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COMP 322: Fundamentals of Parallel Programming (Spring
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2025)
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Instructor: | Mackale Joyner, DH 2063 |
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| TAs: |
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Piazza site:
https://piazza.com/rice/spring2021/comp322 (Piazza is the preferred medium for all course communications)
Cross-listing:
ELEC 323
Lecture location:
Fully Online
Lecture times:
MWF 1:30pm - 2:25pm
Lab locations:
Fully Online
Lab times:
Tu 1:30pm - 2:25pm (TV, MS, TG)
Th 4:50pm - 5:45pm (XW, RW, TR)
Course Syllabus
| 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 |
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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 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., MapReduce
To To achieve these learning outcomes, each class period will include time for both instructor lectures and in-class exercises based on assigned reading and videos. The lab exercises will be used to help students gain hands-on programming experience with the concepts introduced in the lectures.
To ensure that students gain a strong knowledge of parallel programming foundations, the classes and homeworks homework will place equal emphasis on both theory and practice. The programming component of the course will mostly use the Habanero-Java Library (HJ-lib) pedagogic extension to the Java language developed in the Habanero Extreme Scale Software Research project at Rice University. The course will also introduce you to real-world parallel programming models including Java Concurrency, MapReduce, MPI, OpenCL and CUDA. An important goal is that, at the end of COMP 322, you should feel comfortable programming in any parallel language for which you are familiar with the underlying sequential language (Java or C). Any parallel programming primitives that you encounter in the future should be easily recognizable based on the fundamentals studied in COMP 322.
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There
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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 (2021)
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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 25
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Lecture 1: Task Creation and Termination (Async, Finish)
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- 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 | |
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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 | 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 | WS32-solution | |||
Wed | Apr 09 | Lecture 33: Task Affinity and locality. Memory hierarchy | worksheet33 | lec33-slides | Homework 4 | Homework 3 (All) | 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 |
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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 | |
| - | Mar 31 | No lab this week | ||
| 8 | Apr 07 | Concurrent Lists | lab8-handout | |
| 9 | Apr 14 | Actors | lab9-handout | |
- | Apr 21 | No lab this week |
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Wed
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Jan 27
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Lecture 2: Computation Graphs, Ideal Parallelism
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Homework 1
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2
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Mon
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Feb 01
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Lecture 4: Parallel Speedup and Amdahl's Law
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Wed
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Feb 03
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Fri
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Feb 05
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Lecture 6: Finish Accumulators
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Lecture 7: Map Reduce
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Wed
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Feb 10
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Lecture 8: Data Races, Functional & Structural Determinism
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Homework 2
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Fri
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Feb 12
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Lecture 9: Java’s Fork/Join Library
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4
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Mon
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Feb 15
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Fri
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Feb 19
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Lecture 11: Iteration Grouping (Chunking), Barrier Synchronization
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Topic 3.3 Lecture , Topic 3.3 Demonstration, Topic 3.4 Lecture , Topic 3.4 Demonstration
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5
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Mon
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Feb 22
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Lecture 12: Parallelism in Java Streams, Parallel Prefix Sums
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Wed
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Feb 24
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Lecture 13: Iterative Averaging Revisited, SPMD pattern
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Homework 3 (includes one intermediate checkpoint)
Quiz for Unit 3
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Fri
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Feb 26
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Lecture 14: Data-Driven Tasks
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Wed
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Mar 03
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Lecture 15: Point-to-point Synchronization with Phasers
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Fri
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Mar 05
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Lecture 16: Pipeline Parallelism, Signal Statement, Fuzzy Barriers
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7
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Mon
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Mar 08
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Lecture 17: Midterm Review
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Wed
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Mar 10
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Lecture 18: Abstract vs. Real Performance
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Fri
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Mar 12
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Lecture 19: Critical Sections, Isolated construct (start of Module 2)
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8
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Mon
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Mar 15
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Lecture 20: Parallel Spanning Tree algorithm, Atomic variables
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Wed
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Mar 17
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Lecture 21: Actors
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Topic 6.1 Lecture , Topic 6.1 Demonstration , Topic 6.2 Lecture, Topic 6.2 Demonstration
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Fri
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Mar 19
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Lecture 22: Actors (contd)
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Quiz for Unit 4
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9
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Mon
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Mar 22
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Lecture 23: Actors (contd)
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Wed
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Mar 24
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Lecture 24: Java Threads, Java synchronized statement
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10
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Mon
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Mar 29
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Lecture 25: Java Threads, Java synchronized statement (contd), wait/notify
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Wed
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Mar 31
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Lecture 26: Java Threads (exercise)
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Fri
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Apr 02
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Lecture 27: Java Locks
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Quiz for Unit 6
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11
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Mon
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Apr 05
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Lecture 28: Linearizability of Concurrent Objects
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Homework 4 (includes one intermediate checkpoint)
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Wed
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Apr 07
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Lecture 29: Java Locks (exercise)
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Fri
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Apr 09
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Lecture 30: Safety and Liveness Properties, Java Synchronizers, Dining Philosophers Problem
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Quiz for Unit 7
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Quiz for Unit 6
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12
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Mon
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Apr 12
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Lecture 31: Message Passing Interface (MPI), (start of Module 3)
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Wed
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Apr 14
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Lecture 32: Message Passing Interface (MPI, contd)
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Homework 4 Checkpoint-1
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Fri
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Apr 16
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Lecture 33: Message Passing Interface (MPI, contd)
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13
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Mon
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Apr 19
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Lecture 34: Task Affinity with Places
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lec34-slides
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Quiz for Unit 8
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Wed
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Apr 21
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Lecture 35: Eureka-style Speculative Task Parallelism
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Fri
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Apr 24
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14
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Mon
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Apr 26
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TBD
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Lab Schedule
Lab #
Date (2021)
Topic
Handouts
Examples
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Jan 26
Async-Finish Parallel Programming with abstract metrics
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Feb 09
Futures
3
Feb 16
Cutoff Strategy and Real World Performance
Feb 23
DDFs
5
Mar 09
Loop-level Parallelism
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Mar 16
No lab this week (Spring "Sprinkle" Day)
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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 at 11:59pmFriday at 4pm. 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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