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COMP 322: Fundamentals of Parallel Programming (Spring 2015)

Instructor:

Prof. Vivek Sarkar, DH 3131

  

Co-Instructor:

Dr. Eric Allen

Graduate TAs:

Prasanth Chatarasi, Peng Du, Xian Fan, Max Grossman

 Please send all emails to comp322-staff at rice dot eduUndergraduate TAs:Matthew Bernhard, Nicholas Hanson-Holtry, Yi Hua,

 

 

 

Yoko Li, Ayush Narayan, Derek Peirce,

Cross-listing:

ELEC 323

 

Maggie Tang, Wei Zeng, Glenn Zhu

 

 

Course consultants:

Vincent Cavé, John Greiner, Shams Imam

Lectures:

Herzstein Hall 210

Lecture times:

MWF 1:00pm - 1:50pm

Labs:

DH 1064 (Section A01), DH 1070 (Section A02)

Lab times:

Wednesday, 07:00pm - 08:30pm

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

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 Owlspace are included below:

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 any or all of these books.  They will serve as useful references both during and after this course:

Lecture Schedule

Week

Day

Date (2015)

Topic

Assigned Reading

Assigned Videos (Quizzes due by Friday of each week)

In-class Worksheets

Slides

Work Assigned

Work Due

1

Mon

Jan 12

Lecture 1: The What and Why of Parallel Programming, Task Creation and Termination (Async, Finish)

Module 1: Sections 0.1, 0.2, 1.1

Topic 1.1 Lecture, Topic 1.1 Demonstration

worksheet1lec1-slides

 

 

 

Wed

Jan 14

Lecture 2:  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 Demonstrationworksheet2lec2-slides

 

 

 FriJan 16Lecture 3: ,   Abstract Performance Metrics, Multiprocessor SchedulingModule 1: Section 1.4Topic 1.4 Lecture, Topic 1.4 Demonstrationworksheet3lec3-slidesHomework 1Lecture & demo quizzes for topics 1.1, 1.2, 1.3, 1.4

2

Mon

Jan 19

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

      

 

Wed

Jan 21

Lecture 4:   Parallel Speedup and Amdahl's Law

Module 1: Section 1.5Topic 1.5 Lecture, Topic 1.5 Demonstrationworksheet4lec4-slides  

 

Fri

Jan 23

Lecture 5: Future Tasks, Functional Parallelism

Module 1: Section 1.6 (self-study), Section 2.1Topic 1.6 Lecture, Topic 1.6 Demonstration, Topic 2.1 Lecture,  Topic 2.1 Demonstrationworksheet5lec5-slides Lecture & demo quizzes for topics 1.5, 1.6, 2.1

3

Mon

Jan 26

Lecture 6: Finish Accumulators

Module 1: Section 2.3Topic 2.3 Lecture , Topic 2.3 Demonstration  worksheet6lec6-slides  
 WedJan 28

Lecture 7: 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 Demonstration  worksheet7lec7-slidesHomework 2Homework 1

 

Fri

Jan 30

Lecture 8: Map Reduce

Module 1: Section 2.4Topic 2.4 Lecture ,  Topic 2.4 Demonstration  worksheet8lec8-slides Lecture & demo quizzes for topics 2.3, 2.4, 2.5, 2.6

4

Mon

Feb 02

Lecture 9: Memoization

Module 1: Section 2.2Topic 2.2 Lecture ,  Topic 2.2 Demonstrationworksheet9lec9-slides  

 

Wed

Feb 04

Lecture 10: 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

worksheet10lec10-slides  

 

Fri

Feb 06

Lecture 11: Barrier Synchronization

 Topic 3.4 Lecture , Topic 3.4 Demonstrationworksheet11lec11-slides Lecture & demo quizzes for topics 2.2, 3.1, 3.2, 3.3, 3.4

5

Mon

Feb 09

Lecture 12: Iterative Averaging Revisited, SPMD pattern

 Topic 3.5 Lecture, Topic 3.5 Demonstration, Topic 3.6 Lecture,  Topic 3.6 Demonstration  worksheet12lec12-slides  

 

Wed

Feb 11

Lecture 13: Java’s ForkJoin Library

  worksheet13lec13-slides Homework 2

 

Fri

Feb 13

Lecture 14: Data-Driven Tasks and Data-Driven Futures

 Topic 4.5 Lecture,  Topic 4.5 Demonstrationworksheet14lec14-slides

Homework 3

Lecture & demo quizzes for topics 3.5, 3.6, 4.5

6

Mon

Feb 16

Lecture 15: Abstract vs. Real Performance

  worksheet15lec15-slides  

 

Wed

Feb 18

Lecture 16: Phasers, Point-to-point Synchronization

 Topic 4.2 Lecture,  Topic 4.2 Demonstration, Topic 4.3 Lecture,  Topic 4.3 Demonstrationworksheet16lec16-slides  

 

Fri

Feb 20

Lecture 17: Pipeline Parallelism, Signal Statement, Fuzzy Barriers

 Topic 4.4 Lecture,  Topic 4.4 Demonstration, Topic 4.1 Lecture,  Topic 4.1 Demonstration, worksheet17lec17-slides Lecture & demo quizzes for topics 4.1, 4.2, 4.3, 4.4

7

Mon

Feb 23

Lecture 18: Classification of Parallel Programs

 Topic 4.6 Lecture,  Topic 4.6 Demonstrationworksheet18lec18-slides  

 

Wed

Feb 25

Midterm Summary, Take-home Exam 1 distributed

   lec19-slidesExam 1 

 

Fri

Feb 27

No Lecture (Exam 1 due by 4pm today)

     Lecture & demo quizzes for topic 4.6, Exam 1

-

M-F

Feb 28- Mar 08

Spring Break

 

 

  

 

 

8

Mon

Mar 09

Lecture 19: Critical sections, Isolated construct, Parallel Spanning Tree algorithm

 Topic 5.1 Lecture, Topic 5.1 Demonstration, Topic 5.2 Lecture, Topic 5.2 Demonstration, Topic 5.3 Lecture, Topic 5.3 Demonstration    

 

 

Wed

Mar 11

Lecture 20: Speculative parallelization of isolated constructs

    Homework 4

 

 

Fri

Mar 13

Lecture 21: Read-Write Isolation, Atomic variables

 Topic 5.4 Lecture   , Topic 5.4 Demonstration   , Topic 5.5 Lecture, Topic 5.5 Demonstration, Topic 5.6 Lecture, Topic 5.6 Demonstration    

Homework 3, Lecture & demo quizzes for this week

9

Mon

Mar 16

Lecture 22: Actors

 Topic 6.1 Lecture , Topic 6.1 Demonstration , Topic 6.2 Lecture, Topic 6.2 Demonstration, Topic 6.3 Lecture, Topic 6.3 Demonstration   

 

 

 

Wed

Mar 18

Lecture 23: Actors (contd)

 Topic 6.4 Lecture   , Topic 6.4 Demonstration   , Topic 6.5 Lecture, Topic 6.5 Demonstration, Topic 6.6 Lecture, Topic 6.6 Demonstration   

 

 

 

Fri

Mar 20

Lecture 24: Monitors, Java Concurrent Collections, Linearizability of Concurrent Objects

 Topic 7.4 Lecture  

 

Lecture & demo quizzes for this week

10

Mon

Mar 23

Lecture 25: Linearizability (contd), Intro to Java Threads

 Topic 7.1 Lecture  

 

 

 

Wed

Mar 25

Lecture 26: Java Threads (contd), Java synchronized statement

 Topic 7.2 Lecture   

 

 

Fri

Mar 27

Lecture 27: Java synchronized statement (contd), advanced locking

 Topic 7.3 Lecture  

 

Lecture & demo quizzes for this week

11

Mon

Mar 30

Lecture 28: Safety and Liveness Properties

 Topic 7.5 Lecture  

 

 

 

Wed

Apr 01

Lecture 29: Dining Philosophers Problem

 Topic 7.6 Lecture  

Homework 5

Lecture & demo quizzes for this week, Homework 4

-

Fri

Apr 03

Midterm Recess

      

12

Mon

Apr 06

Lecture 30: Message Passing Interface (MPI)

     

 

 

Wed

Apr 08

Lecture 31: Partitioned Global Address Space (PGAS) languages

    

 

 

 

Fri

Apr 10

Lecture 32: Message Passing Interface (MPI, contd)

    

 

 

13

Mon

Apr 13

Lecture 33: Task Affinity with Places

     

 

 

Wed

Apr 15

Lecture 34: GPU Computing

    

Homework 6

Homework 5

 

Fri

Apr 17

Lecture 35: Memory Consistency Models

    

 

14

Mon

Apr 20

Lecture 36: Comparison of Parallel Programming Models

    

 

 

 

Wed

Apr 22

NO CLASS (time allocated to work on homeworks)

    

 

 

 

Fri

Apr 24

Lecture 37: Course Review (lectures 19-35), Last day of classes

     Homework 6 (penalty-free extension till May 1st)

-

 

April 29 - May 6

Scheduled final exam (exact date and time TBD)

 

 

  

 

 

Lab Schedule

Lab #

Date (2015)

Topic

Handouts

Code Examples

1

Jan 14

Infrastructure setup, Async-Finish Parallel Programming

lab1-handoutlab_1.zip

2

Jan 21

Abstract performance metrics with async & finish

lab2-handoutlab_2.zip

3

Jan 28

Futures and Data Race detection

lab3-handoutlab_3_futures.zip and lab_3_datarace.zip

4

Feb 04

Real Performance from Finish Accumulators and Loop-Level Parallelism

lab4-handout and lab4-slideslab_4_forall.zip and lab_4_hjviz.zip

5

Feb 11

Loop Chunking and Barrier Synchronization

lab5-handout and lab5-slides 

6

Feb 18

Futures vs. Data-Driven Futures

lab6-handout and lab6-slides 

7

Feb 25

Basics of Command line and Unix

lab7-handout and lab7-slides 

-

Mar 04

No lab this week — Spring Break

  

8

Mar 11

Isolated Statement and Atomic Variables, Actors

  

9

Mar 18

Java Threads

  
10

Mar 25

Java Locks

  

11

Apr 01

Message Passing Interface (MPI)

  

12

Apr 08

Map Reduce

  

13

Apr 15

TBD

  
-Apr 22No lab this week — Last Week of Classes  

Grading, Honor Code Policy, Processes and Procedures

Grading will be based on your performance on six homeworks (weighted 40% in all), two exams (weighted 20% each), weekly lab exercises (weighted 10% in all), and class participation including worksheets, in-class Q&A, Piazza participation, etc (weighted 10% in all).

The purpose of the homeworks is to train you to solve problems and to help deepen your understanding of concepts introduced in class. Homeworks are due on the dates and times specified in the course schedule. Please turn in all your homeworks using the subversion system set up for the class. 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.

As in 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.). The only requirement for use of your slip days is that you e-mail the instructors prior to the time the assignment is due. On group projects, each student in the group must use a slip day in order to extend the deadline for the assignment.  When slip days are used, you should clearly indicate so at the beginning of the assignment writeup.  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.

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 homework 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).  Exams 1 and 2 test your individual understanding and knowledge of the material. Exams are closed-book, and 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.

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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