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Instructors: | Mackale Joyner, DH 2063 Zoran Budimlić, DH 10383003 | TAs: | Adrienne Li, Austin Hushower, Claire Xu, Diep Hoang, Hunena Badat, Maki Yu, Mantej Singh, Rose Zhang, Victor Song, Yidi Wang |
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| Admin Assistant: | Annepha Hurlock, annepha@rice.edu , DH 3122, 713-348-5186 |
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Piazza site: | https://piazza.com/rice/spring2022/comp322 (Piazza is the preferred medium for all course communications) | Cross-listing: | ELEC 323 |
Lecture location: | Herzstein Amphitheater (online 1st 2 weeks) | Lecture times: | MWF 1:00pm - 1:50pm |
Lab locations: | Keck 100 (online 1st 2 weeks) | Lab times: | Mon 3:00pm - 3:50pm (Austin, Claire) Wed 4:30pm - 5:20pm (Hunena, Mantej, Yidi, Victor, Rose, Adrienne, Diep, Maki) |
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 (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
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Finally, here are some additional resources that may be helpful for you:
- Slides titled "MPI-based Approaches for Java" by Bryan Carpenter
Lecture Schedule
Week | Day | Date (2022) | 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 10 | Lecture 1: Task Creation and Termination (Async, Finish) | Module 1: Section 1.1 | Introduction | Topic 1.1 Lecture, Topic 1.1 Demonstration | worksheet1 | lec1-slidesslides |
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| WS1-solution | |||||||||||||||
| Wed | Jan 12 | Lecture 2: Computation Graphs, Ideal Parallelism | Module 1: Sections 1.2, 1.3 | Topic 1.2 Lecture, Topic 1.2 Demonstration, Topic 1.3 Lecture, Topic 1.3 Demonstration | worksheet2 | lec2-slides | Homework 1 |
| Functional Programming | GList.java | worksheet2 | lec02-slides |
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| WS2-solution | ||||||||||
| Fri | Jan 14 | Lecture 3: Abstract Performance Metrics, Multiprocessor Scheduling | Module 1: Section 1.4 | Topic 1.4 Lecture, Topic 1.4 Demonstration | worksheet3 | lec3-slides | Higher order functions | worksheet3 | lec3-slides |
| WS3-solution | |||||||||||||||
2 | Mon | Jan 17 | Lecture 4: Parallel Speedup and Amdahl's Law | Module 1: Section 1.5 | Topic 1.5 Lecture, Topic 1.5 Demonstration | worksheet4 | lec4-slides | No class: MLK | Quiz for Unit 1 | |||||||||||||||||
| Wed | Jan 19 | Lecture 5: Future Tasks, Functional Parallelism ("Back to the Future") | Module 1: Section 2.1 | Topic 2.1 Lecture, Topic 2.1 Demonstration | worksheet5 | 4: Lazy Computation | LazyList.java Lazy.java | worksheet4 | lec4lec5-slides | WS4-solution | |||||||||||||||
| Fri | Jan 21 | Lecture 65: Java Streams | Finish Accumulators | Module 1: Section 2.3 | Topic 2.3 Lecture, Topic 2.3 Demonstration | worksheet6 | lec6-slides | worksheet5 | lec5-slides | Homework Quiz for Unit 1 | WS5-solution | ||||||||||||||
| 3 | Mon | Jan 24 | Lecture 76: Map Reduce with Java Streams | Module 1: Section 2.4 | Topic 2.4 Lecture, Topic 2.4 Demonstration | worksheet7worksheet6 | lec7lec6-slides |
| WS6-solution | |||||||||||||||||
| Wed | Jan 26 | Lecture 8: Data Races, Functional & Structural Determinism7: Futures | Module 1: Section 2.5, 2.61 | Topic 2.5 1 Lecture , Topic 2.5 Demonstration, Topic 2.6 Lecture, Topic 2.6 Demonstration | worksheet8 | 1 Demonstration | worksheet7 | lec7lec8-slides | Homework 2 | Homework 1 | WS7-solution | ||||||||||||||
| Fri | Jan 28 | Lecture 9: Java’s Fork/Join Library | Topic 2.7 Lecture, Topic 2.7 Demonstration, Topic 2.8 Lecture, Topic 2.8 Demonstration | worksheet9 | lec9-slides | Quiz for Unit 2 | 8: Computation Graphs, Ideal Parallelism | Module 1: Sections 1.2, 1.3 | Topic 1.2 Lecture, Topic 1.2 Demonstration, Topic 1.3 Lecture, Topic 1.3 Demonstration | worksheet8 | lec8-slides | WS8-solution | |||||||||||||
4 | Mon
| Jan 31 | Lecture 9: Async, Finish, Data-Driven Tasks | Module 1: Section 1.1, | Wed | Feb 02 | Lecture 12: Data-Driven Tasks
| Module 1: Section 4.5
| Topic 1.1 Lecture, Topic 1.1 Demonstration, Topic 4.5 Lecture | , Topic 4.5 Demonstration | worksheet9 | lec9-slides | WS9-solution | |||||||||||||
| FriWed | Feb 0402 | Lecture 10: Event-based programming model
| worksheet10 | lec10-slides | WS10-solution | |||||||||||||||||||||
| Fri | Feb 04 | Lecture 11: GUI programming as an example of event-based, futures/callbacks in GUI programming | worksheet11 | lec11-slides | Homework 2 | Homework 1 | WS11-solution | |||||||||||||||||||
| 5 | Mon | Feb 07 | Lecture 12: Scheduling/executing computation graphs Abstract performance metrics | Module 1: Section 1.4 | Topic 1.4 Lecture , Topic 1.4 Demonstration | worksheet12 | lec12-slides | WS12-solution | ||||||||||||||||||
| Wed | Feb 09 | Lecture 13: Lightweight task parallelism. Finish/async Parallel Speedup, Critical Path, Amdahl's Law | Module 1: Section 1.15 | Topic 1.1 5 Lecture , Topic 1.1 5 Demonstration | worksheet13 | lec13-slides | WS13-solution | ||||||||||||||||||
| Fri | Feb 11 | No Classclass: Spring Recess
| Quiz for Unit 2 | ||||||||||||||||||||||
| 6 | Mon | Feb 14 | Lecture 14: Parallel Speedup, Critical Path, Amdah's Law Accumulation and reduction. Finish accumulators | Module 1: Section 12.53 | Topic 12.5 3 Lecture Topic 12.5 3 Demonstration | worksheet14 | lec14-slides | WS14-solution | ||||||||||||||||||
| Wed | Feb 16 | Lecture 15: Recursive Task Parallelism | worksheet15 | lec15-slides | Homework 3 (includes one intermediate checkpoint)
| Homework 2 | WS15-solution | ||||||||||||||||||
| Fri | Feb 18 | Lecture 16: Accumulation and reduction. Finish accumulatorsData Races, Functional & Structural Determinism | Module 1: Section Sections 2.5, 2.36 | Topic 2.3 5 Lecture , Topic 2.5 Demonstration, Topic 2.6 Lecture, Topic 2.3 6 Demonstration | worksheet16 | lec16-slides | Quiz for Unit Homework 3 | Homework 2 | WS16-solution | |||||||||||||||||
7 | Mon | Feb 21 | Lecture 17: Midterm Review | lec17-slides | ||||||||||||||||||||||
| Wed | Feb 23 | Lecture 18: Limitations of Functional parallelism. | worksheet18 | lec18-slides | WS18-solution | ||||||||||||||||||||
| Fri | Feb 25 | Lecture 19: Data Races, Functional & Structural Determinism Fork/Join programming model. OS Threads. Scheduler Pattern | Module 1: Sections 2.5, 2.6 | Topic 2.5 7 Lecture, Topic 2.5 7 Demonstration, Topic 2.6 8 Lecture, Topic 2.6 8 Demonstration, | worksheet19 | lec19-slides | WS19-solution | ||||||||||||||||||
8 | Mon | Feb 28 | 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 | lec20lec20-slides | Quiz for Unit 4 | Quiz for Unit 3WS20-solution | |||||||||||||||||
| Wed | Mar 02 | Lecture 21: N-Body problem, applications and implementations | 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 | WS21-solution | |||||||||||||||||
| Fri | Mar 04 | Lecture 22: Fork/Join programming model. OS Threads. Scheduler Pattern | Module 2: Sections 7.1, 7.2 | Parallel Spanning Tree, other graph algorithms | Topic 7.1 Lecture, Topic 7.2 Lecture | worksheet22 | lec22-slides | Quiz for Unit 4 | Homework 4 | Homework 3 | WS22-solution | ||||||||||||||
9 | Mon | Mar 07 | Lecture 23: Locks, Atomic variables Java Threads and Locks | Module 2: Sections 7.1, 7.3 | Topic 7.1 Lecture, Topic 7.3 Lecture | worksheet23 | lec23-slides | Quiz for Unit 5
| WS23-solution | |||||||||||||||||
| Wed | Mar 09 | Lecture 24: Parallel Spanning Tree, other graph algorithms | Java Locks - Soundness and progress guarantees | Module 2: 7.5 | Topic 7.5 Lecture | worksheet24 | lec24-slides | Homework 3, Checkpoint-1 | WS24-solution | ||||||||||||||||
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| Fri | Mar 11 | Lecture 25: Linearizability of Concurrent Objects Dining Philosophers Problem | Module 2: 7.46 | Topic 7.4 6 Lecture | worksheet25 | lec25-slides | Quiz for Unit 6 | Quiz for Unit 5
| WS25-solution | |||||||||||||||
Mon | Mar 14 | No Classclass: Spring Break |
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| Wed | Mar 16 | No Classclass: Spring Break |
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| Fri | Mar 18 | No Classclass: Spring Break |
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10 | Mon | Mar 21 | Lecture 26: Java Locks - Soundness and progress guarantees | Module 2: 7.5 | N-Body problem, applications and implementations | Topic 7.5 Lecture | worksheet26 | lec26-slides | Homework 4 (includes one intermediate checkpoint) | Homework 3 (all) | WS26-solution | |||||||||||||||
| Wed | Mar 23 | Lecture 27: Dining Philosophers Problem Read-Write Locks, Linearizability of Concurrent Objects | Module Module 2: 7.63, 7.4 | Topic 7.4 3 Lecture, Topic 7.6 4 Lecture | worksheet27 | lec27-slides |
| WS27-solution | |||||||||||||||||
| Fri | Mar 25 | Lecture 28: Read-Write Pattern. Read-Write Locks. Fairness & starvation Message-Passing programming model with Actors | Module 2: 76.31, 76.52 | Topic 76.3 1 Lecture, Topic 7.5 Lecture, 6.1 Demonstration, Topic 6.2 Lecture, Topic 6.2 Demonstration | worksheet28 | lec28-slides | Quiz for Unit 7 |
| WS28-solution | ||||||||||||||||
11 | Mon | Mar 28 | Lecture 29: Task Affinity and locality. Memory hierarchy | 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 | Quiz for Unit 6 |
| WS29-solution | |||||||||||||||
| Wed | Mar 30 | Lecture 30: Reactor Pattern. Web servers Task Affinity and locality. Memory hierarchy | worksheet30 | lec30-slides |
| WS30-solution | |||||||||||||||||||
| Fri | Apr 01 | 12 | Mon | Apr 04 | Lecture 32 Lecture 31: Scan Pattern. Parallel Prefix Sum, uses and algorithms | worksheet31 | lec31-slides | Quiz for Unit 8 | Quiz for Unit 7 | : 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 | worksheet32worksheet31 | lec32lec31-slides | Homework 5 | Homework 4 Checkpoint | WS31-1solution | ||||||||
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| WedMon | Apr 0604 | Lecture 3332: Barrier Synchronization with phasersPhasers | Module 1: Section 3.4 | Topic 3.4 Lecture Lecture, Topic Topic 3.4 Demonstration | worksheet33worksheet32 | lec33lec32-slides |
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| WS32-solution | |||||||||||||||
| FriWed | Apr 0806 | Lecture 3433: 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 | worksheet34worksheet33lec34 | lec33-slides |
| Quiz for Unit 8 | WS33-solution | ||||||||||||||||
| 13 | MonFri | Apr 1108 | Lecture 35: Message-Passing programming model with Actors | Module 2: 6.1, 6.2 | 34: Fuzzy Barriers with Phasers | Module 1: Section 4.1 | Topic 4Topic 6.1 Lecture, Topic 64.1 Demonstration , Topic 6.2 Lecture, Topic 6.2 Demonstration | worksheet35worksheet34 | lec35lec34-slides |
| WS34-solution | ||||||||||||||
13 | WedMon | Apr | 1311 | Lecture 35: Eureka-style Speculative Task Parallelism |
| worksheet35 | lec35-slides |
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| WS35-solution | ||||||||||||||||
| Wed | Apr 13 | Lecture 36: Scan Pattern. Parallel Prefix Sum |
| 36: Active Object Pattern. Combining Actors with task parallelismModule 2: 6.3, 6.4 | Topic 6.3 Lecture , Topic 6.3 Demonstration , Topic 6.4 Lecture, Topic 6.4 Demonstration | worksheet36 | lec36-slides | Homework 4 (all) | WS36-solution | |||||||||||||||||
| Fri | Apr 15 | Lecture 37: Eureka-style Speculative Task Parallelism Parallel Prefix Sum applications | worksheet37 | lec37-slides | ||||||||||||||||||||||
| 14 | Mon | Apr 18 | Lecture 38: Overview of other models and frameworks | lec38-slides | ||||||||||||||||||||||
| Wed | Apr 20 | Lecture 39: Course Review (Lectures 19-38) | lec39-slides | |||||||||||||||||||||||
| Fri | Apr 22 | Lecture 40: Course Review (Lectures 19-38) | lec40-slides |
Lab Schedule
| -38) | lec40-slides | Homework 5 | ||||
Lab # | Date (2021) | Topic | Handouts | Examples | ||
|---|---|---|---|---|---|---|
| 0 | Infrastructure Setup | lab0-handout | ||||
1 | Jan 10 | Async-Finish Parallel Programming with abstract metrics | lab1-handout | |||
| - | Jan 17 |
Lab Schedule
Lab # | Date (2022) | Topic | Handouts | Examples | ||||
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1 | Jan 10 | Infrastructure setup | lab0-handout lab1-handout | |||||
| 2 | Jan 17 | Functional Programming | lab2 | |||||
2 | Jan 24 | Futures | lab2-handout | |||||
| - | Jan 31 | |||||||
3 | Feb 07 | Cutoff Strategy and Real World Performance | lab3-handout | |||||
| 4 3 | Feb 14 | DDFs | Jan 24 | Java Streams | lab4-handout lab3-handout | |||
| -4 | Feb 21 | No lab this week (Midterm exam) | ||||||
| - | Feb 28 | |||||||
| Jan 31 | Futures | lab4-handout | ||||||
5 | Feb 07 | Data-Driven Tasks | 5 | Mar 07 | Loop-level Parallelismlab5-handout | lab5-intro|||
| 6 | MarFeb 14 | Isolated Statement and Atomic VariablesAsync / Finish | lab6-handout | |||||
| - | Mar Feb 21 | No lab this week (Midterm) | ||||||
| 7 | Mar Feb 28Java Threads, Java Locks | Recursive Task Cutoff Strategy | lab7-handout | |||||
| 8 | Apr 04 | Mar 07 | Java ThreadsActors | lab8-handout | ||||
- | Apr 11 | Message Passing Interface (MPI) | Mar 14 | No lab this week (Spring Break) | ||||
| 9 | Mar 21 | Concurrent Lists | lab9-handout | |||||
| 10 | Mar 28 | Actors | lab10-handout | |||||
| 11 | Apr 04 | Loop Parallelism | lab11-handout | - | Apr 18 | Apache Spark | ||
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| Apr 11 | No lab this weekEureka-style Speculative Task Parallelism | |||||
- | Java's ForkJoin Framework | Apr 18 | No lab this week |
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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