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COMP 322: Fundamentals of Parallel Programming (Spring

...

2023)


Instructor:

Prof. Vivek Sarkar

Mackale Joyner, DH

3131Head TA:Max Grossman

Co-Instructor:

Dr. Mackale Joyner

Graduate

2063

TAs:

Jonathan Sharman, Ryan Spring, Bing Xue, Lechen Yu

Admin Assistant:Annepha Hurlock, annepha@rice.edu, DH 3080, 713-348-5186Undergraduate TAs:Marc Canby, Anna Chi, Peter Elmers, Joseph Hungate, Cary Jiang, Gloria Kim, Kevin Mullin, Victoria Nazari, Ashok Sankaran, Sujay Tadwalkar, Anant Tibrewal, Eugene Wang, Yufeng Zhou
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/

class

spring2023/

ixdqx0x3bjl6en

comp322 (Piazza is the preferred medium for all course communications

, but you can also send email to comp322-staff at rice dot edu if needed)

)

Cross-listing:

ELEC 323

Lecture location:

Herzstein

Hall 210

Amphitheater

Lecture times:

MWF 1:00pm - 1:50pm

Lab locations:

DH 1064, DH 1070

Mon (Herzstein Amp), Tue (Keck 100)

Lab times:

Wednesday, 07

Mon  3:00pm -

08:30pm

Course Syllabus

3:50pm (Raiyan, Oscar, Mohamed, Ryan, Michelle, Taha, Jasmine)

Tue 4:00pm - 4:50pm (Tina, Delaney, Chase, Hung, Jerry, Kailin)

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.

...

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.  The links to the latest versions on Canvas are included below:

You  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.There are also a few optional textbooks that we will draw from quite heavily.  You are encouraged to get copies of The links to the latest versions of the lecture handouts are included below:

  • Module 1 handout (Parallelism)
  • Module 2 handout (Concurrency)

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:

Past Offerings of COMP 322

Lecture Schedule

 

...

Week

...

Day

...

Date (2017)

...

Lecture

...

Assigned Videos (see Canvas site for video links)

...

In-class Worksheets

...

Work Assigned

...

Work Due

...

1

...

Mon

...

Jan 09

...

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

...

 

...

 

...

 

...

Wed

...

Jan 11

...

Lecture 2:  Computation Graphs, Ideal Parallelism

...

Homework 1

...

 

...

 

...

2

...

Mon

...

Jan 16

...

No lecture, School Holiday (Martin Luther King, Jr. Day)

...

 

...

Wed

...

Jan 18

...

Lecture 4:   Parallel Speedup and Amdahl's Law

...

 

...

Fri

...

Jan 20

...

Lecture 5: Future Tasks, Functional Parallelism ("Back to the Future")

...

3

...

Mon

...

Jan 23

...

Lecture 6: Memoization

...

Lecture 7: Finish Accumulators

...

Homework 2

...

 

...

Fri

...

Jan 27

...

Lecture 8: Map Reduce

...

 

...

4

...

Mon

...

Jan 30

...

Lecture 9: Data Races, Functional & Structural Determinism

...

 

...

Wed

...

Feb 01

...

 

...

Fri

...

Feb 03

...

Lecture 11: Loop-Level Parallelism, Parallel Matrix Multiplication, Iteration Grouping (Chunking)

...

Topic 3.1 Lecture , Topic 3.1 Demonstration , Topic 3.2 Lecture, Topic 3.2 Demonstration, Topic 3.3 Lecture , Topic 3.3 Demonstration

...

5

...

Mon

...

Feb 06

...

Lecture 12:  Barrier Synchronization

...

Wed

...

Feb 08

...

Lecture 13: Parallelism in Java Streams, Parallel Prefix Sums

...

 Homework 3 (includes two intermediate checkpoints) 

...

-

...

Fri

...

Feb 10

...

Spring Recess

...

6

...

Mon

...

Feb 13

...

Lecture 14: Iterative Averaging Revisited, SPMD pattern

...

 

...

Wed

...

Feb 15

...

Lecture 15:  Phasers, Point-to-point Synchronization

...

 

...

Fri

...

Feb 17

...

Lecture 16: Phasers Review

...

7

...

Mon

...

Feb 20

...

Lecture 17: Midterm Summary

...

 

...

Wed

...

Feb 22

...

Midterm Review (interactive Q&A, no lecture)

...

 

...

Fri

...

Feb 24

...

Lecture 18: Abstract vs. Real Performance

...

8

...

Mon

...

Feb 27

...

Lecture 19: Pipeline Parallelism, Signal Statement, Fuzzy Barriers

...

 

...

 

...

Wed

...

Mar 01

...

Lecture 20: Critical sections, Isolated construct, Parallel Spanning Tree algorithm, Atomic variables (start of Module 2)

...

Topic 5.1 Lecture, Topic 5.1 Demonstration, Topic 5.2 Lecture, Topic 5.2 Demonstration, Topic 5.3 Lecture, Topic 5.3 Demonstration, Topic 5.4 Lecture, Topic 5.4 Demonstration, Topic 5.6 Lecture, Topic 5.6 Demonstration

...

 

...

 

...

Fri

...

Mar 03

...

Lecture 21:  Read-Write Isolation, Review of Phasers

...

Quiz for Unit 4

...

9

...

Mon

...

Mar 06

...

Lecture 22: Actors

...

 

...

 

 

...

 

...

Wed

...

Mar 08

...

Lecture 23:  Actors (contd)

...

 

...

Homework 3, Checkpoint-2

...

 

...

Fri

...

Mar 10

...

Lecture 24: Java Threads, Java synchronized statement

...

M-F

...

Mar 13 - Mar 17

...

Spring Break

...

10

...

Mon

...

Mar 20

...

Lecture 25: Java synchronized statement (contd), wait/notify

...

 

 

...

 

...

Wed

...

Mar 22

...

Lecture 25: Concurrent Objects, Linearizability of Concurrent Objects

...

Homework 4

(includes one intermediate checkpoint)

...

 

...

Fri

...

Mar 24

...

Lecture 27: Parallel Design Patterns, Safety and Liveness Properties, Java Synchronizers, Dining Philosophers Problem

...

Quiz for Unit 6

...

11

...

Mon

...

Mar 27

...

Lecture 28: Message Passing Interface (MPI), (start of Module 3)

...

lec28-slides

...

 

...

Wed

...

Mar 29

...

Lecture 29:  Message Passing Interface (MPI, contd)

...

 

...

 

...

Fri

...

Mar 31

...

Lecture 30: Apache Hadoop and Spark frameworks for Map-Reduce

...

12

...

Mon

...

Apr 03

...

Lecture 31: TF-IDF and PageRank Algorithms with Map-Reduce

...

 

...

 

...

Wed

...

Apr 05

...

Lecture 32: Combining Distribution and Multithreading

...

 

...

Homework 4 Checkpoint-1

...

 

...

Fri

...

Apr 07

...

Lecture 33: Eureka-style Speculative Task Parallelism

...

 

...

Quiz for Unit 8

...

13

...

Mon

...

Apr 10

...

Lecture 34: Task Affinity with Places

...

 

...

Wed

...

Apr 12

...

Lecture 35: GPU Computing

...

Homework 5  

(Due April 21st, with automatic extension until May 1st after which slip days may be used)

...

Homework 4 (all)

...

 

...

Fri

...

Apr 14

...

Lecture 36: Partitioned Global Address Space (PGAS) programming models

...

lec36-slides

...

14

...

Mon

...

Apr 17

...

Lecture 37: Topic TBD

...

 

...

 

...

Wed

...

Apr 19

...

 

...

 

...

 

...

 

...

Fri

...

Apr 21

...

Lecture 39: Course Review (lectures 19 - 38), Last day of classes

...

Homework 5 (automatic extension until May 1st, after which slip days may be used)

...

-

...

Tue

...

May 2

...

9am - 12noon, scheduled final exam (Exam 2 – scope of exam limited to lectures 19 - 38), location TBD by registrar

...

 

...

 

...

 

Lab Schedule

Lab #

Date (2017)

Topic

Handouts

Code Examples

0 Infrastructure Setuplab0-handout-

1

Jan 11

Async-Finish Parallel Programming with abstract metrics

lab1-handout, lab1-slides
lab_1.zip

2

Jan 18

Futures and HJ-Viz 

lab2-handout, lab2-slides
lab_2.zip

3

Jan 25

Cutoff Strategy and Real World Performance

lab3-handout, lab3-slides lab_3.zip

4

Feb 01

Java's ForkJoin Framework

lab4-handout, lab4-slides   lab_4.zip

5

Feb 08

Loop-level Parallelism

lab5-handout, lab5-slides lab_5.zip

6

Feb 15

Phasers

lab6-handout   lab_6.zip

-

Feb 22

No lab this week — Exam 1

--

7

Mar 01

Isolated Statement and Atomic Variables

lab7-handout, lab7-slides 

8

Mar 08

Actors

lab8-handout  

-

Mar 15

No lab this week — Spring Break

  
9

Mar 22

Java Threads, Java Locks

lab9-handout  

10

Mar 29

Message Passing Interface (MPI) 

lab10-handout  

-

Apr 05

No lab this week

  

11

Apr 12

Apache Spark

lab11-handout  
12Apr 19

Eureka-style Speculative Task Parallelism

lab12-handout  

Grading, Honor Code Policy, Processes and Procedures

...

Lecture Schedule



Week

Day

Date (2023)

Lecture

Assigned Reading

Assigned Videos (see Canvas site for video links)

In-class Worksheets

Slides

Work Assigned

Work Due

Worksheet Solutions

1

Mon

Jan 09

Lecture 1: Introduction



worksheet1lec1-slides  



WS1-solution


Wed

Jan 11

Lecture 2:  Functional Programming



worksheet2lec02-slides



WS2-solution

FriJan 13Lecture 3: Higher order functions

worksheet3 lec3-slides   



WS3-solution

2

Mon

Jan 16

No class: MLK










Wed

Jan 18

Lecture 4: Lazy Computation



worksheet4lec4-slides

WS4-solution


Fri

Jan 20

Lecture 5: Java Streams



worksheet5lec5-slidesHomework 1
WS5-solution
3MonJan 23

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 25

Lecture 7: Futures

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



WS7-solution


Fri

Jan 27

Lecture 8:  Async, Finish, Computation Graphs

Module 1: Sections 1.1, 1.2Topic 1.1 Lecture, Topic 1.1 Demonstration, Topic 1.2 Lecture, Topic 1.2 Demonstrationworksheet8lec8-slides

WS8-solution

4

Mon


Jan 30 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

WedFeb 01Lecture 10: Event-based programming model




worksheet10lec10-slides
Homework 1WS10-solution

FriFeb 03Lecture 11: GUI programming as an example of event-based,
futures/callbacks in GUI programming


worksheet11lec11-slidesHomework 2
WS11-solution
5

Mon

Feb 06

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 08

Lecture 13: Parallel Speedup, Critical Path, Amdahl's Law

Module 1: Section 1.5

Topic 1.5 Lecture , Topic 1.5 Demonstration

worksheet13lec13-slides 
WS13-solution


Fri

Feb 10

No class: Spring Recess










6

Mon

Feb 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 15

Lecture 15: Recursive Task Parallelism  



worksheet15lec15-slides



Homework 2WS15-solution

FriFeb 17

Lecture 16: 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 Demonstrationworksheet16 lec16-slidesHomework 3
WS16-solution

7

Mon

Feb 20

Lecture 17: Midterm Review




lec17-slides




Wed

Feb 22

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



worksheet18lec18-slides

WS18-solution


Fri

Feb 24 

Lecture 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 Demonstrationworksheet20lec20-slides      

WS20-solution


Wed

Mar 01

Lecture 21:  Atomic variables, Synchronized statements

Module 2: Sections 5.4, 7.2

Topic 5.4 Lecture, Topic 5.4 Demonstration, Topic 7.2 Lectureworksheet21lec21-slides
Homework 3WS21-solution


Fri

Mar 03

Lecture 22: Parallel Spanning Tree, other graph algorithms 


 worksheet22lec22-slidesHomework 4


WS22-solution

9

Mon

Mar 06

Lecture 23: 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 08

Lecture 24: Java Locks - Soundness and progress guarantees  

Module 2: 7.5Topic 7.5 Lecture worksheet24 lec24-slides


WS24-solution


Fri

Mar 10

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


WS25-solution

Mon

Mar 13

No class: Spring Break

 







WedMar 15No class: Spring Break








Fri

Mar 17

No class: Spring Break









10

Mon

Mar 20

Lecture 26: N-Body problem, applications and implementations 



worksheet26lec26-slides

WS26-solution


Wed

Mar 22

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

Module 2: 7.3, 7.4Topic 7.3 Lecture, Topic 7.4 Lectureworksheet27lec27-slides



WS27-solution


Fri

Mar 24

Lecture 28: Message-Passing programming model with Actors

Module 2: 6.1, 6.2Topic 6.1 Lecture, Topic 6.1 Demonstration,   Topic 6.2 Lecture, Topic 6.2 Demonstration worksheet28lec28-slides




WS28-solution

11

Mon

Mar 27

Lecture 29: 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 29

Lecture 30: Task Affinity and locality. Memory hierarchy 



worksheet30lec30-slides


Homework 4WS30-solution


Fri

Mar 31

Lecture 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


WS31-solution

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 05

Lecture 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 07

Lecture 34: Fuzzy Barriers with Phasers

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


WS34-solution

13

Mon

Apr 10

Lecture 35: Eureka-style Speculative Task Parallelism


worksheet35lec35-slides



WS35-solution

WedApr 12Lecture 36: Scan Pattern. Parallel Prefix Sum


worksheet36lec36-slides

WS36-solution

FriApr 14Lecture 37: Parallel Prefix Sum applications

worksheet37lec37-slides



14MonApr 17Lecture 38: Overview of other models and frameworks


lec38-slides




WedApr 19Lecture 39: Course Review (Lectures 19-38)


lec39-slides
Homework 5


FriApr 21Lecture 40: Course Review (Lectures 19-38)


lec40-slides



Lab Schedule

Lab #

Date (2023)

Topic

Handouts

Examples

1

Jan 09

Infrastructure setup

lab0-handout

lab1-handout


-Jan 16No lab this week (MLK)

2Jan 23Functional Programminglab2-handout

3

Jan 30

Futures

lab3-handout

-Feb 06No lab this week (Spring Recess)

4

Feb 13

Data-Driven Tasks

lab4-handout
-Feb 20No lab this week (Midterm Exam)

5

Feb 27

Async / Finish

lab5-handout
6Mar 06Recursive Task Cutoff Strategylab6-handout
-Mar 13No lab this week (Spring Break)

7Mar 20Java Threadslab7-handout
8Mar 27Concurrent Listslab8-handout
9Apr 03Actorslab9-handout
10

Apr 10

Loop Parallelism

lab10-handout

-

Apr 17

No lab this week



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), weekly lab exercises (weighted 10% in all), online quizzes (weighted 5% in all), and class participation including in-class Q&A, worksheets, Piazza participation class worksheets (weighted 5% in all).

The purpose of the homeworks homework is to give you practice in solving problems that deepen your understanding of concepts introduced in class. Homeworks are 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 automatically tracked through the Autograder, more details are available later in this document and in the Autograder user guideusing 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 checked off by a TA prior to the start of the lab the following week.Worksheets should be completed in class for full credit.  For partial credit, a worksheet can be turned in before the start of the class following the one in which the worksheet for distributed, submitted by the following Wednesday at 4:30pm.  Labs must be checked off by a TA.

Worksheets should be completed by the deadline listed in Canvas so that solutions to the worksheets can be discussed in the next class.

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

  • In-class worksheets: You are free to discuss all aspects of in-class worksheets with your other classmates, the teaching assistants and the professor during the class. You can work in a group and write down the solution that you obtained as a group. If you work on the worksheet outside of class (e.g., due to an absence), then it must be entirely your individual effort, without discussion with any other students.  If you use any material from external sources, you must provide proper attribution.
  • Weekly lab assignments: You are free to discuss all aspects of lab assignments with your other classmates, the teaching assistants and the professor during the lab.  However, all code and reports that you submit are expected to be the result of your individual effort. If you work on the lab outside of class (e.g., due to an absence), then it must be entirely your individual effort, without discussion with any other students.  If you use any material from external sources, you must provide proper attribution (as shown here).
  • HomeworksHomework: All submitted homeworks are homework is expected to be the result of your individual effort. You are free to discuss course material and approaches to problems with your other classmates, the teaching assistants and the professor, but you should never misrepresent someone else’s work as your own. If you use any material from external sources, you must provide proper attribution.
  • Quizzes: Each online quiz will be an open-notes individual test.  The student may consult their course materials and notes when taking the quizzes, but may not consult any other external sources.
  • Exams: Each exam will be a closedopen-book, closedopen-notes, and closedopen-computer individual written test, which must be completed within a specified time limit.  No class notes or external materials may be consulted when taking the exams.

 Graded homeworks will be returned to you via email, and exams as marked-up hardcopies. If you believe we have made an error in grading your homework or exam, please bring the matter to our

attention within one weekFor 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

...