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COMP 322: Fundamentals of Parallel Programming

Instructor:

Prof. Vivek Sarkar

Staff:

Sanjay Chatterjee (DH 2068)

 

Please send all emails to comp322-staff

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rice.edu

 

Raghavan Raman

 

 

 

Christopher Nunu

 

 

 

Max Grossman

Lectures:

Duncan Hall (DH) 1042

Time:

MWF 1:00-01:50pm

Labs:

Ryon 102

Times:

Tuesday 2:30-3:50pm (Sec 1), Wednesday 3:30-4:50pm (Sec 2)

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 211 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 computees 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 language that should also be accessible to C programmers --- no advanced Java features 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, CUDA and Google's MapReduce.  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 four written assignments,  three programming assignments, and two exams.

Textbooks">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 provide a number of online references in the lecture slides as well.

However, there are two optional textbooks that we will draw from quite heavily.  You are encouraged to get copies of either or both books as useful references during and after this course:

Lecture Schedule

 

Day

Date(2009)

Topic

Handouts

Slides

Homework Assigned

Homework Due

1

Mon

Jan 10

Lecture 1: The What and Why of Parallel Programming

lec1-handout

 

HW1 (Written Assignment)

 

2

Wed

Jan 12

Lecture 2: Task Creation & Termination using Async & Finish

 

 

 

 

3

Fri

Jan 14

Lecture 3: Computation Graphs, Abstract Performance Metrics

 

 

HW2 (Written Assignment)

HW1

-

Mon

Jan 17

School Holiday

 

 

 

 

4

Wed

Jan 19

Lecture 4: Futures --- Tasks with Return Values

 

 

 

 

5

Fri

Jan 21

Lecture 5: Parallel Array Sum, Parallel Quicksort

 

 

HW3 (Programming Assignment)

HW2

6

Mon

Jan 24

Lecture 6: Data Races and How to Avoid Them

 

 

 

 

7

Wed

Jan 26

Lecture 7: Parallel Prefix Sum

 

 

 

 

8

Fri

Jan 28

Lecture 8: Scheduling Theory, Amdahl's Law

 

 

 

 

9

Mon

Jan 31

Lecture 9: Forall parallel loops, Data parallelism

 

 

 

 

10

Wed

Feb 02

Lecture 10: Parallel algorithms using forall

 

 

 

 

11

Fri

Feb 04

Lecture 11: Critical sections and the Isolated statements

 

 

HW4 (Written Assignment)

HW3

12

Mon

Feb 07

Lecture 12: Java Atomic Variables

 

 

 

 

13

Wed

Feb 09

Lecture 13: Guest Lecture (John Mellor-Crummey)

 

 

 

 

14

Fri

Feb 11

Lecture 14: Guest Lecture (John Mellor-Crummey)

 

 

 

 

15

Mon

Feb 14

Lecture 15: Barrier Synchronization (Phasers I)

 

 

 

 

16

Wed

Feb 16

Lecture 16: Split-phase Barriers (Phasers II)

 

 

 

 

17

Fri

Feb 18

Lecture 17: Point-to-point Synchronization (Phasers III)

 

 

 

HW4

18

Mon

Feb 21

Lecture 18: Successive Over Relaxation case study

 

 

 

 

19

Wed

Feb 23

Lecture 19: Midterm Summary

 

 

Midterm Exam (Take-home)

 

20

Fri

Feb 25

No lecture, Exam1 due today

 

 

HW5 (Written Assignment)

Midterm Exam (Take-home)

-

M-F

Feb 28 - Mar 04

Spring Break

 

 

 

 

21

Mon

Mar 07

Lecture 20: Map Reduce

 

 

 

 

22

Wed

Mar 09

Lecture 21: Generalized Scan

 

 

 

 

23

Fri

Mar 11

Lecture 22: Task Affinity with Places

 

 

HW6 (Programming Assignment)

HW5

24

Mon

Mar 14

Lecture 23: Task Affinity with Places, contd.

 

 

 

 

25

Wed

Mar 16

Lecture 24: Bounded Buffers

 

 

 

 

26

Fri

Mar 18

Lecture 25: Java Concurrent Collections

 

 

 

 

27

Mon

Mar 21

Lecture 26: Data Flow Programming

 

 

 

 

28

Wed

Mar 23

Lecture 27: Data Flow Programming, contd

 

 

 

 

-

Fri

Mar 25

Midterm Recess

 

 

 

 

29

Mon

Mar 28

Lecture 28: Java Threads

 

 

 

 

30

Wed

Mar 30

Lecture 29: GUI Applications

 

 

 

 

31

Fri

Apr 01

Lecture 30: Java Executors

 

 

HW7 (Programming Assignment)

HW6

32

Mon

Apr 04

Lecture 31: Java Locks & Conditions

 

 

 

 

33

Wed

Apr 06

Lecture 32: Java Synchronizers

 

 

 

 

34

Fri

Apr 08

Lecture 33: Deadlock, Livelock, Liveness

 

 

 

 

35

Mon

Apr 11

Lecture 34: Java Memory Model and Volatile Variables

 

 

 

 

36

Wed

Apr 13

Lecture 35: GPGU programming with CUDA

 

 

 

 

37

Fri

Apr 15

Lecture 36: CUDA contd.

 

 

 

 

38

Mon

Apr 18

Lecture 37: Distributed-memory programming with MPI

 

 

 

 

39

Wed

Apr 20

Lecture 38: MPI contd.

 

 

 

 

40

Fri

Apr 22

Lecture 39: Course Summary

 

 

 

HW7

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.  Take-home exams, 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. 

The work you submit for this class is expected to be the result of your own work and that of your homework partner. 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 online sources, you must provide proper attribution (as shown here) in your homework/programming assignment turnins. A tutorial on how and when to cite sources is here. You should explain what value you have added to work taken from online sources. Finally, it is also your responsibility to protect your work from unauthorized access. I will expect you to follow the Honor Code in this course.
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.

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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