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COMP 322: Fundamentals of Parallel Programming (Spring
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2023)
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Instructor: | Mackale Joyner, DH 2063 | TAs: |
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| 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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spring2023/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: |
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00pm - |
1: |
50pm |
Lab locations: |
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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 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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- Module 1 handout (Parallelism)
- Module 2 handout 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:
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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 |
Wed
Feb 03
| Lecture, Topic 2. |
| 4 Demonstration |
| worksheet6 |
| lec6-slides |
| WS6-solution |
Fri
Feb 05
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 |
Wed
Feb 10
| 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: 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 |
4
Mon
Feb 22
Wed
Feb 24
| 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 |
Spring "Sprinkle" Day (no class)
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 |
Homework 3 (includes one intermediate checkpoint)
Lecture 14: Iterative Averaging Revisited, SPMD pattern
7
Mon
Mar 08
Lecture 15: Point-to-point Synchronization with Phasers
Wed
Mar 10
Lecture 16: Midterm Review
Fri
Lecture 17: Pipeline Parallelism, Signal Statement, Fuzzy Barriers
8
Mon
Mar 15
Lecture 17: Abstract vs. Real Performance
Wed
Mar 17
Lecture 19: Critical Sections, Isolated construct (start of Module 2)
Fri
Mar 19
Lecture 20: Parallel Spanning Tree algorithm, Atomic variables
Quiz for Unit 4
9
Mon
Mar 22
Lecture 21: Actors
Topic 6.1 Lecture , Topic 6.1 Demonstration , Topic 6.2 Lecture, Topic 6.2 Demonstration
Homework 3, Checkpoint-1
Wed
Mar 24
Lecture 22: Actors (contd)
Fri
Mar 26
Mon
Lecture 23: Actors (contd)
Quiz for Unit 5
Quiz for Unit 6
Fri
Apr 02
Lecture 25: Java Threads, Java synchronized statement (contd), wait/notify
11
Mon
Apr 05
Lecture 26: Java Threads (exercise)
Wed
Apr 07
Lecture 27: Java Locks
Fri
Apr 09
Lecture 28: Linearizability of Concurrent Objects
12
Mon
Apr 12
Lecture 30: Safety and Liveness Properties, Java Synchronizers, Dining Philosophers Problem
Quiz for Unit 7
Quiz for Unit 6
Wed
Apr 14
Lecture 31: Message Passing Interface (MPI), (start of Module 3)
Fri
Apr 16
Lecture 32: Message Passing Interface (MPI, contd)
Quiz for Unit 7
13
Mon
Apr 19
Lecture 33: Message Passing Interface (MPI, contd)
Homework 4 Checkpoint-1
Wed
Apr 21
Lecture 34: Task Affinity with Places
lec34-slides
Quiz for Unit 8
Fri
Apr 23
Lecture 35: Eureka-style Speculative Task Parallelism
14
Mon
Apr 26
Lab Schedule
| 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 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 |
|---|---|---|---|---|
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 | 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 (2021)
Topic
Handouts
Examples
1
Jan 26
Async-Finish Parallel Programming with abstract metrics
2
Feb 09
Futures
3
Feb 23
Cutoff Strategy and Real World Performance
Mar 02
DDFs
Mar 09
No lab this week (Midterm exam)
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Isolated Statement and Atomic Variables
Java Threads, Java Locks
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Message Passing Interface (MPI)
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Apache Spark
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Eureka-style Speculative Task Parallelism
Java's ForkJoin Framework
Grading, Honor Code Policy, Processes and Procedures
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Labs must be submitted by the following Monday Wednesday at 114:59pm30pm. Labs must be checked off by a TA.
Worksheets should be completed by the deadline listed in Canvas before the start of the following class (for full credit) so that solutions to the worksheets can be discussed in the next class.
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