edX site | Autograder Guide |
COMP 322: Fundamentals of Parallel Programming (Spring
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2025)
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Instructor: | Mackale Joyner, DH |
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Co-Instructor:
Zoran Budimlić, DH 3081
Graduate TAs:
Jonathan Sharman, Srdjan Milakovic
Austin Bae, Avery Whitaker, Aydin Zanager, Eduard Danalache, Frank Chen, Hamza Nauman, Harrison Brown, Jahid Adam, Jeemin Sim, Kitty Cai, Madison Lewis, Ryan Han, Teju Manchenella, Victor Gonzalez, Victoria Nazari
Piazza site:
https://piazza.com/class/j3w0pi8pl9s8s (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:
Sewall Hall 301
Lecture times:
MWF 1:00pm - 1:50pm
Lab locations:
Sewall Hall 301
Lab times:
Thursday, 4:00pm - 4:50pm
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
2063 | TAs: | Raahim Absar, TJ Li | |
|---|---|---|---|
Piazza site: | https://piazza.com/rice/spring2025/comp322 (Piazza is the preferred medium for all course communications) | Cross-listing: | ELEC 323 |
Lecture location: | Herzstein Amp | Lecture times: | MWF 1:00pm - 1:50pm |
Lab location: | Brockman 101 | Lab time: | Mon 3:00pm - 3:50pm |
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 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: 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., MapReduce
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.
Prerequisite
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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. 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
Lecture Schedule
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Week
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Day
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Date (2018)
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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 08
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Lecture 1: Task Creation and Termination (Async, Finish)
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Wed
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Jan 10
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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 15
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No lecture, School Holiday (Martin Luther King, Jr. Day)
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Wed
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Jan 17
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No lecture, Rice closed due to weather
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Quiz for Unit 1
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Fri
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Jan 19
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Lecture 4: Parallel Speedup and Amdahl's Law
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3
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Mon
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Jan 22
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Lecture 5: Future Tasks, Functional Parallelism ("Back to the Future")
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Lecture 7: Finish Accumulators
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Fri
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Jan 26
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Lecture 8: Memoization, Map Reduce
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4
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Mon
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Jan 29
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Lecture 9: Data Races, Functional & Structural Determinism
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Wed
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Jan 31
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Fri
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Feb 02
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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 05
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Lecture 12: Barrier Synchronization
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Wed
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Feb 07
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Lecture 13: Parallelism in Java Streams, Parallel Prefix Sums
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Homework 3 (includes 2 intermediate checkpoints)
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Fri
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Feb 09
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Spring Recess
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6
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Mon
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Feb 12
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Lecture 14: Iterative Averaging Revisited, SPMD pattern
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Wed
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Feb 14
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Lecture 15: Data-Driven Tasks
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Fri
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Feb 16
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Lecture 16: Point-to-point Synchronization with Phasers
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7
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Mon
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Feb 19
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Lecture 17: Midterm Summary
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Wed
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Feb 21
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Midterm Review (interactive Q&A)
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Fri
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Feb 23
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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 26
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Lecture 19: Pipeline Parallelism, Signal Statement, Fuzzy Barriers
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Wed
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Feb 28
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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 02
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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 05
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Lecture 22: Actors
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Wed
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Mar 07
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Lecture 23: Actors (contd)
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Quiz for Unit 6
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Homework 3, Checkpoint-2
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Fri
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Mar 09
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Lecture 24: Java Threads, Java synchronized statement
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M-F
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Mar 12 - Mar 16
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Spring Break
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10
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Mon
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Mar 19
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Lecture 25: Java synchronized statement (contd), wait/notify
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Wed
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Mar 21
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Lecture 26: Java Locks, Linearizability of Concurrent Objects
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(includes one intermediate checkpoint)
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Fri
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Mar 23
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Lecture 27: Safety and Liveness Properties, Java Synchronizers, Dining Philosophers Problem
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Quiz for Unit 6
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11
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Mon
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Mar 26
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Lecture 28: Message Passing Interface (MPI), (start of Module 3)
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Wed
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Mar 28
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Lecture 29: Message Passing Interface (MPI, contd)
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Quiz for Unit 8
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Fri
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Mar 30
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Lecture 30: Distributed Map-Reduce using Hadoop and Spark frameworks
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12
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Mon
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Apr 02
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Lecture 31: TF-IDF and PageRank Algorithms with Map-Reduce
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Wed
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Apr 04
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Lecture 32: Partitioned Global Address Space (PGAS) programming models
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Homework 4 Checkpoint-1
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Fri
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Apr 06
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Lecture 33: Combining Distribution and Multithreading
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Quiz for Unit 8
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 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
Lecture Schedule
Week | Day | Date (2025) | Lecture | Assigned Reading | Assigned Videos (see Canvas site for video links) | In-class Worksheets | Slides | Work Assigned | Work Due | Worksheet Solutions | |
|---|---|---|---|---|---|---|---|---|---|---|---|
1 | Mon | Jan 13 | Lecture 1: Introduction | worksheet1 | lec1-slides | WS1-solution | |||||
Wed | Jan 15 | Lecture 2: Functional Programming | worksheet2 | lec02-slides | WS2-solution | ||||||
| Fri | Jan 17 | Lecture 3: Higher order functions | worksheet3 | lec3-slides | WS3-solution | ||||||
2 | Mon | Jan 20 | No class: MLK | ||||||||
Wed | Jan 22 | Lecture 4: Lazy Computation | worksheet4 | lec4-slides | WS4-solution | ||||||
Fri | Jan 24 | Lecture 5: Java Streams | worksheet5 | lec5-slides | Homework 1 | WS5-solution | |||||
| 3 | Mon | Jan 27 | 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 29 | Lecture 7: Futures | Module 1: Section 2.1 | Topic 2.1 Lecture , Topic 2.1 Demonstration | worksheet7 | lec7-slides | WS7-solution | ||||
Fri | Jan 31 | 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 | Feb 03 | 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 05 | Lecture 10: Event-based programming model | worksheet10 | lec10-slides | Homework 1 | WS10-solution | |||||
| Fri | Feb 07 | 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 10 | 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 12 | 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 14 | No class: Spring Recess | |||||||||
| 6 | Mon | Feb 17 | 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 19 | Lecture 15: Limitations of Functional parallelism. | worksheet15 | lec15-slides | Homework 2 | WS15-solution | |||||
| Fri | Feb 21 | Lecture 16: Recursive Task Parallelism | worksheet16 | lec16-slides | Homework 3 | WS16-solution | |||||
7 | Mon | Feb 24 | Lecture 17: Midterm Review | lec17-slides | |||||||
Wed | Feb 26 | Lecture 18: Midterm Review | lec18-slides | ||||||||
Fri | Feb 28 | 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 | Mar 03 | Lecture 20: 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 | worksheet20 | lec20-slides | WS20-solution | |||
Wed | Mar 05 | Lecture 21: Barrier Synchronization with Phasers | Module 1: Sections 3.4 | Topic 3.4 Lecture, Topic 3.4 Demonstration | worksheet21 | lec21-slides | WS21-solution | ||||
Fri | Mar 07 | Lecture 22: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 | worksheet22 | lec22-slides | WS22-solution | ||||
9 | Mon | Mar 10 | Lecture 23: Fuzzy Barriers with Phasers | Module 1: Section 4.1 | Topic 4.1 Lecture, Topic 4.1 Demonstration | worksheet23 | lec23-slides | Homework 3 (CP 1) | WS23-solution | ||
Wed | Mar 12 | 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 14 | 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 17 | No class: Spring Break | |||||||||
| Wed | Mar 19 | No class: Spring Break | |||||||||
Fri | Mar 21 | No class: Spring Break | |||||||||
10 | Mon | Mar 24 | Lecture 26: Java Threads and Locks | Module 2: Sections 7.1, 7.3 | Topic 7.1 Lecture, Topic 7.3 Lecture | worksheet26 | lec26-slides | WS26-solution | |||
Wed | Mar 26 | Lecture 27: Read-Write Locks, Soundness and progress guarantees | Module 2: Section 7.3 | Topic 7.3 Lecture, Topic 7.5 Lecture | worksheet27 | lec27-slides | Homework 3 (CP 2) | WS27-solution | |||
Fri | Mar 28 | Lecture 28: Dining Philosophers Problem | Topic 7.6 Lecture | worksheet28 | lec28-slides | WS28-solution | |||||
11 | Mon | Mar 31 | Lecture 29: Linearizability of Concurrent Objects | Module 2: Sections 7.4 | Topic 7.4 Lecture | worksheet29 | lec29-slides | WS29-solution | |||
Wed | Apr 02 | Lecture 30: Parallel Spanning Tree, other graph algorithms | worksheet30 | lec30-slides | WS30-solution | ||||||
Fri | Apr 04 | 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 07 | 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 | Homework 4 | Homework 3 (All) | WS32-solution | |
Wed | Apr 09 | Lecture 33: Task Affinity and locality. Memory hierarchy | worksheet33 | lec33-slides | WS33-solution | ||||||
Fri | Apr 11 | Lecture 34: Eureka-style Speculative Task Parallelism | worksheet34 | lec34-slides | WS34-solution | ||||||
13 | Mon | Apr 14 | No class: Solar Eclipse | ||||||||
| Wed | Apr 16 | Lecture 35: Scan Pattern. Parallel Prefix Sum | worksheet35 | lec35-slides | Homework 4 (CP 1) | WS35-solution | |||||
| Fri | Apr 18 | Lecture 36: Parallel Prefix Sum applications | worksheet36 | lec36-slides | WS36-solution | ||||||
| 14 | Mon | Apr 21 | Lecture 37: Overview of other models and frameworks | lec37-slides | |||||||
| Wed | Apr 23 | Lecture 38: Course Review (Lectures 19-34) | lec38-slides | Homework 4 (All) | |||||||
| Fri | Apr 25 | Lecture 39: Course Review (Lectures 19-34) | lec39-slides |
Lab Schedule
Lab # | Date (2025) | Topic | Handouts | Examples |
|---|---|---|---|---|
1 | Jan 13 | Infrastructure setup | ||
| - | Jan 20 | No lab this week (MLK) | ||
| 2 | Jan 27 | Functional Programming | lab2-handout | |
3 | Feb 03 | Futures | lab3-handout | |
| 4 | Feb 10 | Data-Driven Tasks | lab4-handout | |
- | Feb 17 | No lab this week | ||
| - | Feb 24 | No lab this week (Midterm Exam) | ||
| 5 | Mar 03 | Loop Parallelism | lab5-handout | image kernels |
| 6 | Mar 10 | Recursive Task Cutoff Strategy | lab6-handout | |
| - | Mar 17 | No lab this week (Spring Break) | ||
| 7 | Mar 24 | Java Threads | lab7-handout | |
| 8 | Mar 31 | Concurrent Lists | lab8-handout | |
| 9 | Apr 07 | Actors | lab9-handout | |
| - | Apr 14 | No lab this week | ||
- | Apr 21 | No lab this week |
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13
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Mon
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Apr 09
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Lecture 34: Task Affinity with Places
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Wed
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Apr 11
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Lecture 35: Eureka-style Speculative Task Parallelism
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Homework 5
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Homework 4 (all)
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Fri
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Apr 13
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Lecture 36: Algorithms based on Parallel Prefix (Scan) operations
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lec36-slides
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14
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Mon
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Apr 16
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Lecture 37: Algorithms based on Parallel Prefix (Scan) operations, contd.
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Wed
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Apr 18
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Fri
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Apr 20
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Lecture 39: Course Review (Lectures 18-38)
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Homework 5
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Lab Schedule
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
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5
Mar 01
6
Mar 05
Loop-level Parallelism
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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
Message Passing Interface (MPI)
11
Apr 19
Apache Spark
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 Friday at 5pm. 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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