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COMP 322: Fundamentals of Parallel Programming (Spring
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2023)
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Instructor: | Mackale Joyner, DH |
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Co-Instructor:
Zoran Budimlić, DH 3081
2063 |
TAs: |
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Jonathan Sharman, Srdjan Milakovic
Mohamed Abead, Chase Hartsell, Taha Hasan, Harrison Huang, Jerry Jiang, Jasmine Lee, Michelle Lee, Hung Nguyen, Quang Nguyen, Ryan Ramos, Oscar Reynozo, Delaney Schultz, Tina Wen, Raiyan Zannat, Kailin Zhang | |
Piazza site: |
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comp322 (Piazza is the preferred medium for all course communications |
) | Cross |
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-listing: | ELEC 323 |
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Lecture location: |
Herzstein Amphitheater | Lecture times: | MWF 1:00pm - 1:50pm |
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Lab locations: |
Mon (Herzstein Amp), Tue (Keck 100) | Lab times: |
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Mon 3:00pm - 3:50pm (Raiyan, Oscar, Mohamed, Ryan, Michelle, Taha) Tue 4:00pm - 4:50pm |
(Tina, Delaney, Chase, Hung, Jerry, Kailin, Jasmine) |
Course 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.
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The desired learning outcomes fall into three major areas (course modules):
1) Parallelism: functional programming, Java streams, 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.
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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 homework 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.
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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 handout (Concurrency)
There
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There are also 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:
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Lecture Schedule
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Week | Day | Date ( |
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2023) | Lecture | Assigned Reading | Assigned Videos (see Canvas site for video links) | In-class Worksheets | Slides | Work Assigned | Work Due | Worksheet Solutions | |
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1 | Mon | Jan |
09 | Lecture 1: |
Introduction | worksheet1 | lec1- |
slides | WS1-solution |
Wed | Jan |
11 | Lecture 2: |
2
Mon
Jan 15
No lecture, School Holiday (Martin Luther King, Jr. Day)
Wed
Jan 17
No lecture, Rice closed due to weather
Quiz for Unit 1
Fri
Jan 19
Lecture 4: Parallel Speedup and Amdahl's Law
3
Mon
Jan 22
Lecture 5: Future Tasks, Functional Parallelism ("Back to the Future")
Lecture 7: Finish Accumulators
Fri
Jan 26
Lecture 8: Memoization, Map Reduce
4
Mon
Jan 29
Lecture 9: Data Races, Functional & Structural Determinism
Wed
Jan 31
Fri
Feb 02
Lecture 11: Loop-Level Parallelism, Parallel Matrix Multiplication, Iteration Grouping (Chunking)
Topic 3.1 Lecture , Topic 3.1 Demonstration , Topic 3.2 Lecture, Topic 3.2 Demonstration, Topic 3.3 Lecture , Topic 3.3 Demonstration
5
Mon
Feb 05
Lecture 12: Barrier Synchronization
Wed
Feb 07
Lecture 13: Parallelism in Java Streams, Parallel Prefix Sums
-
Fri
Feb 09
Spring Recess
6
Mon
Feb 12
Lecture 14: Iterative Averaging Revisited, SPMD pattern
Wed
Feb 14
Lecture 15: Data-Driven Tasks
Fri
Feb 16
Lecture 16: Point-to-point Synchronization with Phasers
7
Mon
Feb 19
Lecture 17: Midterm Summary
Wed
Feb 21
Midterm Review (interactive Q&A)
Fri
Feb 23
Lecture 18: Abstract vs. Real Performance
8
Mon
Feb 26
Lecture 19: Pipeline Parallelism, Signal Statement, Fuzzy Barriers
Wed
Feb 28
Lecture 20: Critical sections, Isolated construct, Parallel Spanning Tree algorithm, Atomic variables (start of Module 2)
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
Fri
Mar 02
Lecture 21: Read-Write Isolation, Review of Phasers
Quiz for Unit 4
9
Mon
Mar 05
Lecture 22: Actors
Wed
Mar 07
Lecture 23: Actors (contd)
Quiz for Unit 6
Homework 3, Checkpoint-2
Fri
Mar 09
Lecture 24: Java Threads, Java synchronized statement
M-F
Mar 12 - Mar 16
Spring Break
10
Mon
Mar 19
Lecture 25: Java synchronized statement (contd), wait/notify
Wed
Mar 21
Lecture 26: Java Locks, Linearizability of Concurrent Objects
(includes one intermediate checkpoint)
Fri
Mar 23
Lecture 27: Safety and Liveness Properties, Java Synchronizers, Dining Philosophers Problem
Quiz for Unit 6
11
Mon
Mar 26
Lecture 28: Message Passing Interface (MPI), (start of Module 3)
Wed
Mar 28
Lecture 29: Message Passing Interface (MPI, contd)
Quiz for Unit 8
Fri
Mar 30
Lecture 30: Distributed Map-Reduce using Hadoop and Spark frameworks
12
Mon
Apr 02
Lecture 31: TF-IDF and PageRank Algorithms with Map-Reduce
Wed
Apr 04
Lecture 32: Partitioned Global Address Space (PGAS) programming models
Homework 4 Checkpoint-1
Fri
Apr 06
Lecture 33: Combining Distribution and Multithreading
Quiz for Unit 8
13
Mon
Apr 09
Lecture 34: Task Affinity with Places
Wed
Apr 11
Lecture 35: Eureka-style Speculative Task Parallelism
Homework 4 (all)
Fri
Apr 13
Lecture 36: Algorithms based on Parallel Prefix (Scan) operations
lec36-slides
14
Mon
Apr 16
Lecture 37: Algorithms based on Parallel Prefix (Scan) operations, contd.
Wed
Apr 18
Fri
Apr 20
Lecture 39: Course Review (Lectures 18-38)
Homework 5
Lab Schedule
Functional Programming | worksheet2 | lec02-slides | WS2-solution | ||||||||
Fri | Jan 13 | Lecture 3: Higher order functions | worksheet3 | lec3-slides | WS3-solution | ||||||
2 | Mon | Jan 16 | No class: MLK | ||||||||
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Wed | Jan 18 | Lecture 4: Lazy Computation | worksheet4 | lec4-slides | WS4-solution | ||||||
Fri | Jan 20 | Lecture 5: Java Streams | worksheet5 | lec5-slides | Homework 1 | WS5-solution | |||||
3 | Mon | Jan 23 | Lecture 6: Map Reduce with Java Streams | Module 1: Section 2.4 | Topic 2.4 Lecture, Topic 2.4 Demonstration | worksheet6 | lec6-slides | WS6-solution | |||
Wed | Jan 25 | Lecture 7: Futures | Module 1: Section 2.1 | Topic 2.1 Lecture , Topic 2.1 Demonstration | worksheet7 | lec7-slides | WS7-solution | ||||
Fri | Jan 27 | Lecture 8: Async, Finish, Computation Graphs | Module 1: Sections 1.1, 1.2 | Topic 1.1 Lecture, Topic 1.1 Demonstration, Topic 1.2 Lecture, Topic 1.2 Demonstration | worksheet8 | lec8-slides | WS8-solution | ||||
4 | Mon | Jan 30 | Lecture 9: Ideal Parallelism, Data-Driven Tasks | Module 1: Section 1.3, 4.5 | Topic 1.3 Lecture, Topic 1.3 Demonstration, Topic 4.5 Lecture, Topic 4.5 Demonstration | worksheet9 | lec9-slides | WS9-solution | |||
Wed | Feb 01 | Lecture 10: Event-based programming model | worksheet10 | lec10-slides | Homework 1 | WS10-solution | |||||
Fri | Feb 03 | Lecture 11: GUI programming, Scheduling/executing computation graphs | Module 1: Section 1.4 | Topic 1.4 Lecture , Topic 1.4 Demonstration | worksheet11 | lec11-slides | Homework 2 | WS11-solution | |||
5 | Mon | Feb 06 | Lecture 12: Abstract performance metrics, Parallel Speedup, Amdahl's Law | Module 1: Section 1.5 | Topic 1.5 Lecture , Topic 1.5 Demonstration | worksheet12 | lec12-slides | WS12-solution | |||
Wed | Feb 08 | Lecture 13: Accumulation and reduction. Finish accumulators | Module 1: Section 2.3 | Topic 2.3 Lecture Topic 2.3 Demonstration | worksheet13 | lec13-slides | WS13-solution | ||||
Fri | Feb 10 | No class: Spring Recess | |||||||||
6 | Mon | Feb 13 | Lecture 14: 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 | worksheet14 | lec14-slides | WS14-solution | |||
Wed | Feb 15 | Lecture 15: Limitations of Functional parallelism. | worksheet15 | lec15-slides | Homework 2 | WS15-solution | |||||
Fri | Feb 17 | Lecture 16: Recursive Task Parallelism | worksheet16 | lec16-slides | Homework 3 | WS16-solution | |||||
7 | Mon | Feb 20 | Lecture 17: Midterm Review | lec17-slides | |||||||
Wed | Feb 22 | Lecture 18: Midterm Review | lec18-slides | ||||||||
Fri | Feb 24 | Lecture 19: Data-Parallel Programming model. Loop-Level Parallelism, Loop 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 | worksheet19 | lec19-slides | WS19-solution | ||||
8 | Mon | Feb 27 | Lecture 20: Barrier Synchronization with Phasers | Module 1: Sections 3.4 | Topic 3.4 Lecture, Topic 3.4 Demonstration | worksheet20 | lec20-slides | WS20-solution | |||
Wed | Mar 01 | Lecture 21:Stencil computation. Point-to-point Synchronization with Phasers | Module 1: Sections 4.2, 4.3 | Topic 4.2 Lecture, Topic 4.2 Demonstration, Topic 4.3 Lecture, Topic 4.3 Demonstration | worksheet21 | lec21-slides | WS21-solution | ||||
Fri | Mar 03 | Lecture 22: Fuzzy Barriers with Phasers | Module 1: Section 4.1 | Topic 4.1 Lecture, Topic 4.1 Demonstration | worksheet22 | lec22-slides | WS22-solution | ||||
9 | Mon | Mar 06 | Lecture 23: Fork/Join programming model. OS Threads. Scheduler Pattern | Topic 2.7 Lecture, Topic 2.7 Demonstration, Topic 2.8 Lecture, Topic 2.8 Demonstration | worksheet23 | lec23-slides | Homework 3 (CP 1) | WS23-solution | |||
Wed | Mar 08 | Lecture 24: Confinement & Monitor Pattern. Critical sections | Module 2: Sections 5.1, 5.2 | Topic 5.1 Lecture, Topic 5.1 Demonstration, Topic 5.2 Lecture, Topic 5.2 Demonstration, Topic 5.6 Lecture, Topic 5.6 Demonstration | worksheet24 | lec24-slides | WS24-solution | ||||
Fri | Mar 10 | Lecture 25: Atomic variables, Synchronized statements | Module 2: Sections 5.4, 7.2 | Topic 5.4 Lecture, Topic 5.4 Demonstration, Topic 7.2 Lecture | worksheet25 | lec25-slides | WS25-solution | ||||
Mon | Mar 13 | No class: Spring Break | |||||||||
Wed | Mar 15 | No class: Spring Break | |||||||||
Fri | Mar 17 | No class: Spring Break | |||||||||
10 | Mon | Mar 20 | Lecture 26: Parallel Spanning Tree, other graph algorithms | worksheet26 | lec26-slides | WS26-solution | |||||
Wed | Mar 22 | Lecture 27: Java Threads and Locks | Module 2: Sections 7.1, 7.3 | Topic 7.1 Lecture, Topic 7.3 Lecture | worksheet27 | lec27-slides | Homework 3 (CP 2) | WS27-solution | |||
Fri | Mar 24 | Lecture 28: Java Locks - Soundness and progress guarantees | Module 2: Section 7.5 | Topic 7.5 Lecture | worksheet28 | lec28-slides | WS28-solution | ||||
11 | Mon | Mar 27 | Lecture 29: Dining Philosophers Problem | Module 2: Section 7.6 | Topic 7.6 Lecture | worksheet29 | lec29-slides | WS29-solution | |||
Wed | Mar 29 | Lecture 30: Read-Write Locks, Linearizability of Concurrent Objects | Module 2: Sections 7.3, 7.4 | Topic 7.3 Lecture, Topic 7.4 Lecture | worksheet30 | lec30-slides | WS30-solution | ||||
Fri | Mar 31 | Lecture 31: Message-Passing programming model with Actors | Module 2: Sections 6.1, 6.2 | Topic 6.1 Lecture, Topic 6.1 Demonstration, Topic 6.2 Lecture, Topic 6.2 Demonstration | worksheet31 | lec31-slides | WS31-solution | ||||
12 | Mon | Apr 03 | No class | Homework 4 | Homework 3 (All) | ||||||
Wed | Apr 05 | Lecture 32: Active Object Pattern. Combining Actors with task parallelism | Module 2: Sections 6.3, 6.4 | Topic 6.3 Lecture, Topic 6.3 Demonstration, Topic 6.4 Lecture, Topic 6.4 Demonstration | worksheet32 | lec32-slides | WS32-solution | ||||
Fri | Apr 07 | Lecture 33: Task Affinity and locality. Memory hierarchy | worksheet33 | lec33-slides | WS33-solution | ||||||
13 | Mon | Apr 10 | Lecture 34: Eureka-style Speculative Task Parallelism | worksheet34 | lec34-slides | WS34-solution | |||||
Wed | Apr 12 | Lecture 35: Scan Pattern. Parallel Prefix Sum | worksheet35 | lec35-slides | Homework 4 (CP 1) | WS35-solution | |||||
Fri | Apr 14 | Lecture 36: Parallel Prefix Sum applications | worksheet36 | lec36-slides | WS36-solution | ||||||
14 | Mon | Apr 17 | Lecture 37: Overview of other models and frameworks | lec37-slides | |||||||
Wed | Apr 19 | Lecture 38: Course Review (Lectures 19-34) | lec38-slides | Homework 4 (All) | |||||||
Fri | Apr 21 | Lecture 39: Course Review (Lectures 19-34) | lec39-slides |
Lab Schedule
Lab # | Date (2023) | Topic | Handouts | Examples |
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1 | Jan 09 | Infrastructure setup | lab0-handout lab1-handout | |
- | Jan 16 | No lab this week (MLK) | ||
2 | Jan 23 | Functional Programming | lab2-handout | |
3 | Jan 30 | Futures | lab3-handout | |
4 | Feb 06 | Data-Driven Tasks | lab4-handout | |
5 | Feb 13 | Async / Finish | lab5-handout | |
- | Feb 20 | No lab this week (Midterm Exam) | ||
6 | Feb 27 | Loop Parallelism | lab6-handout | image kernels |
7 | Mar 06 | Recursive Task Cutoff Strategy | lab7-handout | |
- | Mar 13 | No lab this week (Spring Break) | ||
- | Mar 20 | No lab this week | ||
8 | Mar 27 | Java Threads | lab8-handout | |
9 | Apr 03 | Concurrent Lists | lab9-handout | |
10 | Apr 10 | Actors | lab10-handout | |
- | Apr 17 | No lab this week |
Lab #
Date (2018)
Topic
Handouts
Code Examples
1
Jan 11
Async-Finish Parallel Programming with abstract metrics
2
Jan 25
Futures
3
Feb 01
Cutoff Strategy and Real World Performance
4
Feb 15
Java's ForkJoin Framework
-
5
Mar 01
6
Mar 05
Loop-level Parallelism
-
Mar 15
No lab this week - Spring Break
7
Mar 22
Isolated Statement and Atomic Variables
8
Mar 29
Actors
Apr 05
Java Threads, Java Locks
10
Apr 12
Apache Spark
11
Apr 19
Message Passing Interface (MPI)
Eureka-style Speculative Task Parallelism
Grading, Honor Code Policy, Processes and Procedures
Grading will be based on your performance on five homeworks four homework assignments (weighted 40% in all), two exams (weighted 40% in all), weekly lab exercises (weighted 10% in all), online quizzes (weighted 5% in all), and in-class worksheets (weighted 5% in all).
The purpose of the homeworks homework is to give you practice in solving problems that deepen your understanding of concepts introduced in class. Homeworks are Homework is 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 guideusing the README.md file. 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 submitted by the following Wednesday at 4:30pm. Labs Labs must be checked off by a TA by the following Monday at 11:59pm.
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, by the deadline listed in Canvas 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 homework 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).
- HomeworksHomework: All submitted homeworks are homework is 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 closedopen-book, closedopen-notes, and closedopen-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.
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