CS 181E Resource Site: Fundamentals of Parallel Programming (Fall
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Instructor:
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Prof. Vivek Sarkar, Sprague Hall 414
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Grutors:
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Matt Prince
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Co-instructor:
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Prof. Ran Libeskind-Hadas
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Mary Rachel Stimson
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Habanero Research Staff:
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Vincent Cavé, Shams Imam
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Lectures:
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2012
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Lecture times:
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MWF 1:00 - 1:50pm
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Labs:
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Ryon 102
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Lab times:
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Tuesday, 4:00 - 5:20pm (Section 3)
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Wednesday, 3:30 - 4:50pm (Section 2)
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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:
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Introduction
This web site contains resources for the Fall 2012 offering of CS 181E at Harvey Mudd COllege. For general information on the course, please go to the course home page.
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Lecture Schedule
| Day | Date (2012) | Topic | Slides | Audio (Panopto) | Code Examples | Homework Assigned | Homework Due |
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1 | Wed | Sep 05 | Lecture 1: The What and Why of Parallel Programming |
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2 | Mon | Sep 10 | Lecture 2: Async-Finish Parallel Programming and Computation Graphs |
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3 | Wed | Sep 12 | Lecture 3: Computation Graphs, Abstract Performance Metrics, Array Reductions | HW1 | ||||
4 | Mon | Sep 17 | School Holiday |
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5 | Wed | Sep 19 | Lecture 4: Parallel Speedup, Efficiency, Amdahl's Law |
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6 | Mon | Sep 24 | Lecture 5: Data & Control Flow with Async Tasks, Data Races | (See Lab 3) |
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7 | Wed | Sep 26 | Lecture 6: Memory Models, Atomic Variables | (See Lab 3) |
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8 | Mon | Oct 01 | Lecture 7: Memory Models (contd), Futures --- Tasks with Return Values |
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9 | Wed | Oct 03 | Lecture 8: Futures (contd), Dataflow Programming, Data-Driven Tasks |
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10 | Mon | Oct 08 | Lecture 9: Abstract vs. Real Performance, seq clause, forasync loops |
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11 | Wed | Oct 10 | Lecture 10: Forasync Chunking, Parallel Prefix Sum algorithm |
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12 | Mon | Oct 15 | Lecture 11: Parallel Prefix Sum (contd), Parallel Quicksort |
| HW3 (HJ Programming Assignment), SeqScoring.hj, X.txt, Y.txt, BigSeq.zip |
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13 | Wed | Oct 17 | Lecture 12: Finish Accumulators, Forall Loops and Barrier Synchronization |
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14 | Mon | Oct 22 | Lecture 13: Forall Loops and Barrier Synchronization (contd) |
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15 | Wed | Oct 24 | Lecture 14: Point-to-point Synchronization and Phasers |
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16 | Mon | Oct 29 | Lecture 15: Phaser Accumulators, Bounded Phasers |
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17 | Wed | Oct 31 | Lecture 16: Summary of Barriers and Phasers |
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18 | Mon | Nov 05 | Lecture 17: Task Affinity with Places |
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19 | Wed | Nov 07 | Lecture 18: Task Affinity with Places (contd) |
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20 | Mon | Nov 12 | Lecture 19: Midterm Summary |
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21 | Wed | Nov 14 | Lecture 20: Critical sections and the Isolated statement |
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22 | Mon | Nov 19 | Lecture 21: Isolated statement (contd), Monitors, Actors |
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23 | Wed | Nov 21 | Lecture 22: Actors (contd) |
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24 | Mon | Nov 26 | Lecture 23: Linearizability of Concurrent Objects |
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25 | Wed | Nov 28 | Lecture 24: Linearizability of Concurrent Objects (contd) |
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26 | Mon | Mar 16 | Lecture 25: Safety and Liveness Properties |
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27 | Wed | Mar 19 | Lecture 26: Parallel Programming Patterns |
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28 | Mon | Mar 21 | Lecture 27: Introduction to Java Threads |
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29 | Wed | Mar 26 | Lecture 28: Bitonic Sort (guest lecture by Prof. John Mellor-Crummey) |
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30 | Mon | Mar 28 | Lecture 29: Java Threads (contd), Java synchronized statement |
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31 | Wed | Mar 30 | Lecture 30: Java synchronized statement (contd), advanced locking |
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32 | Mon | Apr 02 | Lecture 31: Java Executors and Synchronizers |
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33 | Wed | Apr 04 | Lecture 32: Volatile Variables and Java Memory Model |
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34 | Mon | Apr 06 | Lecture 33: Message Passing Interface (MPI) |
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35 | Wed | Apr 09 | Lecture 34: Message Passing Interface (MPI, contd) |
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36 | Mon | Apr 11 | Lecture 35: Cloud Computing, Map Reduce |
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37 | Wed | Apr 13 | Lecture 36: Map Reduce (contd) |
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38 | Mon | Apr 16 | Lecture 37: Speculative parallelization of isolated blocks (Guest lecture by Prof. Swarat Chaudhuri) |
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39 | Wed | Apr 18 | Lecture 38: Comparison of Parallel Programming Models |
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40 | Mon | Apr 20 | Lecture 39: Course Review |
| Exam 2 (Take-home) | HW6 |
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