• COMP322-Spring-2012
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NOTE: the most recent web site for COMP 322 is located here

 

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

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 (HJ 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

lec9-audio

nqueens.hj

 

HW2

10

Wed

Feb 01

Lecture 10: Forasync Chunking, Parallel Prefix Sum algorithm

lec10-slides

lec10-audio

 

 

 

11

Fri

Feb 03

Lecture 11: Parallel Prefix Sum (contd), Parallel Quicksort

lec11-slides

lec11-audio

 

HW3 (HJ Programming Assignment)SeqScoring.hjX.txtY.txtBigSeq.zip

 

12

Mon

Feb 06

Lecture 12: Finish Accumulators, Forall Loops and Barrier Synchronization

lec12-slides

lec12-audio

 

 

 

13

Wed

Feb 08

Lecture 13: Forall Loops and Barrier Synchronization (contd)

lec13-slides

lec13-audio

 

 

 

14

Fri

Feb 10

Lecture 14: Point-to-point Synchronization and Phasers

lec14-slides

lec14-audio

 

 

 

15

Mon

Feb 13

Lecture 15: Phaser Accumulators, Bounded Phasers

lec15-slides

lec15-audio

 

 

 

16

Wed

Feb 15

Lecture 16: Summary of Barriers and Phasers

lec16-slides

lec16-audio

 

 

 

17

Fri

Feb 17

Lecture 17: Task Affinity with Places

lec17-slides

lec17-audio

 

 

 

18

Mon

Feb 20

Lecture 18: Task Affinity with Places (contd)

lec18-slides

lec18-audio

 

 

 

19

Wed

Feb 22

Lecture 19: Midterm Summary

lec19-slides

 

 

 

 

-

F

Feb 24

No Lecture (Take-home Exam 1 due by 4pm today)

 

 

 

 

HW3

-

M-F

Feb 27 - Mar 02

Spring Break

 

 

 

 

 

20

Mon

Mar 05

Lecture 20: Critical sections and the Isolated statement

lec20-slides

lec20-audio

 

 

 

21

Wed

Mar 07

Lecture 21: Isolated statement (contd), Monitors, Actors

lec21-slides

lec21-audio

 

HW4 (HJ Programming Assignment), hw_4.zip

 

22

Fri

Mar 09

Lecture 22: Actors (contd)

lec22-slides

lec22-audio

HJ Actor Examples

 

 

23

Mon

Mar 12

Lecture 23: Linearizability of Concurrent Objects

lec23-slides

lec23-audio

 

 

 

24

Wed

Mar 14

Lecture 24: Linearizability of Concurrent Objects (contd)

lec24-slides

lec24-audio

 

 

 

25

Fri

Mar 16

Lecture 25: Safety and Liveness Properties

lec25-slides

lec25-audio

 

 

 

26

Mon

Mar 19

Lecture 26: Parallel Programming Patterns

lec26-slides

lec26-audio

 

 

 

27

Wed

Mar 21

Lecture 27: Introduction to Java Threads

lec27-slides

lec27-audio

 

HW5 (Written Assignment) --- HW5.pdf or HW5.doc

HW4

-

Fri

Mar 23

Midterm Recess

 

 

 

 

 

28

Mon

Mar 26

Lecture 28: Bitonic Sort (guest lecture by Prof. John Mellor-Crummey)

lec28-slides

 

 

 

 

29

Wed

Mar 28

Lecture 29: Java Threads (contd), Java synchronized statement

lec29-slides

lec29-audio

 

 

 

30

Fri

Mar 30

Lecture 30: Java synchronized statement (contd), advanced locking

lec30-slides

lec30-audio

 

 

 

31

Mon

Apr 02

Lecture 31: Java Executors and Synchronizers

lec31-slides

lec31-audio

 

 

 

32

Wed

Apr 04

Lecture 32: Volatile Variables and Java Memory Model

lec32-slides

lec32-audio

 

 

 

33

Fri

Apr 06

Lecture 33: Message Passing Interface (MPI)

lec33-slides

lec33-audio

 

 

HW5

34

Mon

Apr 09

Lecture 34: Message Passing Interface (MPI, contd)

lec34-slides

lec34-audio

 

HW6 (Java Programming Assignment) , hw_6.zip

 

35

Wed

Apr 11

Lecture 35: Cloud Computing, Map Reduce

lec35-slides

lec35-audio

 

 

 

36

Fri

Apr 13

Lecture 36: Map Reduce (contd)

lec36-slides

lec36-audio

 

 

 

37

Mon

Apr 16

Lecture 37: Speculative parallelization of isolated blocks (Guest lecture by Prof. Swarat Chaudhuri)

lec37-slides

 

 

 

 

38

Wed

Apr 18

Lecture 38: Comparison of Parallel Programming Models

lec38-slides

lec38-audio

 

 

 

39

Fri

Apr 20

Lecture 39: Course Review

lec39-slides

lec39-audio

 

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

 

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

nqueens.hjArraySum2.hj

 

5

Feb 07, 08, 09

Data-driven futures

lab5-handout

MatrixEval.hj, test0.txt, test.txtDDFEx.hj

 

6

Feb 14, 15, 16

Barriers and Phasers

lab6-handout

OneDimAveraging.hj

 

-

Feb 21, 22, 23

No lab (Exam 1 week)

 

 

 

7

Mar 06, 07, 08

Atomic Variables and Isolated Statement

lab7-handout

spanning_tree_isolated.hjSortedListExampleGbl.hj

 

8

Mar 13, 14, 15

Actors

lab8-handout

HJ Actor Examples

 

-

Mar 20, 21, 22

No lab (HW4 deadline, midterm recess)

 

 

 

9

Mar 27, 28, 29

Java Threads

lab9-handout

nqueens.hj spanning_tree_atomic.hj

 

10

Apr 03, 04, 05

Java Locks

lab10-handout

lab10.zip

 

11

Apr 10, 11, 12

Message Passing Interface (MPI)

lab11-handout

lab11.zip

 

12

Apr 17, 18, 19

Map Reduce

lab12-handout

WordCount.hj  MapReduce.hjwords.txt Index.hj

 

Grading, Honor Code Policy, Processes and Procedures

Grading will be based on your performance on six homeworks (worth 50%), two exams (20% each), and lab attendance (10%).

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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