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COMP 322: Fundamentals of Parallel Programming (Spring 2012)

Instructor:

Prof. Vivek Sarkar, DH 3131

Graduate TA:

Sanjay Chatterjee 

 

Please send all emails to comp322-staff at rice dot edu

Graduate TA:

Deepak Majeti

Assistant:

Amanda Nokleby, akn3@rice.edu, DH 3137

Graduate TA:

Dragos Sbirlea 

 

 

Undergrad TA:

Max Grossman

 

 

Undergrad TA:

Damien Stone

Cross-listing:

ELEC 323

Undergrad TA:

Yunming Zhang

 

 

Research Programmer:

Vincent Cavé

Lectures:

Brockman 101 (new location effective 1/18/2012)

Lecture times:

MWF 1:00 - 1:50pm

Labs:

Ryon 102

Lab times:

Tuesday, 4:00 - 5:20pm (Section 3)

 

 

 

Wednesday, 3:30 - 4:50pm (Section 2)

 

 

 

Thursday, 4:00 - 5:20pm (Section 1)

Introduction

The goal of COMP 322 is to introduce you to the fundamentals of parallel programming and parallel algorithms, using a pedagogical approach that exposes you to the intellectual challenges in parallel software without enmeshing you in low-level details of different parallel systems.  To that end, the main pre-requisite course requirement is COMP 215 or equivalent.  This course should be accessible to anyone familiar with the foundations of sequential algorithms and data structures, and with basic Java programming.  COMP 221 is also recommended as a co-requisite.

The pedagogical approach will introduce you to the following foundations of parallel programming:

  • Primitive constructs for task creation & termination, collective & point-to-point synchronization, task and data distribution, and data parallelism
  • Abstract models of parallel computations and computation graphs
  • Parallel algorithms and data structures including lists, strings, trees, graphs, matrices
  • Common parallel programming patterns including task parallelism, undirected and directed synchronization, data parallelism, divide-and-conquer parallelism, map-reduce, concurrent event processing including graphical user interfaces. 

Laboratory assignments will explore these topics through a simple parallel extension to the Java language called Habanero-Java (HJ), developed in the Habanero Multicore Software Research project at Rice University.  The use of Java will be confined to a subset of the Java 1.4 language that should also be accessible to C programmers --- no advanced Java features (e.g., generics) will be used.  An abstract performance model for HJ programs will be available to aid you in complexity analysis of parallel programs before you embark on performance evaluations on real parallel machines.  We will conclude the course by introducing you to some real-world parallel programming models including the Java Concurrency Utilities, Google's MapReduce, CUDA and MPI.  The foundations gained in this course will prepare you for advanced courses on Parallel Computing offered at Rice (COMP 422, COMP 522). 
 
Since the aim of the course is for you to gain both theoretical and practical knowledge of the foundations of parallel programming, the weightage for course work will be balanced across homeworks, exams, and lab attendance.  

Textbooks

There are no required textbooks for the class. You will be expected to read each lecture handout before coming to the lecture.  We will also provide a number of references in the slides and handouts.

However, there are a few optional textbooks that we will draw from quite heavily.  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

 

Day

Date (2012)

Topic

Slides

Audio (Panopto)

Code Examples

Handouts

Homework Assigned

Homework Due

1

Mon

Jan 9

Lecture 1: The What and Why of Parallel Programming

lec1-slides

 

ArraySum0.hj

 

HW1 (Written Assignment)

 

2

Wed

Jan 11

Lecture 2: Async-Finish Parallel Programming and Computation Graphs

lec2-slides

lec2-audio

PrimeSieve.hj

 

 

 

3

Fri

Jan 13

Lecture 3: Computation Graphs, Abstract Performance Metrics, Array Reductions

lec3-slides

lec3-audio

ArraySum1.hj

 

HW2 (Programming Assignment)

HW1

-

Mon

Jan 16

School Holiday

 

 

 

 

 

 

4

Wed

Jan 18

Lecture 4: Parallel Speedup, Efficiency, Amdahl's Law

lec4-slides

lec4-audio

 

 

 

 

5

Fri

Jan 20

Lecture 5: Data & Control Flow with Async Tasks, Data Races

lec5-slides

lec5-audio

(See Lab 3)

 

 

 

6

Mon

Jan 23

Lecture 6: Memory Models, Atomic Variables

lec6-slides

lec6-audio

(See Lab 3)

 

 

 

7

Wed

Jan 25

Lecture 7: Memory Models (contd), Futures --- Tasks with Return Values

lec7-slides

lec7-audio

ArraySum2.hj

 

 

 

8

Fri

Jan 27

Lecture 8: Futures (contd), Dataflow Programming, Data-Driven Tasks

lec8-slides

lec8-audio

binarytrees.hj

 

 

 

9

Mon

Jan 30

Lecture 9: Abstract vs. Real Performance, seq clause, forasync loops

lec9-slides

 

nqueens.hj

 

 

HW2

10

Wed

Feb 01

Lecture 10: Parallel Prefix Sum, Forall parallel loops

 

 

 

 

HW3

 

11

Fri

Feb 03

Lecture 11: Critical sections and the Isolated statement

 

 

 

 

 

 

12

Mon

Feb 06

Lecture 12: Forall Loops and Barrier Synchronization

 

 

 

 

 

 

13

Wed

Feb 08

Lecture 13: Forall Loops and Barrier Synchronization (contd)

 

 

 

 

 

 

14

Fri

Feb 10

Lecture 14: Point-to-point Synchronization and Phasers

 

 

 

 

 

 

15

Mon

Feb 13

Lecture 15: Point-to-point Synchronization and Phasers (contd)

 

 

 

 

 

 

16

Wed

Feb 15

Lecture 16: Advanced Phaser topics

 

 

 

 

 

 

17

Fri

Feb 17

Lecture 17: Parallel Sorting Algorithms

 

 

 

 

 

 

18

Mon

Feb 20

Lecture 18: Parallel Sorting Algorithms (contd)

 

 

 

 

 

 

19

Wed

Feb 22

Lecture 19: Midterm Summary

 

 

 

 

 

HW3

-

Fri

Feb 24

Exam 1 (in class)

 

 

 

 

 

 

-

M-F

Feb 27 - Mar 02

Spring Break

 

 

 

 

 

 

20

Mon

Mar 05

Lecture 20: Java Atomic Variables

 

 

 

 

HW4

 

21

Wed

Mar 07

Lecture 21: Java Concurrent Collections

 

 

 

 

 

 

22

Fri

Mar 09

Lecture 22: Linearizability of Concurrent Objects

 

 

 

 

 

 

23

Mon

Mar 12

Lecture 23: Task Affinity with Places

 

 

 

 

 

 

24

Wed

Mar 14

Lecture 24: Task Affinity with Places, contd.

 

 

 

 

 

 

25

Fri

Mar 16

Lecture 25: Map Reduce

 

 

 

 

 

 

26

Mon

Mar 19

Lecture 26: Map Reduce, contd.

 

 

 

 

 

HW4

27

Wed

Mar 21

Lecture 27: Dataflow Programming with Intel Concurrent Collections

 

 

 

 

HW5

 

-

Fri

Mar 23

Midterm Recess

 

 

 

 

 

 

28

Mon

Mar 26

Lecture 28: Java Threads

 

 

 

 

 

 

29

Wed

Mar 28

Lecture 29: Java Threads (contd), synchronized statement

 

 

 

 

 

 

30

Fri

Mar 30

Lecture 30: Java synchronized statement with wait/notify

 

 

 

 

 

 

31

Mon

Apr 02

Lecture 31: Advanced locking in Java

 

 

 

 

 

 

32

Wed

Apr 04

Lecture 32: Java Executors and Synchronizers

 

 

 

 

 

HW5

33

Fri

Apr 06

Lecture 33: Volatile Variables and Java Memory Model

 

 

 

 

HW6

 

34

Mon

Apr 09

Lecture 34: GPGPU programming with CUDA

 

 

 

 

 

 

35

Wed

Apr 11

Lecture 35: CUDA contd.

 

 

 

 

 

 

36

Fri

Apr 13

Lecture 36: Liveness and Progress Guarantees

 

 

 

 

 

 

37

Mon

Apr 16

Lecture 37: Introduction to MPI

 

 

 

 

 

 

38

Wed

Apr 18

Lecture 38: Introduction to MPI (contd)

 

 

 

 

 

 

39

Fri

Apr 20

Lecture 39: Course Summary

 

 

 

 

Exam 2 (Take-home)

HW6

-

Fri

Apr 27

Exam 2 due

 

 

 

 

 

Exam 2

Lab Schedule

Lab #

Date (2011)

Topic

Handouts

Code Examples

Solutions

1

Jan 10, 11, 12

DrHJ setup, Async-Finish Parallel Programming

lab1-handout

HelloWorld.hjReciprocalArraySum.hjPrimeSieve.hj

TwoWayParallelPrimeSieve.hj

2

Jan 17, 18, 19

Abstract performance metrics with async & finish

lab2-handout

Search.hj

 

3

Jan 23, 25, 26

Data race detection and repair

lab3-handout

RacyArraySum1.hjRacyFib.hjRacyNQueens.hjRacyFannkuch.hj

 

4

Jan 30 Feb 01, 02

Real performance, work-sharing and work-stealing runtimes, futures

lab4-handout

 

 

5

Feb 07, 08, 09

Points, regions, forall loops, data-driven futures

 

 

 

6

Feb 14, 15, 16

Barriers and Phasers

 

 

 

-

Feb 21, 22, 23

No lab (midterm week)

 

 

 

7

Mar 06, 07, 08

Atomic Variables

 

 

 

8

Mar 13, 14, 15

Places

 

 

 

9

Mar 20, 21, 22

Data Driven Tasks

 

 

 

10

Mar 27, 28, 29

Java Concurrency I

 

 

 

11

Apr 03, 04, 05

Java Concurrency II

 

 

 

12

Apr 10, 11, 12

CUDA

 

 

 

13

Apr 17, 18, 19

MPI

 

 

 

Grading, Honor Code Policy, Processes and Procedures

Grading will be based on your performance on homeworks (worth 50%) and exams (20% for first exam, and 30% for the second exam).

The purpose of the homeworks is to train you to solve problems and to help deepen your understanding of concepts introduced in class. Homeworks and programming assignments are due on the dates and times specified in the course schedule. Please turn in all your homeworks using the CLEAR turn-in system. Homework is worth full credit when turned in on time. A 10% penalty per day will be levied on late homeworks, up to a maximum of 6 days. No submissions will be accepted more than 6 days after the due date.

You will be expected to follow the Honor Code in all homeworks and exams.  All submitted homeworks are 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 ([as shown here|http://www.dartmouth.edu/~writing/sources/]).  Exams 1 and 2, which are pledged under the Honor Code, test your individual understanding and knowledge of the material. Collaboration on exams is strictly forbidden.  Finally, it is also your responsibility to protect your homeworks and exams from unauthorized access. 

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 week.

Past Offerings of COMP 322

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.

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