduce you to the fundamentals of parallel programming and parallel algorithms, by following a pedagogic approach that exposes you to the intellectual challenges in parallel software without enmeshing you in the jargon and lower-level details of today's parallel systems.  A strong grasp of the course fundamentals will enable you to quickly pick up any specific parallel programming system that you may encounter in the future, and also prepare you for studying advanced topics related to parallelism and concurrency in courses such as COMP 422. 

The desired learning outcomes fall into three major areas (course modules):

1) Parallelism: 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.

2) Concurrency: critical sections, atomicity, isolation, high level data races, nondeterminism, linearizability, liveness/progress guarantees, actors, request-response parallelism, Java Concurrency, locks, condition variables, semaphores, memory consistency models.

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 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.

Prerequisite    

The prerequisite course requirements are COMP 182 and COMP 215.  COMP 322 should be accessible to anyone familiar with the foundations of sequential algorithms and data structures, and with basic Java programming.  COMP 321 is also recommended as a co-requisite.  

Textbooks and Other Resources

There are no required textbooks for the class. Instead, lecture handouts are provided for each module as follows.  You are expected to read the relevant sections in each lecture handout before coming to the lecture.  We will also provide a number of references in the slides and handouts.The links to the latest versions of the lecture handouts are included below:

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

 

 

Week

Day

Date (2022)

Lecture

Assigned Reading

Assigned Videos (see Canvas site for video links)

In-class Worksheets

Slides

Work Assigned

Work Due

  

1

Mon

Jan 10

Lecture 1: Introduction

 

 

worksheet1lec1-slides

 

 

  

 

Wed

Jan 12

Lecture 2:  Functional Programming

  worksheet2lec2-slides

 

 

  
 FriJan 14Lecture 3: Higher order functions  worksheet3lec3-slides

 

   

2

Mon

Jan 17

No class: MLK

        

 

Wed

Jan 19

Lecture 4: Lazy Computation  worksheet4lec4-slides    

 

Fri

Jan 21

Lecture 5: Java Streams

  worksheet5lec5-slidesHomework 1   
3MonJan 24

Lecture 6: Map Reduce with Java Streams

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

 

   

 

Wed

Jan 26

Lecture 7: Futures

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

 

   

 

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    

4

Mon

 

Jan 31 Lecture 9: Data-Driven Tasks 

Module 1: Section 4.5

 

Topic 4.5 Lecture   Topic 4.5 Demonstration

worksheet9

lec9-slides     
 WedFeb 02Lecture 10: Event-based programming model

 

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

 

Wed

Feb 09

Lecture 13: Lightweight task parallelism. Finish/async

Module 1: Section 1.1

Topic 1.1 Lecture , Topic 1.1 Demonstration

worksheet13lec13-slides    

 

Fri

Feb 11

No class: Spring Recess

 

        
6

Mon

Feb 14

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

Module 1: Section 1.5Topic 1.5 Lecture   Topic 1.5 Demonstrationworksheet14lec14-slides    

 

Wed

Feb 16

Lecture 15: Recursive Task Parallelism 

  worksheet15lec15-slides

 

 

   
 FriFeb 18

Lecture 16: Accumulation and reduction. Finish accumulators

Module 1: Section 2.3Topic 2.3 Lecture , Topic 2.3 Demonstrationworksheet16 lec16-slidesHomework 3   

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

   lec18-slides    

 

Fri

Feb 25 

Lecture 19: Data Races, Functional & Structural Determinism

Module 1: Sections 2.5, 2.6Topic 2.5 Lecture, Topic 2.5 Demonstration, Topic 2.6 Lecture, Topic 2.6 Demonstrationworksheet19lec19-slides    

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 Demonstrationworksheet20lec20-slides       

 

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, 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)

Topic

Handouts

Examples

1

Jan 10

Infrastructure setup

lab0-handout

lab1-handout

 
-Jan 17   

-

Jan 24

 


 
-Jan 31   

-

Feb 07

 

  
-

Feb 14

 

  
-

Feb 21

 

  
-Feb 28   
-Mar 07   

-

Mar 14

No lab this week (Spring Break)

  
-Mar 21   
-Mar 28   
-

Apr 04

 

  

-

Apr 11

 

  

-

Apr 18

 

  

Grading, Honor Code Policy, Processes and Procedures

Grading will be based on your performance on four homework assignments (weighted 40% in all), two exams (weighted 40% in all), lab exercises (weighted 10% in all), online quizzes (weighted 5% in all), and in-class worksheets (weighted 5% in all).

The purpose of the homework is to give you practice in solving problems that deepen your understanding of concepts introduced in class. Homework is due on the dates and times specified in the course schedule.  No late submissions (other than those using slip days mentioned below) will be accepted.

The slip day policy for COMP 322 is similar to that of COMP 321. All students will be given 3 slip days to use throughout the semester. When you use a slip day, you will receive up to 24 additional hours to complete the assignment. You may use these slip days in any way you see fit (3 days on one assignment, 1 day each on 3 assignments, etc.). Slip days will be tracked using the README.md file. Other than slip days, no extensions will be given unless there are exceptional circumstances (such as severe sickness, not because you have too much other work). Such extensions must be requested and approved by the instructor (via e-mail, phone, or in person) before the due date for the assignment. Last minute requests are likely to be denied.

Labs must be submitted by the following Monday at 11:59pm.  Labs must be checked off by a TA.

Worksheets should be completed 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.

You will be expected to follow the Honor Code in all homework and exams.  The following policies will apply to different work products in the course:

 

For grade disputes, please send an email to the course instructors within 7 days of receiving your grade. The email subject should include COMP 322 and the assignment. Please provide enough information in the email so that the instructor does not need to perform a checkout of your code.

Accommodations for Students with Special Needs

Students with disabilities are encouraged to contact me during the first two weeks of class regarding any special needs. Students with disabilities should also contact Disabled Student Services in the Ley Student Center and the Rice Disability Support Services.