COMP 322: Fundamentals of Parallel Programming (Spring 2013)
Instructor: | Prof. Vivek Sarkar, DH 3131 | Graduate TA: | Kumud Bhandari |
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| Please send all emails to comp322-staff at rice dot edu | Graduate TA: | |
Assistant: | Sherry Nassar, sherry.nassar@rice.edu, DH 3137 | Graduate TA: | Sriraj Paul |
Graduate TA: | Rishi Surendran | ||
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| Undergrad TA: | Annirudh Prasad |
Cross-listing: | ELEC 323 | Undergrad TA: | Yunming Zhang |
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| HJ consultants: | Vincent Cavé, Shams Imam |
Lectures: | Herzstein Hall 212 | Lecture times: | MWF 1:00 - 1:50pm |
Labs: | Ryon 102 | Lab times: | Tuesday, 4:00 - 5:15pm (Section 3) |
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| Wednesday, 3:30 - 4:50pm (Section 2) |
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| Thursday, 4:00 - 5:15pm (Section 1) |
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 get a strong grasp of parallel programming foundations, the classes and homeworks will place equal emphasis on advancing both theoretical and practical knowledge. The programming component of the course work will initially use a simple parallel extension to the Java language called Habanero-Java (HJ), developed in the Habanero Multicore Software Research project at Rice University. Later in the course, we will introduce you to some real-world parallel programming models including Java Concurrency, .Net Task Parallel Library, MapReduce, CUDA and MPI. The use of Java will be confined to a subset of the Java language that should also be accessible to C programmers --- advanced Java features (e.g., wildcards in generics) will not be used. 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; any parallel programming primitives should be easily recognizable based on the primitives 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 four modules:
- Deterministic Shared-Memory Parallelism: creation and coordination of parallelism, collective & point-to-point synchronization (phasers, barriers), abstract performance metrics (work, span, 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), parallel sorting algorithms.
- Nondeterministic Shared-Memory Parallelism and Concurrency: critical sections, atomicity, isolation, high level data races, nondeterminism, linearizability, liveness/progress guarantees, actors, request-response parallelism
- Distributed-Memory Parallelism and Locality: memory hierarchies, cache affinity, false sharing, message-passing (MPI), communication overheads (bandwidth, latency), MapReduce, systolic arrays, accelerators, GPGPUs.
- Current Practice — today's Parallel Programming Models and Challenges: Java Concurrency, locks, condition variables, semaphores, memory consistency models, comparison of parallel programming models (.Net Task Parallel Library, OpenMP, CUDA, OpenCL); energy efficiency, data movement, resilience.
Prerequisite
The prerequisite 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.
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)
- Module 4 handout (Current Practice — today's Parallel Programming Models and Challenges)
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:
- 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
Past Offerings of COMP 322
Lecture Schedule
| Day | Date (2013) | Topic | Reading | Slides | Audio (Panopto) | Code Examples | Homework Assigned | Homework Due |
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1 | Mon | Jan 7 | Lecture 1: The What and Why of Parallel Programming | Module 1: Sections1.1, 1.2, 2.1, 2.2 |
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2 | Wed | Jan 9 | Lecture 2: Async-Finish Parallel Programming and Computation Graphs | Module 1: Sections 1.3, 3.1, 3.2 |
| HW1, quicksort.hj |
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3 | Fri | Jan 11 | Lecture 3: Computation Graphs, Abstract Performance Metrics, Array Reductions | ||||||
4 | Mon | Jan 14 | Lecture 4: Parallel Speedup, Efficiency, Amdahl's Law | ||||||
5 | Wed | Jan 16 | Lecture 5: Data & Control Flow with Async Tasks, Data Races | ||||||
6 | Fri | Jan 18 | Lecture 6: Memory Models, Atomic Variables | ||||||
- | Mon | Jan 21 | School Holiday (Martin Luther King, Jr. Day) | ||||||
7 | Wed | Jan 23 | Lecture 7: Memory Models (contd), Futures --- Tasks with Return Values | HW2 | HW1 | ||||
8 | Fri | Jan 25 | Lecture 8: Futures (contd), Dataflow Programming, Data-Driven Tasks | ||||||
9 | Mon | Jan 28 | Lecture 9: Abstract vs. Real Performance, seq clause, forasync loops | ||||||
10 | Wed | Jan 30 | Lecture 10: Forasync Chunking, Parallel Prefix Sum algorithm | ||||||
11 | Fri | Feb 1 | Lecture 11: Parallel Prefix Sum (contd), Parallel Quicksort | ||||||
12 | Mon | Feb 04 | Lecture 12: Finish Accumulators, Forall Loops and Barrier Synchronization | ||||||
13 | Wed | Feb 06 | Lecture 13: Forall Loops and Barrier Synchronization (contd) | HW3 | HW2 | ||||
14 | Fri | Feb 08 | Lecture 14: Point-to-point Synchronization and Phasers | ||||||
15 | Mon | Feb 11 | Lecture 15: Phaser Accumulators, Bounded Phasers | ||||||
16 | Wed | Feb 13 | Lecture 16: Summary of Barriers and Phasers | ||||||
17 | Fri | Feb 15 | Lecture 17: Task Affinity with Places | ||||||
18 | Mon | Feb 18 | Lecture 18: Task Affinity with Places (contd) | ||||||
19 | Wed | Feb 20 | Lecture 19: Midterm Summary, Take-home Exam 1 distributed | HW4 | HW3 | ||||
- | F | Feb 22 | No Lecture (Exam 1 due by 4pm today) | ||||||
- | M-F | Feb 25- Mar 01 | Spring Break |
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20 | Mon | Mar 04 | Lecture 20: Critical sections and the Isolated statement |
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21 | Wed | Mar 06 | Lecture 21: Isolated statement (contd), Monitors, Actors |
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22 | Fri | Mar 08 | Lecture 22: Actors (contd) |
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23 | Mon | Mar 11 | Lecture 23: Linearizability of Concurrent Objects |
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24 | Wed | Mar 13 | Lecture 24: Linearizability of Concurrent Objects (contd) |
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25 | Fri | Mar 15 | Lecture 25: Safety and Liveness Properties |
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26 | Mon | Mar 18 | Lecture 26: Parallel Programming Patterns |
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27 | Wed | Mar 20 | Lecture 27: Introduction to Java Threads | HW5 | HW4 | ||||
28 | Fri | Mar 22 | Lecture 28: Bitonic Sort |
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29 | Mon | Mar 25 | Lecture 29: Java Threads (contd), Java synchronized statement |
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30 | Wed | Mar 27 | Lecture 30: Java synchronized statement (contd), advanced locking |
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- | Fri | Mar 29 | Midterm Recess | ||||||
31 | Mon | Apr 01 | Lecture 31: Java Executors and Synchronizers |
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32 | Wed | Apr 03 | Lecture 32: Volatile Variables and Java Memory Model |
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33 | Fri | Apr 05 | Lecture 33: Message Passing Interface (MPI) |
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34 | Mon | Apr 08 | Lecture 34: Message Passing Interface (MPI, contd) |
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35 | Wed | Apr 10 | Lecture 35: Cloud Computing, Map Reduce |
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36 | Fri | Apr 12 | Lecture 36: Map Reduce (contd) |
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37 | Mon | Apr 15 | Lecture 37: Speculative parallelization of isolated blocks |
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38 | Wed | Apr 17 | Lecture 38: Comparison of Parallel Programming Models |
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39 | Fri | Apr 19 | Lecture 39: Course Review, Take-home Exam 2 distributed | ||||||
- | Fri | Apr 25 | Exam 2 due |
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Lab Schedule
Lab # | Date (2013) | Topic | Handouts | Code Examples | Solutions |
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1 | Jan 08, 09, 10 | Infrastructure setup, Async-Finish Parallel Programming | lab1-handout | HelloWorldError.hj, ReciprocalArraySum.hj |
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2 | Jan 15, 16, 17 | Abstract performance metrics with async & finish |
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3 | Jan 22, 23, 24 | Data race detection and repair |
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4 | Jan 29, 30, 31 | Real performance, work-sharing and work-stealing runtimes, futures |
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5 | Feb 05, 06, 07 | Data-driven futures |
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6 | Feb 12, 13, 14 | Barriers and Phasers |
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- | Feb 19, 20, 21 | No lab (Exam 1 week) |
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7 | Mar 05, 06, 07 | Atomic Variables and Isolated Statement | |||
8 | Mar 12, 13, 14 | Actors | |||
9 | Mar 19, 20, 21 | Java Threads | |||
- | Mar 26, 27, 28 | No lab (HW4 deadline, midterm recess) | |||
10 | Apr 02, 03, 04 | Java Locks |
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11 | Apr 09, 10, 11 | Message Passing Interface (MPI) |
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12 | Apr 16, 17, 18 | Map Reduce |
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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 re 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. 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.