COMP 322: Fundamentals of Parallel Programming (Spring
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2024)
Instructor: |
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Mackale Joyner, DH |
2063 |
Graduate TA:
Kumud Bhandari
Please send all emails to comp322-staff at rice dot edu
Graduate TA:
Assistant:
Sherry Nassar, sherry.nassar@rice.edu, DH 3137
Graduate TA:
Sriraj Paul
Undergrad TA:
Annirudh Prasad
Cross-listing:
ELEC 323
Yunming Zhang
HJ consultants:
Vincent Cavé, Shams Imam
Lectures:
Herzstein Hall 212
Lecture times:
MWF 1:00 - 1:50pm
Labs:
Ryon 102
Lab times:
Tuesday, 4:00 - 5:15pm (Section 3, TAs: Kumud Bhandari, Yunming Zhang)
Wednesday, 3:30 - 4:50pm (Section 2, TAs: Deepak Majeti, Sriraj Paul)
Thursday, 4:00 - 5:15pm (Section 1: Annirudh Prasad, Rishi Surendran)
Course Objectives
TAs: | Haotian Dang, Andrew Ondara, Stefan Boskovic, Huzaifa Ali, Raahim Absar | ||
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Piazza site: | https://piazza.com/rice/spring2024/comp322 (Piazza is the preferred medium for all course communications) | Cross-listing: | ELEC 323 |
Lecture location: | Herzstein Amp | Lecture times: | MWF 1:00pm - 1:50pm |
Lab locations: | Mon (Brockman 101) Tue (Herzstein Amp) | Lab times: | 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.
Course Objectives
The primary goal of COMP 322 is to introduce you to the fundamentals of parallel programming and parallel algorithms, by following The goal of COMP 322 is to introduce you to the fundamentals of parallel programming and parallel algorithms, using a pedagogic approach that exposes you to the intellectual challenges in parallel software without enmeshing you in the jargon and lower-level details of today's parallel systems. A strong grasp of the course fundamentals will enable you to quickly pick up any specific parallel programming model system that you may encounter in the future, and also prepare you for studying advanced topics related to parallelism and concurrency in more advanced courses such as COMP 422.
To ensure that students get a strong grasp of parallel programming foundations, the classes and homeworks will place equal emphasis on advancing both theoretical and practical knowledge. The programming component of the course work will initially use a simple parallel extension to the Java language called Habanero-Java (HJ), developed in the Habanero Multicore Software Research project at Rice University. Later in the course, we will introduce you to some real-world parallel programming models including Java Concurrency, .Net Task Parallel Library, MapReduce, CUDA and MPI. The use of Java will be confined to a subset of the Java language that should also be accessible to C programmers --- advanced Java features (e.g., wildcards in generics) will not be used. 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; any parallel programming primitives should be easily recognizable based on the primitives studied in COMP 322.
Course Overview
COMP 322 provides the student with a comprehensive introduction to the building blocks of parallel software, which includes the following concepts:
- Primitive constructs for task creation & termination, synchronization, task and data distribution
- Abstract models: parallel computations, computation graphs, Flynn's taxonomy (instruction vs. data parallelism), PRAM model
- Parallel algorithms for data structures that include arrays, lists, strings, trees, graphs, and key-value pairs
- Common parallel programming patterns including task parallelism, pipeline parallelism, data parallelism, divide-and-conquer parallelism, map-reduce, concurrent event processing including graphical user interfaces.
These concepts will be introduced in four modules:
- Deterministic Shared-Memory Parallelism: 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.
- Nondeterministic Shared-Memory Parallelism and Concurrency: critical sections, atomicity, isolation, high level data races, nondeterminism, linearizability, liveness/progress guarantees, actors, request-response parallelism
- Distributed-Memory Parallelism and Locality: memory hierarchies, cache affinity, false sharing, message-passing (MPI), communication overheads (bandwidth, latency), MapReduce, systolic arrays, accelerators, GPGPUs.
- Current Practice — today's Parallel Programming Models and Challenges: Java Concurrency, locks, condition variables, semaphores, memory consistency models, comparison of parallel programming models (.Net Task Parallel Library, OpenMP, CUDA, OpenCL); energy efficiency, data movement, resilience.
Prerequisite
The prerequisite course requirement is COMP 215 or equivalent. This course should be accessible to anyone familiar with the foundations of sequential algorithms and data structures, and with basic Java programming. COMP 221 is also recommended as a co-requisite.
Textbooks
There are no required textbooks for the class. Instead, lecture handouts are provided for each module as follows:
- Module 1 handout (Deterministic Shared-Memory Parallelism)
- Module 2 handout (Nondeterministic Shared-Memory Parallelism and Concurrency)
- Module 3 handout (Distributed-Memory Parallelism and Locality)
- Module 4 handout (Current Practice — today's Parallel Programming Models and Challenges)
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.
There are also a few optional textbooks that we will draw from quite heavily. 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:
- 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
Quiz Schedule
- Lab quizzes are usually published on Tuesday each week there is a lab, and are due by that Friday night.
- Lecture quizzes are usually published on Saturday each week and are due by the following Tuesday night.
- Exception: combined lecture quiz for Week 5 and Week 6 will be assigned on Thursday, Feb 14, and due by Sunday, Feb 17, night
Lecture Schedule
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Week
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Day
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Date (2013)
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Topic
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Slides
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Audio (Panopto)
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Code Examples
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Homework Assigned
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Homework Due
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1
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Mon
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Jan 7
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Lecture 1: The What and Why of Parallel Programming
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Wed
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Jan 9
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Lecture 2: Async-Finish Parallel Programming, Data & Control Flow with Async Tasks, Computation Graphs
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Fri
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Jan 11
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Lecture 3: Computation Graphs (contd), Parallel Speedup, Strong Scaling, Abstract Performance Metrics
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2
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Mon
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Jan 14
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Lecture 4: Abstract Performance Metrics (contd), Parallel Efficiency, Amdahl's Law, Weak Scaling
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Wed
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Jan 16
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Lecture 5: Data Races, Determinism, Memory Models
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Fri
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Jan 18
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Lecture 6: Data races (contd), Futures --- Tasks with Return Values
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3
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Mon
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Jan 21
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No lecture, School Holiday (Martin Luther King, Jr. Day)
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Wed
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Jan 23
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No lecture, Reading Assignment on Futures: Chapter 5 of Module 1 handout
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Fri
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Jan 25
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Lecture 7: Futures (contd), Parallel Design Patterns, Finish Accumulators
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4
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Mon
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Jan 28
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Lecture 8: Parallel N-Queens, Parallel Prefix Sum (Array Reductions with Associative Operators)
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Wed
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Jan 30
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Lecture 9: Abstract vs. Real Performance
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Fri
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Feb 1
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Lecture 10: Abstract vs. Real Performance (contd), seq clause
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5
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Mon
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Feb 04
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Lecture 11: Forasync Loops, Forasync Chunking
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Wed
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Feb 06
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Lecture 12: Forall Loops, Barrier Synchronization
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Fri
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Feb 08
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Lecture 13: Forall and Barriers, Dataflow Computing, Data-Driven Tasks
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UsefulParScoring.hj, SparseParScoring.hj
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6
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Mon
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Feb 11
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Lecture 14: Recap of HJ constructs, Point-to-point Synchronization, Pipeline Parallelism, Introduction to Phasers
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Wed
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Feb 13
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Lecture 15: Point-to-point Synchronization with Phasers
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Fri
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Feb 15
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Lecture 16: Phaser Accumulators, Bounded Phasers, Summary of Barriers and Phasers
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7
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Mon
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Feb 18
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Lecture 17: Midterm Summary
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Wed
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Feb 20
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Lecture 18: Midterm Summary (contd), Take-home Exam 1 distributed
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F
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Feb 22
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No Lecture (Exam 1 due by 5pm today)
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-
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M-F
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Feb 25- Mar 01
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Spring Break
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8
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Mon
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Mar 04
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Lecture 19: Critical sections, Isolated statement, Atomic variables
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Wed
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Mar 06
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Lecture 20: Parallel Spanning Tree algorithm, Monitors, Java Concurrent Collections
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Fri
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Mar 08
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Lecture 21: Actors
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9
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Mon
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Mar 11
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Lecture 22: Actors (contd), Linearizability of Concurrent Objects
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Wed
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Mar 13
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Lecture 23: Linearizability of Concurrent Objects (contd)
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Fri
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Mar 15
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Lecture 24: Safety and Liveness Properties
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10
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Mon
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Mar 18
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Lecture 25: Introduction to Java Threads
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Wed
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Mar 20
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Lecture 26: Java Threads (contd), Java synchronized statement
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HW4 (due by 11:55pm on March 20th)
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Fri
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Mar 22
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Lecture 27: Java synchronized statement (contd), advanced locking
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11
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Mon
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Mar 25
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Lecture 28: Java Executors and Synchronizers
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Wed
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Mar 27
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Lecture 29: Volatile Variables and Java Memory Model
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-
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Fri
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Mar 29
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Midterm Recess
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12
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Mon
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Apr 01
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Lecture 30: Task Affinity with Places
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Wed
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Apr 03
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Lecture 31: Task Affinity with Places (contd)
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HW6
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HW5 (due by 11:55pm on April 3rd)
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Fri
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Apr 05
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Lecture 32: Message Passing Interface (MPI)
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13
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Mon
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Apr 08
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Lecture 33: Message Passing Interface (MPI, contd)
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Wed
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Apr 10
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Lecture 34: Cloud Computing, Map Reduce
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Fri
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Apr 12
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Lecture 35: Map Reduce (contd)
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14
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Mon
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Apr 15
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Lecture 36: Speculative parallelization of isolated blocks
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Wed
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Apr 17
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Lecture 37: Comparison of Parallel Programming Models
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Fri
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Apr 19
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Lecture 38: Course Review, Take-home Exam 2 distributed
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Fri
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Apr 25
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No Lecture (Exam 2 due by 5pm today)
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Lab Schedule
Lab # | Date (2013) | Topic | Handouts | Code Examples |
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1 | Jan 08, 09, 10 | Infrastructure setup, Async-Finish Parallel Programming | lab1-handout | HelloWorldError.hj, ReciprocalArraySum.hj |
2 | Jan 15, 16, 17 | Abstract performance metrics with async & finish | lab2-handout | ArraySum1.hj, Search2.hj, ArraySum3.hj |
3 | Jan 22, 23, 24 | Data race detection and repair | lab3-handout | RacyArraySum1.hj, RacyFib.hj, RacyParSearch.hj, RacyFannkuch.hj |
4 | Jan 29, 30, 31 | Futures, Finish Accumulators | lab4-handout | ArraySum2.hj, ArraySum4.hj, binarytrees.hj |
5 | Feb 05, 06, 07 | Real performance, work-sharing and work-stealing runtimes | nqueens.hj, OneDimAveraging.hj | |
6 | Feb 12, 13, 14 | Barriers, Data-Driven Futures | lab6-handout | Data-Driven Future Examples: TestAsyncDDF0.hj, TestAsyncDDF2.hj |
- | Feb 19, 20, 21 | No lab (HW3 due, Exam 1 assigned) |
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7 | Mar 05, 06, 07 | Isolated Statement and Atomic Variables | lab7-handout | spanning_tree_seq.hj |
8 | Mar 12, 13, 14 | Actors | lab8-handout | PiSerial1.hj, PiSerial2.hj, PiUtil.hj, PiActor1.hj, PiActor2.hj, SieveSerial.hj, Sieve.hj, other-actor-examples |
9 | Mar 19, 20, 21 | Java Threads | ||
10 | Mar 26, 27, 28 | Java Locks | ||
11 | Apr 02, 03, 04 | Message Passing Interface (MPI) | ||
12 | Apr 09, 10, 11 | Map Reduce | ||
- | Apr 16, 17, 18 | No lab (HW6 due, Exam 2 assigned) |
Grading, Honor Code Policy, Processes and Procedures
Grading will be based on your performance on six homeworks (weighted 40% in all), two exams (weighted 20% each), weekly lecture & lab quizzes (weighted 10% in all), and class participation (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 CLEAR turn-in system. Homework is worth full credit when turned in on time. A 10% penalty per day will be levied on late homeworks, up to a maximum of 6 days. No submissions will be accepted more than 6 days after the due date.
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The desired learning outcomes fall into three major areas:
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.
2) Concurrency: critical sections, atomicity, isolation, high level data races, nondeterminism, linearizability, liveness/progress guarantees, actors, request-response parallelism, Java Concurrency, locks, condition variables, semaphores, memory consistency models.
3) Locality & Distribution: memory hierarchies, locality, data movement, message-passing, 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 homework will place equal emphasis on both theory and practice. The programming component of the course will 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. 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.
Prerequisite
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. 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 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 | Day | Date (2024) | 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 08 | Lecture 1: 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 | worksheet9 | 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 | 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 22 | Lecture 28: Dining Philosophers Problem | Topic 7.6 Lecture | worksheet28 | lec28-slides | WS28-solution | |||||
11 | Mon | Mar 25 | Lecture 29: Linearizability of Concurrent Objects | Module 2: Sections 7.4 | Topic 7.4 Lecture | worksheet29 | lec29-slides | 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 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 | 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 | 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 (2023) | Topic | Handouts | Examples |
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1 | Jan 08 | Infrastructure setup | lab0-handout lab1-handout | |
- | Jan 15 | No lab this week (MLK) | ||
2 | Jan 22 | Functional Programming | lab2-handout | |
3 | Jan 29 | Futures | lab3-handout | |
4 | Feb 05 | Data-Driven Tasks | lab4-handout | |
- | Feb 12 | No lab this week | ||
- | 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 |
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 Monday at 3pm. 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 (as shown here). Exams 1 and 2 and all quizzes are pledged under the Honor Code. They test your individual understanding and knowledge of the material. Collaboration on quizzes and exams is strictly forbidden. Quizzes are open-book and exams will be closed-book. Finally, it is also your responsibility to protect your homeworks, quizzes 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
Students with disabilities are encouraged to contact me during the first two weeks of class regarding any special needs. Students with disabilities should also contact Disabled Student Services in the Ley Student Center and the Rice Disability Support Services.