edX site | Autograder Guide |
COMP 322: Fundamentals of Parallel Programming (Spring
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2023)
Instructor: |
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Mackale Joyner, DH |
2063 |
Admin Assistant:
Annepha Hurlock, annepha@rice.edu, DH 3080, 713-348-5186
Graduate TAs:
Prasanth Chatarasi, Arghya Chatterjee, Yuhan Peng, Jonathan Sharman
| TAs: | 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 |
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Piazza site: |
comp322 (Piazza is the preferred medium for all course communications |
) | Cross |
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-listing: | ELEC 323 |
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Lecture location: | Herzstein |
Amphitheater | Lecture times: | MWF 1:00pm - 1:50pm |
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Lab |
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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, Jasmine) Tue 4:00pm - 4:50pm (Tina, Delaney, Chase, Hung, Jerry, Kailin) |
Course 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 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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The prerequisite course requirements are COMP 182 and COMP 215. COMP 322 should be accessible to anyone familiar with the foundations of sequential algorithms and data structures, and with basic Java programming. COMP 321 is also recommended as a co-requisite.
Textbooks and Other Resources
There are no required textbooks for the class. Instead, lecture handouts are provided for each module as follows. The links to the latest versions on Owlspace are included below:
- Module 1 handout (Parallelism)
- Module 2 handout (Concurrency)
- Module 3 handout (Distribution and Locality)
You are 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:
- Module 1 handout (Parallelism)
- Module 2 handout (Concurrency)
There There are also a few optional textbooks that we will draw from quite heavilyduring 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
Past Offerings of COMP 322
Lecture Schedule
worksheet13
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Week
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Day
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Date (2016)
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Topic
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Assigned Videos (Quizzes due by Friday of each week)
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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 11
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Lecture 1: Task Creation and Termination (Async, Finish)
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Wed
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Jan 13
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Lecture 2: Computation Graphs, Ideal Parallelism
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(2 weeks)
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2
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Mon
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Jan 18
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No lecture, School Holiday (Martin Luther King, Jr. Day)
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Wed
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Jan 20
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Lecture 4: Parallel Speedup and Amdahl's Law
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Fri
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Jan 22
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Lecture 5: Future Tasks, Functional Parallelism
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3
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Mon
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Jan 25
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Lecture 6: Memoization
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Lecture 7: Finish Accumulators
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Fri
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Jan 29
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Lecture 8: Data Races, Functional & Structural Determinism
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(2 weeks)
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4
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Mon
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Feb 01
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Lecture 9: Map Reduce
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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 11: Loop-Level Parallelism, Parallel Matrix Multiplication, Iteration Grouping (Chunking)
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Topic 3.1 Lecture, Topic 3.1 Demonstration, Topic 3.2 Lecture, Topic 3.2 Demonstration, Topic 3.3 Lecture, Topic 3.3 Demonstration
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5
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Mon
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Feb 08
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Lecture 12: Barrier Synchronization
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Wed
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Feb 10
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Lecture 13: Iterative Averaging Revisited, SPMD pattern
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Fri
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Feb 12
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Lecture 14: Data-Driven Tasks and Data-Driven Futures
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(5 weeks, with two intermediate checkpoints)
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Homework 2, Lecture & demo quizzes for topics 3.4, 3.5, 3.6, 4.5
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6
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Mon
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Feb 15
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Lecture 15: Phasers, Point-to-point Synchronization
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Wed
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Feb 17
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Lecture 16: Pipeline Parallelism, Signal Statement, Fuzzy Barriers
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Fri
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Feb 19
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Lecture 17: Abstract vs. Real Performance
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7
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Mon
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Feb 22
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Lecture 18: Midterm Summary
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Wed
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Feb 24
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Midterm Review (Interactice Q&A using PollEverywhere)
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Fri
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Feb 26
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Lecture 19: Task Scheduling Policies
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-
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M-F
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Feb 29- Mar 04
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Spring Break
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8
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Mon
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Mar 07
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Lecture 20: Critical sections, Isolated construct, Parallel Spanning Tree algorithm
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Wed
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Mar 09
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Lecture 21: Atomic variables, Read-Write Isolation
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Fri
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Mar 11
Lecture 22: Parallelism in Java Streams, Parallel Prefix Sums
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Homework 3 Checkpoint-2, Lecture & demo quizzes for topics 5.1 to 5.6
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9
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Mon
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Mar 14
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Lecture 23: Java Threads, Java synchronized statement
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Wed
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Mar 16
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Lecture 24: Java synchronized statement (contd), wait/notify
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Fri
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Mar 18
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Lecture 25: Concurrent Objects, Linearizability of Concurrent Objects
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Homework 4
(3 weeks, with one intermediate checkpoint)
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Homework 3, Lecture quizzes for topics 7.1 - 7.4
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10
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Mon
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Mar 21
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Lecture 26: Safety and Liveness Properties
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Wed
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Mar 23
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Lecture 27: Actors
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Fri
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Mar 25
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Lecture 28: Actors (contd)
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Lecture & demo quizzes for topics 7.5, 6.1 - 6.6
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11
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Mon
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Mar 28
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Lecture 29: Dining Philosophers Problem
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Wed
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Mar 30
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Lecture 30: Eureka-style Speculative Task Parallelism
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Fri
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Apr 01
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Midterm Recess
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12
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Mon
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Apr 04
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Lecture 31: Task Affinity with Places
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Wed
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Apr 06
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Lecture 32: Apache Spark framework for cluster computing
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Fri
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Apr 08
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Lecture 33: Message Passing Interface (MPI)
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Homework 5
(2 weeks, with 1-week automatic extension)
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Homework 4
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13
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Mon
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Apr 11
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Lecture 34: Message Passing Interface (MPI, contd)
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Wed
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Apr 13
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Lecture 35: PGAS languages
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Fri
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Apr 15
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Lecture 36: Memory Consistency Models
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lec36-slides
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14
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Mon
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Apr 18
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Lecture 37: GPU Computing
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Wed
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Apr 20
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Lecture 38: Fortress language
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Fri
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Apr 22
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Lecture 39: Course Review (lectures 20-37), Last day of classes
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TBD
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TBD
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Scheduled final exam
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Lab Schedule
Lab # | Date (2015) | Topic | Handouts | Code Examples |
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| 0 | Infrastructure Setup | lab0-handout | - | |
1 | Jan 13 | Async-Finish Parallel Programming | lab1-handout, lab1-slides | lab_1.zip |
2 | Jan 20 | Abstract performance metrics with async & finish | lab2-handout, lab2-slides | lab_2.zip |
3 | Jan 27 | DIY HJ-lib Programming, Futures, HJ-Viz | lab3-handout, lab3-slides | lab_3.zip |
4 | Feb 03 | Finish Accumulators and Loop-Level Parallelism | lab4-handout and lab4-slides | lab_4.zip |
5 | Feb 10 | Loop Chunking and Barrier Synchronization | lab5-handout and lab5-slides | lab_5.zip |
6 | Feb 17 | Data-Driven Futures and Phasers | lab6-handout | lab_6.zip |
- | Feb 24 | No lab this week — Exam 1 | - | - |
- | Mar 02 | No lab this week — Spring Break | - | - |
7 | Mar 09 | Isolated Statement and Atomic Variables | lab7-handout | |
8 | Mar 16 | Java Threads and Locks | lab8-handout | |
| 9 | Mar 23 | Actors and Selectors | lab9-handout | |
10 | Mar 30 | Eureka-style Speculative Task Parallelism | lab10-handout | |
11 | Apr 06 | Parallel Pretty Pictures | lab11-handout | |
12 | Apr 13 | Spark | lab12-handout | |
| 13 | Apr 20 | Message Passing Interface (MPI) | lab13-handout |
Grading, Honor Code Policy, Processes and Procedures
Grading will be based on your performance on five homeworks (weighted 40% in all), two exams (weighted 40% in all), weekly lab exercises (weighted 10% in all), and class participation including worksheets, in-class Q&A, Piazza participation, and online quizzes (weighted 10% in all).
The purpose of the homeworks is to train you to solve problems and to help deepen your understanding of concepts introduced in class. Homeworks are due on the dates and times specified in the course schedule. Please turn in all your homeworks using the subversion system set up for the class. Homework is worth full credit when turned in on time. No late submissions (other than those using slip days mentioned below) will be accepted.
As in 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.). If you use slip days, you must submit a SLIPDAY.txt file in your SVN homework folder before the actual submission deadline indicating the number of slip days that you plan to use. 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. If you do receive an extension from the instructor, please indicate this by placing an EXTENSION.txt file in your SVN homework folder before the actual submission deadline indicating the date that the extension was granted by the instructor as well as the length of the extension.
Labs must be checked off by a TA prior to the start of the lab the following week.
Worksheets are due by the beginning of the class after they are distributed, so that solutions to the worksheets can be discussed.
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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 | |
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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 Lecture, Topic 2.4 Demonstration | worksheet6 | lec6-slides | WS6-solution | |||
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 | 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: Recursive Task Parallelism | worksheet14 | lec14-slides | WS14-solution | |||||
Wed | Feb 15 | Lecture 15: 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 | worksheet15 | lec15-slides | Homework 2 | WS15-solution | |||
| Fri | Feb 17 | Lecture 16: Limitations of Functional 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 | WS18-solution | |||||||
Fri | Feb 24 | 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 27 | 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 | lec20-slides | WS20-solution | |||
Wed | Mar 01 | Lecture 21: 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 | Homework 3 | WS21-solution | |||
Fri | Mar 03 | Lecture 22: Parallel Spanning Tree, other graph algorithms | worksheet22 | lec22-slides | Homework 4 | WS22-solution | |||||
9 | Mon | Mar 06 | Lecture 23: Java Threads and Locks | Module 2: Sections 7.1, 7.3 | Topic 7.1 Lecture, Topic 7.3 Lecture | worksheet23 | lec23-slides | WS23-solution | |||
Wed | Mar 08 | Lecture 24: Java Locks - Soundness and progress guarantees | Module 2: 7.5 | Topic 7.5 Lecture | worksheet24 | lec24-slides | WS24-solution | ||||
Fri | Mar 10 | Lecture 25: Dining Philosophers Problem | Module 2: 7.6 | Topic 7.6 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: N-Body problem, applications and implementations | worksheet26 | lec26-slides | WS26-solution | |||||
Wed | Mar 22 | Lecture 27: Read-Write Locks, Linearizability of Concurrent Objects | Module 2: 7.3, 7.4 | Topic 7.3 Lecture, Topic 7.4 Lecture | worksheet27 | lec27-slides | WS27-solution | ||||
Fri | Mar 24 | Lecture 28: Message-Passing programming model with Actors | Module 2: 6.1, 6.2 | Topic 6.1 Lecture, Topic 6.1 Demonstration, Topic 6.2 Lecture, Topic 6.2 Demonstration | worksheet28 | lec28-slides | WS28-solution | ||||
11 | Mon | Mar 27 | Lecture 29: 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 | WS29-solution | |||
Wed | Mar 29 | Lecture 30: Task Affinity and locality. Memory hierarchy | worksheet30 | lec30-slides | Homework 4 | WS30-solution | |||||
Fri | Mar 31 | Lecture 31: 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 | worksheet31 | lec31-slides | Homework 5 | WS31-solution | |||
12 | Mon | Apr 03 | Lecture 32: Barrier Synchronization with Phasers | Module 1: Section 3.4 | Topic 3.4 Lecture, Topic 3.4 Demonstration | worksheet32 | lec32-slides | WS32-solution | |||
Wed | Apr 05 | Lecture 33: 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 | worksheet33 | lec33-slides | WS33-solution | ||||
Fri | Apr 07 | Lecture 34: Fuzzy Barriers with Phasers | Module 1: Section 4.1 | Topic 4.1 Lecture, Topic 4.1 Demonstration | worksheet34 | lec34-slides | WS34-solution | ||||
13 | Mon | Apr 10 | Lecture 35: Eureka-style Speculative Task Parallelism | worksheet35 | lec35-slides | WS35-solution | |||||
| Wed | Apr 12 | Lecture 36: Scan Pattern. Parallel Prefix Sum | worksheet36 | lec36-slides | WS36-solution | ||||||
| Fri | Apr 14 | Lecture 37: Parallel Prefix Sum applications | worksheet37 | lec37-slides | |||||||
| 14 | Mon | Apr 17 | Lecture 38: Overview of other models and frameworks | lec38-slides | |||||||
| Wed | Apr 19 | Lecture 39: Course Review (Lectures 19-38) | lec39-slides | Homework 5 | |||||||
| Fri | Apr 21 | Lecture 40: Course Review (Lectures 19-38) | lec40-slides |
Lab Schedule
Lab # | Date (2023) | Topic | Handouts | Examples |
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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 | Recursive Task Cutoff Strategy | lab6-handout | |
| 7 | Mar 06 | Java Threads | lab7-handout | |
| - | Mar 13 | No lab this week (Spring Break) | ||
| 8 | Mar 20 | Concurrent Lists | lab8-handout | |
| 9 | Mar 27 | Actors | lab9-handout | |
| - | Apr 03 | TBD | ||
| 10 | Apr 10 | Loop Parallelism | lab10-handout | |
- | Apr 17 | No lab this week |
Grading, Honor Code Policy, Processes and Procedures
Grading will be based on your performance on four homework assignments (weighted 40% in all), two exams (weighted 40% in all), lab exercises (weighted 10% in all), online quizzes (weighted 5% in all), and in-class worksheets (weighted 5% in all).
The purpose of the homework is to give you practice in solving problems that deepen your understanding of concepts introduced in class. 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 tracked using 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 the following Wednesday at 4:30pm. Labs must be checked off by a TA.
Worksheets should be completed 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 homework and exams. The following policies will apply to different work products in the course:
- In-class worksheets: You are free to discuss all aspects of in-class worksheets with your other classmates, the teaching assistants and the professor during the class. You can work in a group and write down the solution that you obtained as a group. If you work on the worksheet outside of class (e.g., due to an absence), then it must be entirely your individual effort, without discussion with any other students. If you use any material from external sources, you must provide proper attribution.
- Weekly lab assignments: You are free to discuss all aspects of lab assignments with your other classmates, the teaching assistants and the professor during the lab. However, all code and reports that you submit are expected to be the result of your individual effort. If you work on the lab outside of class (e.g., due to an absence), then it must be entirely your individual effort, without discussion with any other students. If you use any material from external sources, you must provide proper attribution (as shown here).
- Homework: All submitted homework is expected to be the result of your individual effort. You are free to discuss course material and approaches to
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- problems with your other classmates, the teaching assistants and the professor, but you should never misrepresent someone else’s work as your own. If you use any material from external sources, you must provide proper attribution.
- Quizzes: Each online quiz will be an open-notes individual test. The student may consult their course materials and notes when taking the quizzes, but may not consult any other external sources.
- Exams: Each exam will be a open-book, open-notes, and open-computer individual test, which must be completed within a specified time limit. No external materials may be consulted when taking the exams.
For grade disputes, please send an email to the course instructors within 7 days of receiving your grade. The email subject should include COMP 322 and the assignment. Please provide enough information in the email so that the instructor does not need to perform a checkout of your code must provide proper attribution (as shown here). Exams 1 and 2 test your individual understanding and knowledge of the material. Exams are closed-book, and collaboration on exams is strictly forbidden. Finally, it is also your responsibility to protect your homeworks and exams from unauthorized access. Graded homeworks will be returned to you via email, and exams as marked-up hardcopies. If you believe we have made an error in grading your homework or exam, please bring the matter to our attention within one week.
Accommodations for Students with Special Needs
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