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COMP 322: Fundamentals of Parallel Programming (Spring 2014)

Instructor:

Prof. Vivek Sarkar, DH 3080

Graduate TA:

Kumud Bhandari

 

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

Graduate TA:

Rishi Surendran

Assistant:

Penny Anderson, anderson@rice.edu, DH 3080

Graduate TA:

Yunming Zhang

  Undergrad TA: Wenxuan Cai

 

 

Undergrad TA:

Kyle Kurihara

Cross-listing:

ELEC 323

Undergrad TA:

Max Payton

 

 

Course consultants:

Vincent Cavé, Shams Imam, Maggie Tang, Bing Xue

Lectures:

Herzstein Hall 212

Lecture times:

MWF 1:00 - 1:50pm

Labs:

Symonds II

Lab times:

Monday, 4:00 - 5:30pm (Section A01, Staff: Yunming, Kumud, Wenxuan, Maggie)

 

 

 

Wednesday, 4:30 - 6:00pm (Section A02, Staff: Rishi, Kyle, Max, Bing)

Course Objectives

The goal of COMP 322 is to introduce you to the fundamentals of parallel programming and parallel algorithms, using 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 model that you may encounter in the future, and also prepare you for studying advanced topics related to parallelism and concurrency in more advanced courses such as COMP 422.

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 Multicore 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.

Course Overview  

COMP 322 provides the student with a comprehensive introduction to the building blocks of parallel software, which includes the following concepts:

  • Primitive constructs for task creation & termination, synchronization, task and data distribution
  • Abstract models: parallel computations, computation graphs, Flynn's taxonomy (instruction vs. data parallelism), PRAM model
  • Parallel algorithms for data structures that include arrays, lists, strings, trees, graphs, and key-value pairs
  • Common parallel programming patterns including task parallelism, pipeline parallelism, data parallelism, divide-and-conquer parallelism, map-reduce, concurrent event processing including graphical user interfaces.

These concepts will be introduced in three modules: 

  1. Deterministic Shared-Memory 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 arrays.
  2. Nondeterministic Shared-Memory Parallelism and 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. Distributed-Memory Parallelism and Locality: memory hierarchies, cache affinity, data movement, message-passing (MPI), communication overheads (bandwidth, latency), MapReduce, accelerators, GPGPUs, CUDA, OpenCL, energy efficiency, resilience.

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 221 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:

  • Module 1 handout (Deterministic Shared-Memory Parallelism)
  • Module 2 handout (Nondeterministic Shared-Memory Parallelism and Concurrency)
  • Module 3 handout (Distributed-Memory Parallelism and Locality)

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

 

 lec35-slides

Week

Day

Date (2014)

Topic

Reading

Videos

In-class Worksheets

Slides

Code Examples

Work Assigned

Work Due

1

Mon

Jan 13

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

Demo File: ReciprocalArraySum.java

Topic 1.1 Lecture Quiz,  Topic 1.1 Demo Quiz

 

 

Wed

Jan 15

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-slidesDemo File: Search.java

Topic 1.2 Lecture Quiz , Topic 1.2 Demo Quiz , Topic 1.3 Lecture Quiz , Topic 1.3 Demo Quiz

 

 

Fri

Jan 17

Lecture 3: , Abstract Performance Metrics, Multiprocessor Scheduling

Module 1: Section 1.4Topic 1.4 Lecture, Topic 1.4 Demonstrationworksheet3lec3-slides

Worksheet File: Search.java

Homework 1 Files: QuicksortUtil.java , QuicksortSeq.java , QuicksortPar.java

Homework 1, Topic 1.4 Lecture Quiz , Topic 1.4 Demo Quiz, Topic 1.6 Lecture Quiz , Topic 1.6 Demo Quiz

2

Mon

Jan 20

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

       

 

Wed

Jan 22

Lecture 4:   Parallel Speedup and Amdahl's Law

Module 1: Section 1.5Topic 1.5 Lecture, Topic 1.5 Demonstrationworksheet4lec4-slidesDemo File: VectorAdd.javaTopic 1.5 Lecture Quiz , Topic 1.5 Demo Quiz 

 

Fri

Jan 24

No lecture (inclement weather)

      All 12 lecture & demo quizzes in Unit 1 are due by 5pm CST today

3

Mon

Jan 27

Lecture 5: Future Tasks, Functional Parallelism

Module 1: Section 2.1Topic 2.1 Lecture , Topic 2.1 Demonstrationworksheet5lec5-slidesDemo File(s): ReciprocalArraySumFutures.java, BinaryTreesSeq.java, BinaryTrees.java  

 

Wed

Jan 29

Lecture 6: Finish Accumulators

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

Demo File: Nqueens.java

Worksheet5.java, nqueens.java

 

 

 

Fri

Jan 31

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   lec7-slidesDemo File: ReciprocalArraySum.java Homework 1

4

Mon

Feb 03

Lecture 8: Map Reduce

Module 1: Section 2.4Topic 2.4 Lecture , Topic 2.4 Demonstration  worksheet8lec8-slides

Demo File(s): WordCount.java, words.txt

Worksheet Files: WordCount.java , words.txt

Homework 2 Files: GeneralizedReduce.java, GeneralizedReduceApp.java, SumReduction.java, TestSumReduction.java

Homework 2 

 

Wed

Feb 05

Lecture 9: Memoization

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

Demo File: PascalsTriangleWithFuture.java

Worksheet File: PascalsTriangleMemoized.java

Worksheet Solution: PascalsTriangleMemoizedSolution.java

  

 

Fri

Feb 07

Lecture 10: Abstract vs. Real Performance

  worksheet10lec10-slides   

5

Mon

Feb 10

Lecture 11: Loop-Level Parallelism, Parallel Matrix Multiplication

 Topic 3.1 Lecture, Topic 3.1 Demonstration, Topic 3.2 Lecture , Topic 3.2 Demonstration  worksheet11lec11-slidesDemo File: ForallWithIterable.java, VectorAddForall.java, MatrixMultiplicationMetrics.java  

 

Wed

Feb 12

Lecture 12: Iteration Grouping (Chunking), Barrier Synchronization

 Topic 3.3 Lecture , Topic 3.3 Demonstration , Topic 3.4 Lecture , Topic 3.4 Demonstration  worksheet12lec12-slidesDemo File: MatrixMultiplicationPerformance.java, BarrierInForall.java  

 

Fri

Feb 14

Lecture 13: Iterative Averaging Revisited

 Topic 3.5 Lecture , Topic 3.5 Demonstration , Topic 3.6 Lecture , Topic 3.6 Demonstration  worksheet13lec13-slides

Demo File: OneDimAveragingGrouped.java, OneDimAveragingBarrier.java

Worksheet File: OneDimAveragingBarrier.java

 

 

6

Mon

Feb 17

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

 Topic 4.5 Lecture , Topic 4.5 Demonstrationworksheet14lec14-slidesDemo File: DataDrivenFutures4.java Homework 2

 

Wed

Feb 19

Lecture 15: Review of Module-1 HJ-lib API's

  worksheet15lec15-slidesHomework 3 Files: SeqScoring.java Homework 3 

 

Fri

Feb 21

Lecture 16: Point-to-point Synchronization with Phasers

 Topic 4.2 Lecture , Topic 4.2 Demonstrationworksheet16lec16-slidesDemo File: Phaser3Asyncs.java  

7

Mon

Feb 24

Lecture 17: Phasers (contd), Signal Statement, Fuzzy Barriers

 Topic 4.1 Lecture , Topic 4.1 Demonstrationworksheet17lec17-slidesDemo File: PhaserSignal.java  

 

Wed

Feb 26

Lecture 18: Midterm Summary, Take-home Exam 1 distributed

   lec18-slides Exam 1 

 

F

Feb 28

No Lecture (Exam 1 due by 4pm today)

      Exam 1

-

M-F

Feb 28- Mar 09

Spring Break

 

 

  

 

 

 

8

Mon

Mar 10

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 Demonstrationworksheet19lec19-slides  

 

 

Wed

Mar 12

Lecture 20: Speculative parallelization of isolated constructs (Guest lecture by Prof. Swarat Chaudhuri)

  worksheet20lec20-slides  

Homework 3

 

Fri

Mar 14

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 Demonstrationworksheet21lec21-slides  

 

9

Mon

Mar 17

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 Demonstrationworksheet22lec22-slides

Homework 4 Files: hw4_files.zip  

Homework 4

 

 

Wed

Mar 19

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 Demonstrationworksheet23lec23-slides 

 

 

 

Fri

Mar 21

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

 Topic 7.4 Lectureworksheet24lec24-slides

 

 

 

10

Mon

Mar 24

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

 Topic 7.1 Lectureworksheet25lec25-slides

 

 

 

 

Wed

Mar 26

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

 Topic 7.2 Lectureworksheet26lec26-slides  

 

 

Fri

Mar 28

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

 Topic 7.3 Lectureworksheet27lec27-slides

 

 

 

11

Mon

Mar 31

Lecture 28: Safety and Liveness Properties

 Topic 7.5 Lectureworksheet28lec28-slides

 

 

 

 

Wed

Apr 02

Lecture 29: Dining Philosophers Problem

 Topic 7.6 Lectureworksheet29lec29-slides

 

 

Homework 4 (due by 11:55pm on April 2nd)

-

Fri

Apr 04

Midterm Recess

       

12

Mon

Apr 07

Lecture 30: Message Passing Interface (MPI)

  worksheet30lec30-slidesHomework 5 files: hw5_files.zipHomework 5

 

 

Wed

Apr 09

Lecture 31: Partitioned Global Address Space (PGAS) languages (Guest lecture by Prof. John Mellor-Crummey)

  worksheet31lec31-slides

 

 

 

 

Fri

Apr 11

Lecture 32: Message Passing Interface (MPI, contd)

  worksheet32lec32-slides

 

 

 

13

Mon

Apr 14

Lecture 33: Task Affinity with Places

  worksheet33lec33-slides  

 

 

Wed

Apr 16

Lecture 34: GPU Computing

  worksheet34lec34-slides

 

 

 

 

Fri

Apr 18

Lecture 35: Memory Consistency Models

  worksheet35

lec35-slides

Homework 6 (written only)

 

14

Mon

Apr 21

Lecture 36: Comparison of Parallel Programming Models

  worksheet36lec36-slides 

 

Homework 5 (due by 11:55pm on Monday, April 21st)

 

Wed

Apr 23

NO CLASS (time allocated to work on homeworks)

    

 

 

 

 

Fri

Apr 25

Lecture 37: Course Review (lectures 19-35), Take-home Exam 2 distributed, Last day of classes

     Exam 2Homework 6 (due by 11:55pm on April 25th, penalty-free extension till May 2nd)

-

Fri

May 02

Exam 2 due by 4pm today

 

 

  

 

 

Exam 2

Lab Schedule

Lab #

Date (2014)

Topic

Handouts

Code Examples

1

Jan 13, 15

Infrastructure setup, Async-Finish Parallel Programming

lab1-handoutHelloWorldError.java, ReciprocalArraySum.java

-

Jan 20, 22

No lab this week — Jan 20 is Martin Luther King, Jr. Day

  

2

Jan 27, 29

Abstract performance metrics with async & finish

lab2-handoutArraySum1.java , ArraySumUtil.java Search2.java , ArraySumLoop.java , ArraySumRecursive.java

3

Feb 03, 05

Futures

lab3-handoutArraySum2.java, ArraySum4.java, BinaryTrees.java

4

Feb 10, 12

Real Performance from Finish Accumulators and Loop-Level Parallelism

lab4-handout

Nqueens.java, OneDimAveraging.java, Linux/Sugar Tutorial

5

Feb 17, 19

Futures vs. Data-Driven Futures

lab5-handoutMatrixEval.java, test.txt

6

Feb 24, 26

Barriers and Phasers

lab6-handoutOneDimAveraging.java

-

Mar 03, 05

No lab this week — Spring Break

  

7

Mar 10, 12

Isolated Statement and Atomic Variables

lab7-handoutspanning_tree_seq.java

8

Mar 17, 19

Actors

lab8-handoutPiSerial1.java PiActor1.java PiSerial2.java PiActor2.java PiUtil.java Sieve.java SieveSerial.java
9

Mar 24, 26

Java Threads

lab9-handoutnqueens_hj.java spanning_tree_atomic_hj.java

10

Mar 31, Apr 02

Java Locks

lab10-handoutlab10.zip

11

Apr 07, 09

Message Passing Interface (MPI)

lab11-handoutlab_11.zip

12

Apr 14, 16

Map Reduce

lab12-handout 
-Apr 21, 23No 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 lecture & lab quizzes (weighted 10% in all), and class participation (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 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, quizzes 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 and all quizzes are pledged under the Honor Code.  They test your individual understanding and knowledge of the material. Collaboration on quizzes and exams is strictly forbidden.  Quizzes are open-book and exams are closed-book.  Finally, it is also your responsibility to protect your homeworks, quizzes 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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