edX site | Autograder Guide |
COMP 322: Fundamentals of Parallel Programming (Spring
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2024)
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Instructor: | Mackale Joyner, DH 2063 |
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| TAs: |
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| Haotian Dang, Andrew Ondara, Stefan Boskovic, Huzaifa Ali, Raahim Absar | |
Piazza site: |
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Piazza site:
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: |
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00pm - |
1: |
50pm |
Lab locations: |
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Mon (Brockman 101) Tue (Herzstein Amp) | Lab times: |
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Mon 3: |
00pm - |
3: |
50pm ( |
SB, |
HA, |
AO) |
Tue 4: |
00pm - |
4: |
50pm ( |
RA, HD) |
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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- 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
Lecture Schedule
Week |
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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
Day | Date (2024 |
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Week
Day
) | Lecture | Assigned Reading | Assigned Videos (see Canvas site for video links) | In-class Worksheets | Slides | Work Assigned | Work Due |
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| Worksheet Solutions | ||
|---|---|---|
1 | Mon | Jan |
08 | Lecture 1: |
Wed
Jan 27
Lecture 2: Computation Graphs, Ideal Parallelism
Homework 1
2
Mon
Feb 01
Lecture 4: Parallel Speedup and Amdahl's Law
Wed
Feb 03
Fri
Feb 05
Lecture 6: Finish Accumulators
Lecture 7: Map Reduce
Wed
Feb 10
Lecture 8: Data Races, Functional & Structural Determinism
Homework 2
Fri
Feb 12
Lecture 9: Java’s Fork/Join Library
4
Mon
Feb 15
Fri
Feb 19
Lecture 11: Iteration Grouping (Chunking), Barrier Synchronization
Topic 3.3 Lecture , Topic 3.3 Demonstration, Topic 3.4 Lecture , Topic 3.4 Demonstration
5
Mon
Feb 22
Lecture 12: Parallelism in Java Streams, Parallel Prefix Sums
Wed
Feb 24
Lecture 13: Iterative Averaging Revisited, SPMD pattern
Homework 3 (includes one intermediate checkpoint)
Quiz for Unit 3
Fri
Feb 26
Lecture 14: Data-Driven Tasks
Wed
Mar 03
Lecture 15: Point-to-point Synchronization with Phasers
Fri
Mar 05
Lecture 16: Pipeline Parallelism, Signal Statement, Fuzzy Barriers
7
Mon
Mar 08
Lecture 17: Midterm Review
Wed
Mar 10
Lecture 18: Abstract vs. Real Performance
Fri
Mar 12
Lecture 19: Critical Sections, Isolated construct (start of Module 2)
8
Mon
Mar 15
Lecture 20: Parallel Spanning Tree algorithm, Atomic variables
Homework 3, Checkpoint-1
Wed
Mar 17
Lecture 21: Actors
Topic 6.1 Lecture , Topic 6.1 Demonstration , Topic 6.2 Lecture, Topic 6.2 Demonstration
Fri
Mar 19
Lecture 22: Actors (contd)
Quiz for Unit 4
9
Mon
Mar 22
Lecture 23: Actors (contd)
Wed
Mar 24
Lecture 24: Java Threads, Java synchronized statement
10
Mon
Mar 29
Lecture 25: Java Threads, Java synchronized statement (contd), wait/notify
Introduction | worksheet1 | lec1-slides | WS1-solution | ||||||||
Wed | Jan 10 | Lecture 2: Functional Programming | worksheet2 | lec02-slides | WS2-solution | ||||||
| Fri | Jan 12 | Lecture 3: Higher order functions | worksheet3 | lec3-slides | WS3-solution | ||||||
2 | Mon | Jan 15 | No class: MLK | ||||||||
Wed | Jan 17 | Lecture 4: Lazy Computation | worksheet4 | lec4-slides | WS4-solution | ||||||
Fri | Jan 19 | Lecture 5: Java Streams | worksheet5 | lec5-slides | Homework 1 | WS5-solution | |||||
| 3 | Mon | Jan 22 | 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 24 | Lecture 7: Futures | Module 1: Section 2.1 | Topic 2.1 Lecture , Topic 2.1 Demonstration | worksheet7 | lec7-slides | WS7-solution | ||||
Fri | Jan 26 | 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 29 | 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 | Jan 31 | Lecture 10: Event-based programming model | worksheet10 | lec10-slides | Homework 1 | WS10-solution | |||||
| Fri | Feb 02 | 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 05 | 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 07 | 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 09 | No class: Spring Recess | |||||||||
| 6 | Mon | Feb 12 | 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 14 | Lecture 15: Limitations of Functional parallelism. | worksheet15 | lec15-slides | Homework 2 | WS15-solution | |||||
| Fri | Feb 16 | Lecture 16: Recursive Task Parallelism | worksheet16 | lec16-slides | Homework 3 | WS16-solution | |||||
7 | Mon | Feb 19 | Lecture 17: Midterm Review | lec17-slides | |||||||
Wed | Feb 21 | Lecture 18: Midterm Review | lec18-slides | ||||||||
Fri | Feb 23 | 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 26 | 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 | Feb 28 | 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 01 | 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 04 | 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 06 | 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 08 | 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 11 | No class: Spring Break | |||||||||
| Wed | Mar 13 | No class: Spring Break | |||||||||
Fri | Mar 15 | No class: Spring Break | |||||||||
10 | Mon | Mar 18 | 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 20 |
Wed
Mar 31
Lecture 26: Java Threads (exercise)
Fri
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 |
Quiz for Unit 6
| Homework 3 (CP 2) | WS27-solution | |||||||||
Fri | Mar 22 | Lecture 28: Dining Philosophers Problem | Topic 7.6 Lecture | worksheet28 | lec28-slides | WS28-solution |
11 | Mon |
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Mar 25 | Lecture |
29: |
Linearizability of Concurrent Objects | Module 2: Sections 7.4 | Topic 7.4 Lecture |
| worksheet29 |
| lec29-slides |
Homework 4 (includes one intermediate checkpoint)
Wed
Apr 07
Lecture 29: Java Locks (exercise)
Fri
Apr 09
Lecture 30: Safety and Liveness Properties, Java Synchronizers, Dining Philosophers Problem
Quiz for Unit 7
Quiz for Unit 6
| WS29-solution | |||||||||||
Wed | Mar 27 | Lecture 30: Parallel Spanning Tree, other graph algorithms | worksheet30 | lec30-slides | WS30-solution | ||||||
Fri | Mar 29 | 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 |
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01 | 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 |
Wed
Apr 14
Lecture 32: Message Passing Interface (MPI, contd)
| lec32-slides |
Homework 4 | Homework 3 (All) | WS32-solution | |||||||||
Wed | Apr 03 | Lecture 33: Task Affinity and locality. Memory hierarchy | worksheet33 | lec33-slides | WS33-solution | ||||||
Fri | Apr 05 | Lecture 34: Eureka-style Speculative Task Parallelism | worksheet34 | lec34-slides | WS34-solution | ||||||
13 | Mon | Apr 08 | No class: Solar Eclipse | ||||||||
| Wed | Apr 10 | Lecture 35: Scan Pattern. Parallel Prefix Sum | worksheet35 | lec35-slides | Homework 4 (CP 1) | WS35-solution | |||||
| Fri | Apr 12 | Lecture 36: Parallel Prefix Sum applications | worksheet36 | lec36-slides | WS36-solution | ||||||
| 14 | Mon | Apr 15 | Lecture 37: Overview of other models and frameworks | lec37-slides | |||||||
| Wed | Apr 17 |
Fri
Apr 16
Lecture 33: Message Passing Interface (MPI, contd)
Homework 4 Checkpoint-1
13
Mon
Apr 19
Lecture 34: Task Affinity with Places
lec34-slides
Quiz for Unit 8
Wed
Apr 21
Lecture 35: Eureka-style Speculative Task Parallelism
Fri
Apr 24
14
Mon
Apr 26
TBD
| Lecture 38: Course Review (Lectures 19-34) |
| lec38-slides |
| Homework 4 ( |
| All) | ||
| Fri | Apr |
| 19 | Lecture 39: Course Review (Lectures 19-34) | lec39-slides |
Lab Schedule
Lab # | Date ( |
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2023) | Topic | Handouts | Examples |
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1 |
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Jan 08 | Infrastructure |
setup |
1
Jan 26
| - |
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| Jan 15 | No lab this week |
| (MLK) | ||
| 2 |
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Feb 09
| Jan 22 | Functional Programming |
| lab2-handout |
3 |
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Feb 16
Jan 29 | Futures | lab3-handout |
| 4 | Feb |
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| 05 | Data-Driven Tasks | lab4- |
| handout | |
- |
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Feb 12 | No lab this week |
5
Mar 09
| - |
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| Feb 19 |
-
| No lab this week ( |
| Midterm Exam) | ||||
| 5 | Feb 26 | Loop Parallelism | lab5-handout | image kernels |
|---|---|---|---|---|
| 6 | Mar 04 | Recursive Task Cutoff Strategy | lab6-handout | |
| - | Mar 11 | No lab this week (Spring Break) | ||
| 7 | Mar 18 | Java Threads | lab7-handout | |
| 8 | Mar 25 | Concurrent Lists | lab8-handout | |
| 9 | Apr 01 | Actors | lab9-handout | |
| - | Apr 08 | No lab this week (Solar Eclipse) | ||
- | Apr 15 | No lab this week |
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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 11:59pm3pm. 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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