edX site | Autograder Guide |
COMP 322: Fundamentals of Parallel Programming (Spring
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2023)
Instructor: |
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Mackale Joyner, DH |
Co-Instructor:
Dr. Mackale Joyner
2063 |
| TAs: |
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Jonathan Sharman, Ryan Spring, Bing Xue, Lechen Yu
| 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-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 - |
Course Syllabus
3:50pm (Raiyan, Oscar, Mohamed, Ryan, Michelle, Taha, Jasmine) Tue 4:00pm - 4:50pm (Tina, Delaney, Chase, Hung, Jerry, Kailin) |
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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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. The links to the latest versions on Canvas are included below:
- Module 1 handout (Parallelism)
- Module 2 handout (Concurrency)
- Module 3 handout (Distribution and Locality)
You 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 are also a few optional textbooks that we will draw from during the course. You are encouraged to get copies of 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:
- 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
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
Week | Day | Date (2023) | Lecture | Assigned Reading | Assigned Videos (see Canvas site for video links) | In-class Worksheets | Slides | Work Assigned | Work Due | Worksheet Solutions | |
|---|---|---|---|---|---|---|---|---|---|---|---|
1 | Mon | Jan 09 | Lecture 1: Introduction | worksheet1 | lec1-slides | WS1-solution | |||||
Wed | Jan 11 | Lecture 2: 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 | ||||||||
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 | 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: Recursive Task Parallelism | worksheet14 | lec14-slides | WS14-solution | |||||
Wed | Feb 15 | Lecture 15: 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 | worksheet15 | lec15-slides | Homework 2 | WS15-solution | |||
| Fri | Feb 17 | Lecture 16: Limitations of Functional 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 | WS18-solution | |||||||
Fri | Feb 24 | Lecture 19: Fork/Join programming model. OS Threads. Scheduler Pattern | Topic 2.7 Lecture, Topic 2.7 Demonstration, Topic 2.8 Lecture, Topic 2.8 Demonstration, | worksheet19 | lec19-slides | WS19-solution | |||||
8 | Mon | Feb 27 | Lecture 20: Confinement & Monitor Pattern. Critical sections | Module 2: Sections 5.1, 5.2, 5.6 | Topic 5.1 Lecture, Topic 5.1 Demonstration, Topic 5.2 Lecture, Topic 5.2 Demonstration, Topic 5.6 Lecture, Topic 5.6 Demonstration | worksheet20 | lec20-slides | WS20-solution | |||
Wed | Mar 01 | Lecture 21: Atomic variables, Synchronized statements | Module 2: Sections 5.4, 7.2 | Topic 5.4 Lecture, Topic 5.4 Demonstration, Topic 7.2 Lecture | worksheet21 | lec21-slides | Homework 3 | WS21-solution | |||
Fri | Mar 03 | Lecture 22: Parallel Spanning Tree, other graph algorithms | worksheet22 | lec22-slides | Homework 4 | WS22-solution | |||||
9 | Mon | Mar 06 | Lecture 23: Java Threads and Locks | Module 2: Sections 7.1, 7.3 | Topic 7.1 Lecture, Topic 7.3 Lecture | worksheet23 | lec23-slides | WS23-solution | |||
Wed | Mar 08 | Lecture 24: Java Locks - Soundness and progress guarantees | Module 2: 7.5 | Topic 7.5 Lecture | worksheet24 | lec24-slides | WS24-solution | ||||
Fri | Mar 10 | Lecture 25: Dining Philosophers Problem | Module 2: 7.6 | Topic 7.6 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: N-Body problem, applications and implementations | worksheet26 | lec26-slides | WS26-solution | |||||
Wed | Mar 22 | Lecture 27: Read-Write Locks, Linearizability of Concurrent Objects | Module 2: 7.3, 7.4 | Topic 7.3 Lecture, Topic 7.4 Lecture | worksheet27 | lec27-slides | WS27-solution | ||||
Fri | Mar 24 | Lecture 28: Message-Passing programming model with Actors | Module 2: 6.1, 6.2 | Topic 6.1 Lecture, Topic 6.1 Demonstration, Topic 6.2 Lecture, Topic 6.2 Demonstration | worksheet28 | lec28-slides | WS28-solution | ||||
11 | Mon | Mar 27 | Lecture 29: Active Object Pattern. Combining Actors with task parallelism | Module 2: 6.3, 6.4 | Topic 6.3 Lecture, Topic 6.3 Demonstration, Topic 6.4 Lecture, Topic 6.4 Demonstration | worksheet29 | lec29-slides | WS29-solution | |||
Wed | Mar 29 | Lecture 30: Task Affinity and locality. Memory hierarchy | worksheet30 | lec30-slides | Homework 4 | WS30-solution | |||||
Fri | Mar 31 | Lecture 31: 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 | worksheet31 | lec31-slides | Homework 5 | WS31-solution | |||
12 | Mon | Apr 03 | Lecture 32: Barrier Synchronization with Phasers | Module 1: Section 3.4 | Topic 3.4 Lecture, Topic 3.4 Demonstration | worksheet32 | lec32-slides | WS32-solution | |||
Wed | Apr 05 | Lecture 33: Stencil computation. Point-to-point Synchronization with Phasers | Module 1: Section 4.2, 4.3 | Topic 4.2 Lecture, Topic 4.2 Demonstration, Topic 4.3 Lecture, Topic 4.3 Demonstration | worksheet33 | lec33-slides | WS33-solution | ||||
Fri | Apr 07 | Lecture 34: Fuzzy Barriers with Phasers | Module 1: Section 4.1 | Topic 4.1 Lecture, Topic 4.1 Demonstration | worksheet34 | lec34-slides | WS34-solution | ||||
13 | Mon | Apr 10 | Lecture 35: Eureka-style Speculative Task Parallelism | worksheet35 | lec35-slides | WS35-solution | |||||
| Wed | Apr 12 | Lecture 36: Scan Pattern. Parallel Prefix Sum | worksheet36 | lec36-slides | WS36-solution | ||||||
| Fri | Apr 14 | Lecture 37: Parallel Prefix Sum applications | worksheet37 | lec37-slides | |||||||
| 14 | Mon | Apr 17 | Lecture 38: Overview of other models and frameworks | lec38-slides | |||||||
| Wed | Apr 19 | Lecture 39: Course Review (Lectures 19-38) | lec39-slides | Homework 5 | |||||||
| Fri | Apr 21 | Lecture 40: Course Review (Lectures 19-38) | lec40-slides |
Lab Schedule
Lab # | Date (2023) | Topic | Handouts | Examples |
|---|---|---|---|---|
1 | Jan 09 | Infrastructure setup | ||
| - | 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 | Recursive Task Cutoff Strategy | lab6-handout | |
| 7 | Mar 06 | Java Threads | lab7-handout | |
| - | Mar 13 | No lab this week (Spring Break) | ||
| 8 | Mar 20 | Concurrent Lists | lab8-handout | |
| 9 | Mar 27 | Actors | lab9-handout | |
| - | Apr 03 | TBD | ||
| 10 | Apr 10 | Loop Parallelism | lab10-handout | |
- | Apr 17 | No lab this week |
Lecture Schedule
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Week
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Day
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Date (2017)
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Lecture
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Assigned Videos (see Canvas site for video links)
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In-class Worksheets
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Work Assigned
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Work Due
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1
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Mon
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Jan 09
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Lecture 1: Task Creation and Termination (Async, Finish)
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Wed
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Jan 11
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Lecture 2: Computation Graphs, Ideal Parallelism
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2
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Mon
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Jan 16
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No lecture, School Holiday (Martin Luther King, Jr. Day)
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Wed
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Jan 18
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Lecture 4: Parallel Speedup and Amdahl's Law
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Fri
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Jan 20
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Lecture 5: Future Tasks, Functional Parallelism ("Back to the Future")
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3
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Mon
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Jan 23
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Lecture 6: Memoization
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Lecture 7: Finish Accumulators
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Fri
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Jan 27
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Lecture 8: Map Reduce
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4
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Mon
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Jan 30
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Lecture 9: Data Races, Functional & Structural Determinism
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Wed
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Feb 01
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Fri
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Feb 03
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Lecture 11: Loop-Level Parallelism, Parallel Matrix Multiplication, Iteration Grouping (Chunking)
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Topic 3.1 Lecture , Topic 3.1 Demonstration , Topic 3.2 Lecture, Topic 3.2 Demonstration, Topic 3.3 Lecture , Topic 3.3 Demonstration
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5
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Mon
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Feb 06
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Lecture 12: Barrier Synchronization
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Wed
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Feb 08
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Lecture 13: Parallelism in Java Streams, Parallel Prefix Sums
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Homework 3 (includes two intermediate checkpoints)
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-
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Fri
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Feb 10
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Spring Recess
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6
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Mon
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Feb 13
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Lecture 14: Iterative Averaging Revisited, SPMD pattern
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Wed
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Feb 15
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Lecture 15: Phasers, Point-to-point Synchronization
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Fri
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Feb 17
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Lecture 16: Phasers Review
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7
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Mon
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Feb 20
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Lecture 17: Midterm Summary
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Wed
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Feb 22
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Midterm Review (interactive Q&A, no lecture)
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Fri
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Feb 24
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Lecture 18: Abstract vs. Real Performance
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8
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Mon
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Feb 27
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Lecture 19: Pipeline Parallelism, Signal Statement, Fuzzy Barriers
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Wed
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Mar 01
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Lecture 20: Critical sections, Isolated construct, Parallel Spanning Tree algorithm, Atomic variables (start of Module 2)
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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
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Fri
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Mar 03
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Lecture 21: Read-Write Isolation, Review of Phasers
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Quiz for Unit 4
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9
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Mon
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Mar 06
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Lecture 22: Actors
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Wed
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Mar 08
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Lecture 23: Actors (contd)
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Homework 3, Checkpoint-2
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Fri
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Mar 10
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Lecture 24: Java Threads, Java synchronized statement
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M-F
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Mar 13 - Mar 17
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Spring Break
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10
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Mon
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Mar 20
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Lecture 25: Java synchronized statement (contd), wait/notify
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Wed
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Mar 22
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Lecture 25: Concurrent Objects, Linearizability of Concurrent Objects
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Homework 4
(includes one intermediate checkpoint)
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Fri
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Mar 24
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Lecture 27: Parallel Design Patterns, Safety and Liveness Properties, Java Synchronizers, Dining Philosophers Problem
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11
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Mon
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Mar 27
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Lecture 28: Message Passing Interface (MPI), (start of Module 3)
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lec28-slides
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Wed
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Mar 29
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Lecture 29: Message Passing Interface (MPI, contd)
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Fri
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Mar 31
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Lecture 30: Apache Hadoop and Spark frameworks for Map-Reduce
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12
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Mon
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Apr 03
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Lecture 31: TF-IDF and PageRank Algorithms with Map-Reduce
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Wed
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Apr 05
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Lecture 32:
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Homework 4 Checkpoint-1
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Fri
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Apr 07
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Lecture 33: Eureka-style Speculative Task Parallelism
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13
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Mon
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Apr 10
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Lecture 34: Task Affinity with Places
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Wed
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Apr 12
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Lecture 35: GPU Computing
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Homework 5
(Due April 21st, with automatic extension until May 1st after which slip days may be used)
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Homework 4 (all)
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Fri
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Apr 14
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Lecture 36: Partitioned Global Address Space (PGAS) programming models
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lec36-slides
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14
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Mon
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Apr 17
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Lecture 37:
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Wed
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Apr 19
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Fri
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Apr 21
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Lecture 39: Course Review (lectures 19 - 38), Last day of classes
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Homework 5 (automatic extension until May 1st, after which slip days may be used)
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Tue
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May 2
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9am - 12noon, scheduled final exam (Exam 2 – scope of exam limited to lectures 19 - 38), location TBD by registrar
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Lab Schedule
Lab #
Date (2017)
Topic
Handouts
Code Examples
1
Jan 11
Async-Finish Parallel Programming with abstract metrics
2
Jan 18
Futures and HJ-Viz
3
Jan 25
Cutoff Strategy and Real World Performance
4
Feb 01
Java's ForkJoin Framework
5
Feb 08
Loop-level Parallelism
6
Feb 15
Phasers
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Feb 22
No lab this week — Exam 1
7
Mar 01
Isolated Statement and Atomic Variables
8
Mar 08
Actors
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Mar 15
No lab this week — Spring Break
Mar 22
Java Threads, Java Locks
10
Mar 29
Eureka-style Speculative Task Parallelism
11
Apr 05
Message Passing Interface (MPI)
12
Apr 12
Apache Spark
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 class participation including in-class Q&A, worksheets, Piazza participation 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 checked off by a TA prior to the start of the lab the following week.submitted by the following Wednesday at 4:30pm. Labs must be checked off by a TA.
Worksheets should be completed by the deadline listed in Canvas 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 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.
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 weekFor grade disputes, please send an email to the course instructors within 7 days of receiving your grade. The email subject should include COMP 322 and the assignment. Please provide enough information in the email so that the instructor does not need to perform a checkout of your code.
Accommodations for Students with Special Needs
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