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edX site | Autograder Guide |
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Instructor: | Mackale Joyner, DH 2063 |
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| TAs: |
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| 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 |
Tory Songyang, Zishi Wang
Piazza site:
https://piazza.com/configure-classes/spring2020/comp322 (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:
Sewell Hall 301
Lecture times:
MWF 1:00pm - 1:50pm
Lab locations:
Sewell Hall 301
Lab times:
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 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 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:
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:
Finally, here are some additional resources that may be helpful for you:
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Week
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Day
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Date (2020)
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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 13
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Lecture 1: Task Creation and Termination (Async, Finish)
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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 | 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 # | 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 15
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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 20
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No lecture, School Holiday (Martin Luther King, Jr. Day)
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Wed
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Jan 22
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Lecture 4: Parallel Speedup and Amdahl's Law
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Fri
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Jan 24
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3
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Mon
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Jan 27
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Lecture 6: Finish Accumulators
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Lecture 7: Map Reduce
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Fri
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Jan 31
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Lecture 8: Data Races, Functional & Structural Determinism
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4
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Mon
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Feb 03
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Lecture 9: Java’s Fork/Join Library
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Wed
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Feb 05
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Fri
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Feb 07
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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 10
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Lecture 12: Parallelism in Java Streams, Parallel Prefix Sums
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Wed
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Feb 12
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Lecture 13: Iterative Averaging Revisited, SPMD pattern
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Homework 3 (includes 2 intermediate checkpoints)
Quiz for Unit 3
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Fri
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Feb 14
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Spring Recess
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6
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Mon
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Feb 17
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Lecture 14: Data-Driven Tasks
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Wed
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Feb 19
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Lecture 15: Point-to-point Synchronization with Phasers
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Fri
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Feb 21
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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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Feb 24
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Lecture 17: Midterm Review
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Wed
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Feb 26
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Lecture 18: Abstract vs. Real Performance
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Fri
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Feb 28
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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 02
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Lecture 20: Parallel Spanning Tree algorithm, Atomic variables
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Wed
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Mar 04
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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 06
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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 09
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Lecture 23: Java Threads, Java synchronized statement
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Wed
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Mar 11
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Lecture 24: TBD
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Homework 3, Checkpoint-2
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Fri
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Mar 13
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M-F
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Mar 16 - Mar 20
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Spring Break
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10
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Mon
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Mar 23
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Lecture 25: Java synchronized statement (contd), wait/notify
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Wed
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Mar 25
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Lecture 26: Java Locks, Linearizability of Concurrent Objects
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Homework 4
(includes one intermediate checkpoint)
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Fri
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Mar 27
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Lecture 27: Safety and Liveness Properties, Java Synchronizers, Dining Philosophers Problem
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Homework 3 (all)
Quiz for Unit 6
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11
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Mon
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Mar 30
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Lecture 28: Message Passing Interface (MPI), (start of Module 3)
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lec28-slides
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Wed
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Apr 01
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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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Apr 03
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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 06
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Lecture 31: TF-IDF and PageRank Algorithms with Map-Reduce
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Wed
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Apr 08
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TBD
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Homework 4 Checkpoint-1
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Fri
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Apr 10
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Lecture 32: Partitioned Global Address Space (PGAS) programming models
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Quiz for Unit 8
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13
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Mon
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Apr 13
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Lecture 33: Combining Distribution and Multithreading
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Wed
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Apr 15
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Lecture 34: Task Affinity with Places
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Homework 5
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Homework 4 (all)
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Fri
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Apr 17
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Lecture 35: Eureka-style Speculative Task Parallelism
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lec35-slides
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14
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Mon
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Apr 20
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Lecture 36: Algorithms based on Parallel Prefix (Scan) operations
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Wed
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Apr 22
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Fri
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Apr 24
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Lecture 38: Course Review (Lectures 20-38)
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Homework 5
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Lab #
Date (2020)
Topic
Handouts
Examples
1
Jan 16
Async-Finish Parallel Programming with abstract metrics
2
Jan 30
Futures
3
Feb 06
Cutoff Strategy and Real World Performance
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Feb 20
DDFs
5
Feb 27
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Mar 05
Loop-level Parallelism
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Mar 12
Isolated Statement and Atomic Variables
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No lab this week - Spring Break
Apr 02
Java Threads, Java Locks
10
Apr 09
Message Passing Interface (MPI)
Apache Spark
Eureka-style Speculative Task Parallelism
Java's ForkJoin Framework
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 4pm. 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:
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