NOTE: the most recent web site for COMP 322 is located here
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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 written assignments, programming assignments, and exams. Students interested in taking COMP 322 in Spring 2012 can find summary information here\.
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.
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- Java Concurrency in Practice by Brian Goetz with Tim Peierls, Joshua Bloch, Joseph Bowbeer, David Holmes and Doug Lea
- Principles of Parallel Programming by Calvin Lin and Lawrence Snyder
- The Art of Multiprocessor Programming by Maurice Herlihy and Nir Shavit
Lecture Schedule
| Day | Date (2011) | Topic | Handouts | Slides | Homework Assigned | Homework Due |
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1 | Mon | Jan 10 | Lecture 1: The What and Why of Parallel Programming |
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2 | Wed | Jan 12 | Lecture 2: Task Creation & Termination using Async & Finish |
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3 | Fri | Jan 14 | Lecture 3: Computation Graphs, Abstract Performance Metrics | lec3-handout | HW1 | ||
- | Mon | Jan 17 | School Holiday |
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4 | Wed | Jan 19 | Lecture 4: Futures --- Tasks with Return Values |
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5 | Fri | Jan 21 | Lecture 5: Parallel Array Sum and Array Reductions |
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6 | Mon | Jan 24 | Lecture 6: Data Races and How to Avoid Them |
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7 | Wed | Jan 26 | Lecture 7: Parallel Prefix Sum, Forall parallel loops |
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8 | Fri | Jan 28 | Lecture 8: Parallel Quicksort | lec8-handout |
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9 | Mon | Jan 31 | Lecture 9: PRAM model, Amdahl's Law |
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10 | Wed | Feb 02 | Lecture 10: Critical sections and the Isolated statement | lec10-handout |
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- | Fri | Feb 04 | No Lecture, School closed due to inclement weather |
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11 | Mon | Feb 07 | Lecture 11: Abstract vs Real Performance, Work-sharing & Work-stealing schedulers |
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12 | Wed | Feb 09 | Lecture 12: Barrier Synchronization in Forall Loops |
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13 | Fri | Feb 11 | Lecture 13: Barrier Synchronization in Forall Loops (contd) |
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14 | Mon | Feb 14 | Lecture 14: Point-to-point Synchronization and Phasers |
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15 | Wed | Feb 16 | Lecture 15: Point-to-point Synchronization and Phasers (contd) |
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16 | Fri | Feb 18 | Lecture 16: Guest Lecture on Bitonic Sort (John Mellor-Crummey) |
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17 | Mon | Feb 21 | Lecture 17: Advanced Phaser topics |
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18 | Wed | Feb 23 | Lecture 18: Midterm Summary |
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- | Fri | Feb 25 | No lecture, Midterm Exam due today |
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- | M-F | Feb 28 - Mar 04 | Spring Break |
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19 | Mon | Mar 07 | Lecture 19: Java Atomic Variables |
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20 | Wed | Mar 09 | Lecture 20: Java Concurrent Collections |
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21 | Fri | Mar 11 | Lecture 21: Linearizability of Concurrent Objects |
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22 | Mon | Mar 14 | Lecture 22: Task Affinity with Places |
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23 | Wed | Mar 16 | Lecture 23: Task Affinity with Places, contd. |
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24 | Fri | Mar 18 | Lecture 24: Map Reduce |
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25 | Mon | Mar 21 | Lecture 25: Dataflow Programming and Data-Driven Futures |
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26 | Wed | Mar 23 | Lecture 26: Dataflow Programming with Intel Concurrent Collections |
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- | Fri | Mar 25 | Midterm Recess |
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27 | Mon | Mar 28 | Lecture 27: Java Threads |
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28 | Wed | Mar 30 | Lecture 28: Java Threads (contd), synchronized statement |
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29 | Fri | Apr 01 | Lecture 29: Java synchronized statement with wait/notify |
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30 | Mon | Apr 04 | Lecture 30: Advanced locking in Java |
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31 | Wed | Apr 06 | Lecture 31: Java Executors and Synchronizers |
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32 | Fri | Apr 08 | Lecture 32: Volatile Variables and Java Memory Model |
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33 | Mon | Apr 11 | Lecture 33: GPGPU programming with CUDA |
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34 | Wed | Apr 13 | Lecture 34: CUDA contd. |
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35 | Fri | Apr 15 | Lecture 35: Liveness and Progress Guarantees |
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36 | Mon | Apr 18 | Lecture 36: Introduction to MPI |
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37 | Wed | Apr 20 | Lecture 37: Introduction to MPI (contd) |
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38 | Fri | Apr 22 | Lecture 38: Course Summary |
| Final Exam (Take-home) | HW7 | |
- | Fri | Apr 29 |
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| Final Exam (Take-home) |
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Lab # | Date (2011) | Topic | Handouts |
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1 | Jan 11, 12 | Infrastructure setup | |
2 | Jan 18, 19 | Abstract performance metrics with async & finish | |
3 | Jan 25, 26 | Data race detection | |
4 | Feb 01, 02 | Points, regions, forall loops | |
5 | Feb 08, 09 | Abstract vs Real Performance, Work-sharing & Work-stealing schedulers | |
6 | Feb 15, 16 | Barriers and Phasers | |
- | Feb 22, 23 | No lab (midterm week) |
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7 | Mar 08, 09 | Atomic Variables | |
8 | Mar 15, 16 | Places | |
9 | Mar 22, 23 | Data Driven Tasks | |
10 | Mar 29, 30 | Java Concurrency I | |
11 | Apr 05, 06 | Java Concurrency II | |
12 | Apr 12, 13 | CUDA | |
13 | Apr 19, 20 | MPI |
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.