COMP 322: Fundamentals of Parallel Programming (Spring 2017)
Instructor: | Prof. Vivek Sarkar, DH 3131 | Head TA: | Max Grossman |
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Co-Instructor: | Dr. Mackale Joyner | Graduate TAs: | Jonathan Sharman, Ryan Spring, Bing Xue, Lechen Yu |
Admin Assistant: | Annepha Hurlock, annepha@rice.edu, DH 3080, 713-348-5186 | Undergraduate TAs: | Marc Canby, Anna Chi, Peter Elmers, Joseph Hungate, Cary Jiang, Gloria Kim, Kevin Mullin, Victoria Nazari, Ashok Sankaran, Sujay Tadwalkar, Anant Tibrewal, Eugene Wang, Yufeng Zhou |
Piazza site: | https://piazza.com/class/ixdqx0x3bjl6en (Piazza is the preferred medium for all course communications, but you can also send email to comp322-staff at rice dot edu if needed) | Cross-listing: | ELEC 323 |
Lecture location: | Herzstein Hall 210 | Lecture times: | MWF 1:00pm - 1:50pm |
Lab locations: | DH 1064, DH 1070 | 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 and Other Resources
There are no required textbooks for the class. Instead, lecture handouts are provided for each module as follows. 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.The links to the latest versions of the lecture handouts are included below:
- Module 1 handout (Parallelism)
- Module 2 handout (Concurrency)
- There is no lecture handout for Module 3 (Distribution and Locality). The instructors will refer you to optional resources to supplement the lecture slides and videos.
There are also a few optional textbooks that we will draw from during the course. 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:
- Fork-Join Parallelism with a Data-Structures Focus (FJP) by Dan Grossman (Chapter 7 in Topics in Parallel and Distributed Computing)
- 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
Finally, here are some additional resources that may be helpful for you:
- Slides titled "MPI-based Approaches for Java" by Bryan Carpenter
Past Offerings of COMP 322
- Spring 2016 (Rice University)
- Spring 2015 (Rice University)
- Spring 2014 (Rice University)
- Spring 2013 (Rice University)
- Fall 2012 (Harvey Mudd College CS 181E, half-semester class, co-instructor: Prof. Ran Libeskind-Hadas)
- Spring 2012 (Rice University)
- Spring 2011 (Rice University)
- Fall 2009 (Rice University)
Lecture Schedule
Homework 4 (includes one intermediate checkpoint)
Week | Day | Date (2017) | Lecture | Assigned Reading | Assigned Videos (see Canvas site for video links) | In-class Worksheets | Slides | Work Assigned | Work Due |
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1 | Mon | Jan 09 | Lecture 1: Task Creation and Termination (Async, Finish) | Module 1: Section 1.1 | worksheet1 | lec1-slides |
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| Wed | Jan 11 | 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 13 | Lecture 3: Abstract Performance Metrics, Multiprocessor Scheduling | Module 1: Section 1.4 | Topic 1.4 Lecture, Topic 1.4 Demonstration | worksheet3 | lec3-slides |
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2 | Mon | Jan 16 | No lecture, School Holiday (Martin Luther King, Jr. Day) | ||||||
| Wed | Jan 18 | Lecture 4: Parallel Speedup and Amdahl's Law | Module 1: Section 1.5 | Topic 1.5 Lecture, Topic 1.5 Demonstration | worksheet4 | lec4-slides | ||
| Fri | Jan 20 | Lecture 5: Future Tasks, Functional Parallelism ("Back to the Future") | Module 1: Section 2.1 | Topic 2.1 Lecture, Topic 2.1 Demonstration | worksheet5 | lec5-slides | ||
3 | Mon | Jan 23 | Lecture 6: Memoization | Module 1: Section 2.2 | Topic 2.2 Lecture, Topic 2.2 Demonstration | worksheet6 | lec6-slides | ||
Wed | Jan 25 | Lecture 7: Finish Accumulators | Module 1: Section 2.3 | Topic 2.3 Lecture, Topic 2.3 Demonstration | worksheet7 | lec7-slides | Homework 1 | ||
| Fri | Jan 27 | Lecture 8: Map Reduce | Module 1: Section 2.4 | Topic 2.4 Lecture, Topic 2.4 Demonstration | worksheet8 | lec8-slides |
| Quiz for Unit 1 |
4 | Mon | Jan 30 | Lecture 9: 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 | worksheet9 | lec9-slides | ||
| Wed | Feb 01 | Lecture 10: Java’s Fork/Join Library | Module 1: Sections 2.7, 2.8 | Topic 2.7 Lecture, Topic 2.8 Lecture, | worksheet10 | lec10-slides | ||
| Fri | Feb 03 | Lecture 11: Loop-Level Parallelism, Parallel Matrix Multiplication, Iteration Grouping (Chunking) | Module 1: Sections 3.1, 3.2, 3.3 | Topic 3.1 Lecture , Topic 3.1 Demonstration , Topic 3.2 Lecture, Topic 3.2 Demonstration, Topic 3.3 Lecture , Topic 3.3 Demonstration | worksheet11 | lec11-slides | ||
5 | Mon | Feb 06 | Lecture 12: Barrier Synchronization | Module 1: Section 3.4 | Topic 3.4 Lecture , Topic 3.4 Demonstration | worksheet12 | lec12-slides | ||
Wed | Feb 08 | Lecture 13: Parallelism in Java Streams, Parallel Prefix Sums | worksheet13 | lec13-slides | Homework 2 | ||||
- | Fri | Feb 10 | Spring Recess | Quiz for Unit 2 | |||||
6 | Mon | Feb 13 | Lecture 14: Iterative Averaging Revisited, SPMD pattern | Module 1: Sections 3.5, 3.6 | Topic 3.5 Lecture , Topic 3.5 Demonstration , Topic 3.6 Lecture, Topic 3.6 Demonstration | worksheet14 | lec14-slides | ||
| Wed | Feb 15 | Lecture 15: Data-Driven Tasks, Point-to-Point Synchronization with Phasers | Module 1: Sections 4.5, 4.2, 4.3 | Topic 4.5 Lecture Topic 4.5 Demonstration, Topic 4.2 Lecture , Topic 4.2 Demonstration, Topic 4.3 Lecture, Topic 4.3 Demonstration | worksheet15 | lec15-slides | ||
| Fri | Feb 17 | Lecture 16: Phasers Review | Module 1: Sections 4.2 | Topic 4.2 Lecture , Topic 4.2 Demonstration | worksheet16 | lec16-slides | Quiz for Unit 3 | |
7 | Mon | Feb 20 | Lecture 17: Midterm Summary | lec17-slides | |||||
| Wed | Feb 22 | Midterm Review (interactive Q&A) | Exam 1 held during lab time (7:00pm - 10:00pm), scope of exam limited to lectures 1-16 | |||||
| Fri | Feb 24 | Lecture 18: Abstract vs. Real Performance | worksheet18 | lec18-slides | Homework 3, Checkpoint-1 | |||
8 | Mon | Feb 27 | Lecture 19: Pipeline Parallelism, Signal Statement, Fuzzy Barriers | Module 1: Sections 4.4, 4.1 | Topic 4.4 Lecture , Topic 4.4 Demonstration, Topic 4.1 Lecture, Topic 4.1 Demonstration, | worksheet19 | lec19-slides |
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| Wed | Mar 01 | Lecture 20: Critical sections, Isolated construct, Parallel Spanning Tree algorithm, Atomic variables (start of Module 2) | Module 2: Sections 5.1, 5.2, 5.3, 5.4, 5.6 | Topic 5.1 Lecture, Topic 5.1 Demonstration, Topic 5.2 Lecture, Topic 5.2 Demonstration, Topic 5.3 Lecture, Topic 5.3 Demonstration, Topic 5.4 Lecture, Topic 5.4 Demonstration, Topic 5.6 Lecture, Topic 5.6 Demonstration | worksheet20 | lec20-slides |
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| Fri | Mar 03 | Lecture 21: Read-Write Isolation, Review of Phasers | Module 2: Section 5.5 | Topic 5.5 Lecture, Topic 5.5 Demonstration | worksheet21 | lec21-slides | Quiz for Unit 4 | |
9 | Mon | Mar 06 | Lecture 22: Actors | Module 2: 6.1, 6.2 | Topic 6.1 Lecture , Topic 6.1 Demonstration , Topic 6.2 Lecture, Topic 6.2 Demonstration | worksheet22 | lec22-slides |
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| Wed | Mar 08 | Lecture 23: Actors (contd) | Module 2: 6.3, 6.4, 6.5, 6.6 | Topic 6.3 Lecture, Topic 6.3 Demonstration, Topic 6.4 Lecture , Topic 6.4 Demonstration, Topic 6.5 Lecture, Topic 6.5 Demonstration, Topic 6.6 Lecture, Topic 6.6 Demonstration | worksheet23 | lec23-slides |
| Homework 3, Checkpoint-2 |
| Fri | Mar 10 | Lecture 24: Java Threads, Java synchronized statement | Module 2: 7.1, 7.2 | Topic 7.1 Lecture, Topic 7.2 Lecture | worksheet24 | lec24-slides | Quiz for Unit 5 | |
- | M-F | Mar 13 - Mar 17 | Spring Break | ||||||
10 | Mon | Mar 20 | Lecture 25: Java synchronized statement (contd), wait/notify | Module 2: 7.2 | Topic 7.2 Lecture | worksheet25 | lec25-slides |
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Wed | Mar 22 | Lecture 26: Java Locks, Linearizability of Concurrent Objects | Module 2: 7.3, 7.4 | Topic 7.3 Lecture, Topic 7.4 Lecture | worksheet26 | lec26-slides |
(includes one intermediate checkpoint)
| Homework 3 (all) | |
| Fri | Mar 24 | Lecture 27: Safety and Liveness Properties, Java Synchronizers, Dining Philosophers Problem | Module 2: 7.5, 7.6 | Topic 7.5 Lecture, Topic 7.6 Lecture | worksheet27 | lec27-slides | Quiz for Unit 6 | |
11 | Mon | Mar 27 | Lecture 28: Message Passing Interface (MPI), (start of Module 3) | Topic 8.1 Lecture, Topic 8.2 Lecture, Topic 8.3 Lecture, | worksheet28 | ||||
| Wed | Mar 29 | Lecture 29: Message Passing Interface (MPI, contd) | Topic 8.4 Lecture, Topic 8.5 Lecture, Topic 8 Demonstration Video | worksheet29 | lec29-slides |
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| Fri | Mar 31 | Lecture 30: Distributed Map-Reduce using Hadoop and Spark frameworks | Topic 9.1 Lecture (optional, overlaps with video 2.4), Topic 9.2 Lecture, Topic 9.3 Lecture | worksheet30 | lec30-slides | Quiz for Unit 7 | ||
12 | Mon | Apr 03 | Lecture 31: TF-IDF and PageRank Algorithms with Map-Reduce | Topic 9.4 Lecture, Topic 9.5 Lecture, Unit 9 Demonstration | worksheet31 | lec31-slides |
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| Wed | Apr 05 | Lecture 32: Partitioned Global Address Space (PGAS) programming models | worksheet32 | lec32-slides |
| Homework 4 Checkpoint-1 | ||
| Fri | Apr 07 | Lecture 33: Combining Distribution and Multithreading | Lectures 10.1 - 10.5, Unit 10 Demonstration (all videos optional – unit 10 has no quiz) | worksheet33 | lec33-slides |
| Quiz for Unit 8 | |
13 | Mon | Apr 10 | Lecture 34: Task Affinity with Places | worksheet34 | lec34-slides |
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| Wed | Apr 12 | Lecture 35: Eureka-style Speculative Task Parallelism | worksheet35 | lec35-slides |
| Homework 4 (all) | ||
| Fri | Apr 14 | Lecture 36: Algorithms based on Parallel Prefix (Scan) operations | worksheet36 | (Due April 21st, with automatic extension until May 1st after which slip days may be used) | Quiz for Unit 9 | |||
14 | Mon | Apr 17 | Lecture 37: Algorithms based on Parallel Prefix (Scan) operations, contd. | worksheet37 | lec37-slides |
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| Wed | Apr 19 | Lecture 38: GPU Computing | worksheet38 | lec38-slides |
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| Fri | Apr 21 | Lecture 39: Course Review (Lectures 18-38) | Homework 5 (automatic extension until May 1st, after which slip days may be used) | |||||
- | Mon | Apr 24 | Group Office Hours, location & time TBD | ||||||
- | Wed | Apr 26 | Group Office Hours, location & time TBD | ||||||
- | Fri | Apr 28 | Group Office Hours, location & time TBD | ||||||
- | Tue | May 2 | 9am - 12noon, scheduled final exam (Exam 2 – scope of exam limited to lectures 18 - 38), location TBD by registrar |
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Lab Schedule
Lab # | Date (2017) | Topic | Handouts | Code Examples |
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0 | Infrastructure Setup | lab0-handout | - | |
1 | Jan 11 | Async-Finish Parallel Programming with abstract metrics | lab1-handout, lab1-slides | lab_1.zip |
2 | Jan 18 | Futures and HJ-Viz | lab2-handout, lab2-slides | lab_2.zip |
3 | Jan 25 | Cutoff Strategy and Real World Performance | lab3-handout, lab3-slides | lab_3.zip |
4 | Feb 01 | Java's ForkJoin Framework | lab4-handout, lab4-slides | lab_4.zip |
5 | Feb 08 | Loop-level Parallelism | lab5-handout, lab5-slides | lab_5.zip |
6 | Feb 15 | Phasers | lab6-handout | lab_6.zip |
- | Feb 22 | No lab this week — Exam 1 | - | - |
7 | Mar 01 | Isolated Statement and Atomic Variables | lab7-handout, lab7-slides | |
8 | Mar 08 | Actors | lab8-handout | |
- | Mar 15 | No lab this week — Spring Break | ||
9 | Mar 22 | Java Threads, Java Locks | lab9-handout | |
- | Mar 29 | No lab this week — Willy Week! | ||
10 | Apr 05 | Message Passing Interface (MPI) | lab10-handout | |
11 | Apr 12 | Apache Spark | lab11-handout | |
12 | Apr 19 | Eureka-style Speculative Task Parallelism | lab12-handout |
Grading, Honor Code Policy, Processes and Procedures
Grading will be based on your performance on five homeworks (weighted 40% in all), two exams (weighted 40% in all), weekly lab exercises (weighted 10% in all), online quizzes (weighted 5% in all), and class participation including in-class Q&A, worksheets, Piazza participation (weighted 5% in all).
The purpose of the homeworks is to give you practice in solving problems that deepen your understanding of concepts introduced in class. Homeworks are due on the dates and times specified in the course schedule. No late submissions (other than those using slip days mentioned below) will be accepted.
The slip day policy for COMP 322 is similar to that of COMP 321. All students will be given 3 slip days to use throughout the semester. When you use a slip day, you will receive up to 24 additional hours to complete the assignment. You may use these slip days in any way you see fit (3 days on one assignment, 1 day each on 3 assignments, etc.). Slip days will be automatically tracked through the Autograder, more details are available later in this document and in the Autograder user guide. Other than slip days, no extensions will be given unless there are exceptional circumstances (such as severe sickness, not because you have too much other work). Such extensions must be requested and approved by the instructor (via e-mail, phone, or in person) before the due date for the assignment. Last minute requests are likely to be denied.
Labs must be checked off by a TA prior to the start of the lab the following week.
Worksheets should be completed in class for full credit. For partial credit, a worksheet can be turned in before the start of the class following the one in which the worksheet for distributed, so that solutions to the worksheets can be discussed in the next class.
You will be expected to follow the Honor Code in all homeworks and exams. The following policies will apply to different work products in the course:
- In-class worksheets: You are free to discuss all aspects of in-class worksheets with your other classmates, the teaching assistants and the professor during the class. You can work in a group and write down the solution that you obtained as a group. If you work on the worksheet outside of class (e.g., due to an absence), then it must be entirely your individual effort, without discussion with any other students. If you use any material from external sources, you must provide proper attribution.
- Weekly lab assignments: You are free to discuss all aspects of lab assignments with your other classmates, the teaching assistants and the professor during the lab. However, all code and reports that you submit are expected to be the result of your individual effort. If you work on the lab outside of class (e.g., due to an absence), then it must be entirely your individual effort, without discussion with any other students. If you use any material from external sources, you must provide proper attribution (as shown here).
- Homeworks: 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.
- Quizzes: Each online quiz will be an open-notes individual test. The student may consult their course materials and notes when taking the quizzes, but may not consult any other external sources.
- Exams: Each exam will be a closed-book, closed-notes, and closed-computer individual written test, which must be completed within a specified time limit. No class notes or external materials may be consulted when taking the exams.
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.