COMP 322: Fundamentals of Parallel Programming (Spring 2015)
Instructor: | Prof. Vivek Sarkar, DH 3131 | ||
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Co-Instructor: | Dr. Eric Allen | Graduate TAs: | Prasanth Chatarasi, Peng Du, Xian Fan, Max Grossman |
| Please send all emails to comp322-staff at rice dot edu | Undergraduate TAs: | Matthew Bernhard, Nicholas Hanson-Holtry, Yi Hua, | |
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| Yoko Li, Ayush Narayan, Derek Peirce, |
Cross-listing: | ELEC 323 | Maggie Tang, Wei Zeng, Glenn Zhu | |
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| Course consultants: | Vincent Cavé, John Greiner, Shams Imam |
Lectures: | Herzstein Hall 210 | Lecture times: | MWF 1:00pm - 1:50pm |
Labs: | DH 1064 (Section A01), DH 1070 (Section A02) | Lab times: | Wednesday, 07:00pm - 08:30pm |
Course Syllabus
A summary PDF file containing the course syllabus for the course can be found here . Much of the syllabus information is also included below in this course web site, along with some additional details that are not included in the syllabus.
Course Objectives
The primary goal of COMP 322 is to introduce you to the fundamentals of parallel programming and parallel algorithms, by following 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 system that you may encounter in the future, and also prepare you for studying advanced topics related to parallelism and concurrency in courses such as COMP 422.
The desired learning outcomes fall into three major areas (course modules):
1) 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 algorithms.
2) 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) Locality & Distribution: memory hierarchies, locality, cache affinity, data movement, message-passing (MPI), communication overheads (bandwidth, latency), MapReduce, accelerators, GPGPUs, CUDA, OpenCL.
To achieve these learning outcomes, each class period will include time for both instructor lectures and in-class exercises based on assigned reading and videos. The lab exercises will be used to help students gain hands-on programming experience with the concepts introduced in the lectures.
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 Extreme Scale 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.
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 321 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. The links to the latest versions on Owlspace are included below:
- Module 1 handout (Parallelism)
- Module 2 handout (Concurrency)
- Module 3 handout (Distribution 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:
- 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
Week | Day | Date (2015) | Topic | Assigned Reading | Assigned Videos (Quizzes due by Friday of each week) | In-class Worksheets | Slides | Work Assigned | Work Due |
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1 | Mon | Jan 12 | Lecture 1: The What and Why of Parallel Programming, Task Creation and Termination (Async, Finish) | Module 1: Sections 0.1, 0.2, 1.1 | worksheet1 | lec1-slides |
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| Wed | Jan 14 | Lecture 2: Computation Graphs, Ideal Parallelism | Module 1: Sections 1.2, 1.3 | Topic 1.2 Lecture, Topic 1.2 Demonstration, Topic 1.3 Lecture, Topic 1.3 Demonstration | worksheet2 | lec2-slides |
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| Fri | Jan 16 | Lecture 3: , Abstract Performance Metrics, Multiprocessor Scheduling | Module 1: Section 1.4 | Topic 1.4 Lecture, Topic 1.4 Demonstration | worksheet3 | lec3-slides | Homework 1 | Lecture & demo quizzes for topics 1.1, 1.2, 1.3, 1.4 | |
2 | Mon | Jan 19 | No lecture, School Holiday (Martin Luther King, Jr. Day) | ||||||
| Wed | Jan 21 | Lecture 4: Parallel Speedup and Amdahl's Law | Module 1: Sections 1.5, 1.6 | Topic 1.5 Lecture, Topic 1.5 Demonstration, Topic 1.6 Lecture, Topic 1.6 Demonstration, | worksheet4 | lec4-slides | ||
| Fri | Jan 23 | Lecture 5: Future Tasks, Functional Parallelism | Module 1: Section 2.1 | Topic 2.1 Lecture , Topic 2.1 Demonstration | Lecture & demo quizzes for topics 1.5, 1.6, 2.1 | |||
3 | Mon | Jan 26 | Lecture 6: Finish Accumulators | Module 1: Section 2.3 | Topic 2.3 Lecture , Topic 2.3 Demonstration | ||||
| Wed | Jan 28 | Lecture 7: Data Races, Functional & Structural Determinism | Module 1: Sections 2.5, 2.6 | Topic 2.5 Lecture , Topic 2.5 Demonstration, Topic 2.6 Lecture , Topic 2.6 Demonstration | Homework 2 | Homework 1 | |||
| Fri | Jan 30 | Lecture 8: Map Reduce | Module 1: Section 2.4 | Topic 2.4 Lecture , Topic 2.4 Demonstration | Lecture & demo quizzes for this week | |||
4 | Mon | Feb 02 | Lecture 9: Memoization | Module 1: Section 2.2 | Topic 2.2 Lecture , Topic 2.2 Demonstration | ||||
| Wed | Feb 04 | TBD | ||||||
| Fri | Feb 06 | Lecture 10: Abstract vs. Real Performance | Lecture & demo quizzes for this week | |||||
5 | Mon | Feb 09 | Lecture 11: Loop-Level Parallelism, Parallel Matrix Multiplication | Topic 3.1 Lecture, Topic 3.1 Demonstration, Topic 3.2 Lecture , Topic 3.2 Demonstration | |||||
| Wed | Feb 11 | Lecture 12: Iteration Grouping (Chunking), Barrier Synchronization | Topic 3.3 Lecture , Topic 3.3 Demonstration , Topic 3.4 Lecture , Topic 3.4 Demonstration | Homework 3 | Homework 2 | |||
| Fri | Feb 13 | Lecture 13: Iterative Averaging Revisited | Topic 3.5 Lecture , Topic 3.5 Demonstration , Topic 3.6 Lecture , Topic 3.6 Demonstration |
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6 | Mon | Feb 16 | Lecture 14: Data-Driven Tasks and Data-Driven Futures | Topic 4.5 Lecture , Topic 4.5 Demonstration | |||||
| Wed | Feb 18 | Lecture 15: Review of Module-1 HJ-lib API's | ||||||
| Fri | Feb 20 | Lecture 16: Point-to-point Synchronization with Phasers | Topic 4.2 Lecture , Topic 4.2 Demonstration | Lecture & demo quizzes for this week | ||||
7 | Mon | Feb 23 | Lecture 17: Phasers (contd), Signal Statement, Fuzzy Barriers | Topic 4.1 Lecture , Topic 4.1 Demonstration | |||||
| Wed | Feb 25 | Lecture 18: Midterm Summary, Take-home Exam 1 distributed | Exam 1 | |||||
| Fri | Feb 27 | No Lecture (Exam 1 due by 4pm today) | Lecture & demo quizzes for this week, Exam 1 | |||||
- | M-F | Feb 28- Mar 08 | Spring Break |
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8 | Mon | Mar 09 | 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 Demonstration |
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| Wed | Mar 11 | Lecture 20: Speculative parallelization of isolated constructs | Homework 4 | Homework 3 | ||||
| Fri | Mar 13 | 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 Demonstration | Lecture & demo quizzes for this week | ||||
9 | Mon | Mar 16 | 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 Demonstration |
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| Wed | Mar 18 | 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 Demonstration |
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| Fri | Mar 20 | Lecture 24: Monitors, Java Concurrent Collections, Linearizability of Concurrent Objects | Topic 7.4 Lecture |
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10 | Mon | Mar 23 | Lecture 25: Linearizability (contd), Intro to Java Threads | Topic 7.1 Lecture |
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| Wed | Mar 25 | Lecture 26: Java Threads (contd), Java synchronized statement | Topic 7.2 Lecture |
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| Fri | Mar 27 | Lecture 27: Java synchronized statement (contd), advanced locking | Topic 7.3 Lecture |
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11 | Mon | Mar 30 | Lecture 28: Safety and Liveness Properties | Topic 7.5 Lecture |
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| Wed | Apr 01 | Lecture 29: Dining Philosophers Problem | Topic 7.6 Lecture | Homework 5 | Lecture & demo quizzes for this week, Homework 4 | |||
- | Fri | Apr 03 | Midterm Recess | ||||||
12 | Mon | Apr 06 | Lecture 30: Message Passing Interface (MPI) |
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| Wed | Apr 08 | Lecture 31: Partitioned Global Address Space (PGAS) languages |
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| Fri | Apr 10 | Lecture 32: Message Passing Interface (MPI, contd) |
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13 | Mon | Apr 13 | Lecture 33: Task Affinity with Places |
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| Wed | Apr 15 | Lecture 34: GPU Computing | Homework 6 | Homework 5 | ||||
| Fri | Apr 17 | Lecture 35: Memory Consistency Models |
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14 | Mon | Apr 20 | Lecture 36: Comparison of Parallel Programming Models |
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| Wed | Apr 22 | NO CLASS (time allocated to work on homeworks) |
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| Fri | Apr 24 | Lecture 37: Course Review (lectures 19-35), Last day of classes | Homework 6 (penalty-free extension till May 1st) | |||||
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| April 29 - May 6 | Scheduled final exam (exact date and time TBD) |
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Lab Schedule
Lab # | Date (2015) | Topic | Handouts | Code Examples |
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1 | Jan 14 | Infrastructure setup, Async-Finish Parallel Programming | lab1-handout | lab_1.zip |
2 | Jan 21 | Abstract performance metrics with async & finish | lab2-handout | lab_2.zip |
3 | Jan 28 | Futures | ||
4 | Feb 04 | Real Performance from Finish Accumulators and Loop-Level Parallelism | ||
5 | Feb 11 | Futures vs. Data-Driven Futures | ||
6 | Feb 18 | Barriers and Phasers | ||
- | Feb 25 | Isolated Statement and Atomic Variables | ||
- | Mar 04 | No lab this week — Spring Break | ||
8 | Mar 11 | Actors | ||
9 | Mar 18 | Java Threads | ||
| 10 | Mar 25 | Java Locks | ||
11 | Apr 01 | Message Passing Interface (MPI) | ||
12 | Apr 08 | Map Reduce | ||
13 | Apr 15 | TBD | ||
| - | Apr 22 | No 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 lab exercises (weighted 10% in all), and class participation including worksheets, in-class Q&A, Piazza participation, etc (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 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 test your individual understanding and knowledge of the material. Exams are closed-book, and collaboration on exams is strictly forbidden. Finally, it is also your responsibility to protect your homeworks 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.