NOTE: the most recent web site for COMP 322 is located here
COMP 322: Fundamentals of Parallel Programming (Spring 2011)
Prof. Vivek Sarkar, DH 3131
Please send all emails to comp322-staff at rice dot edu
Amanda Nokleby, firstname.lastname@example.org, DH 3137
Duncan Hall (DH) 1042
Tuesday 2:30-3:50pm (Sec 1), Wednesday 3:30-4:50pm (Sec 2)
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 211 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 written assignments, programming assignments, and exams. Students interested in taking COMP 322 in Spring 2012 can find summary information here\.
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:
- 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 1: The What and Why of Parallel Programming
Lecture 2: Task Creation & Termination using Async & Finish
Lecture 3: Computation Graphs, Abstract Performance Metrics
Lecture 4: Futures --- Tasks with Return Values
Lecture 5: Parallel Array Sum and Array Reductions
Lecture 6: Data Races and How to Avoid Them
Lecture 7: Parallel Prefix Sum, Forall parallel loops
Lecture 8: Parallel Quicksort
Lecture 9: PRAM model, Amdahl's Law
Lecture 10: Critical sections and the Isolated statement
No Lecture, School closed due to inclement weather
Lecture 11: Abstract vs Real Performance, Work-sharing & Work-stealing schedulers
Lecture 12: Barrier Synchronization in Forall Loops
Lecture 13: Barrier Synchronization in Forall Loops (contd)
Lecture 14: Point-to-point Synchronization and Phasers
Lecture 15: Point-to-point Synchronization and Phasers (contd)
Lecture 16: Guest Lecture on Bitonic Sort (John Mellor-Crummey)
Lecture 17: Advanced Phaser topics
Lecture 18: Midterm Summary
Midterm Exam (Take-home)
No lecture, Midterm Exam due today
Midterm Exam (Take-home)
Feb 28 - Mar 04
Lecture 19: Java Atomic Variables
Lecture 20: Java Concurrent Collections
Lecture 21: Linearizability of Concurrent Objects
Lecture 22: Task Affinity with Places
Lecture 23: Task Affinity with Places, contd.
Lecture 24: Map Reduce
Lecture 25: Dataflow Programming and Data-Driven Futures
Lecture 26: Dataflow Programming with Intel Concurrent Collections
Lecture 27: Java Threads
Lecture 28: Java Threads (contd), synchronized statement
Lecture 29: Java synchronized statement with wait/notify
Lecture 30: Advanced locking in Java
Lecture 31: Java Executors and Synchronizers
Lecture 32: Volatile Variables and Java Memory Model
Lecture 33: GPGPU programming with CUDA
Lecture 34: CUDA contd.
Lecture 35: Liveness and Progress Guarantees
Lecture 36: Introduction to MPI
Lecture 37: Introduction to MPI (contd)
Lecture 38: Course Summary
Final Exam (Take-home)
Final Exam (Take-home)
Lab Schedule ||
Jan 11, 12
Jan 18, 19
Abstract performance metrics with async & finish
Jan 25, 26
Data race detection
Feb 01, 02
Points, regions, forall loops
Feb 08, 09
Abstract vs Real Performance, Work-sharing & Work-stealing schedulers
Feb 15, 16
Barriers and Phasers
Feb 22, 23
No lab (midterm week)
Mar 08, 09
Mar 15, 16
Mar 22, 23
Data Driven Tasks
Mar 29, 30
Java Concurrency I
Apr 05, 06
Java Concurrency II
Apr 12, 13
Apr 19, 20
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.