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:
Rishi Surendran
Assistant:
Penny Anderson, anderson@rice.edu, DH 3080
Graduate TA:
Yunming Zhang
Undergrad TA:
Kyle Kurihara
| TAs: | Haotian Dang, Andrew Ondara, Stefan Boskovic, Huzaifa Ali, Raahim Absar | ||
|---|---|---|---|
Piazza site: | https://piazza.com/rice/spring2024/comp322 (Piazza is the preferred medium for all course communications) | Cross-listing: | ELEC 323 |
Lecture location: |
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Max Payton
Course consultants:
Vincent Cavé, Shams Imam, Maggie Tang, Bing Xue
Lectures:
Herzstein Hall 212
Herzstein Amp | Lecture |
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times: | MWF 1: |
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00pm - 1:50pm |
Labs:
Lab locations: | Mon (Brockman 101) Tue (Herzstein Amp) | Lab times: |
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Monday, 4:00 - 5:30pm (Section A01, Staff: Yunming, Kumud, Wenxuan, Maggie)
Wednesday, 4:30 - 6:00pm (Section A02, Staff: Rishi, Kyle, Max, Bing)
Course Objectives
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 The goal of COMP 322 is to introduce you to the fundamentals of parallel programming and parallel algorithms, using by following 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 gain a strong knowledge of parallel programming foundations, the classes and homeworks 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 Multicore 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.
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 three 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, systolic arrays.
- Nondeterministic Shared-Memory Parallelism and 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.
- Distributed-Memory Parallelism and Locality: memory hierarchies, cache affinity, data movement, message-passing (MPI), communication overheads (bandwidth, latency), MapReduce, accelerators, GPGPUs, CUDA, OpenCL, energy efficiency, resilience.
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 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)
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
Lecture Schedule
lec35-slides
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Week
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Day
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Date (2014)
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Topic
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Videos
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In-class Worksheets
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Code Examples
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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: The What and Why of Parallel Programming, Task Creation and Termination (Async, Finish)
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Demo File: ReciprocalArraySum.java
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Topic 1.1 Lecture Quiz, Topic 1.1 Demo Quiz
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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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Topic 1.2 Lecture Quiz , Topic 1.2 Demo Quiz , Topic 1.3 Lecture Quiz , Topic 1.3 Demo Quiz
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...
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Fri
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Jan 17
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Lecture 3: , Abstract Performance Metrics, Multiprocessor Scheduling
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Worksheet File: Search.java
Homework 1 Files: QuicksortUtil.java , QuicksortSeq.java , QuicksortPar.java
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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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No lecture (inclement weather)
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3
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Mon
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Jan 27
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Lecture 5: Future Tasks, Functional Parallelism
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Wed
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Jan 29
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Lecture 6: Finish Accumulators
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Demo File: Nqueens.java
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Fri
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Jan 31
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Lecture 7: 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 8: Map Reduce
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Demo File(s): WordCount.java, words.txt
Worksheet Files: WordCount.java , words.txt
Homework 2 Files: GeneralizedReduce.java, GeneralizedReduceApp.java, SumReduction.java, TestSumReduction.java
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Wed
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Feb 05
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Lecture 9: Memoization
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Demo File: PascalsTriangleWithFuture.java
Worksheet File: PascalsTriangleMemoized.java
Worksheet Solution: PascalsTriangleMemoizedSolution.java
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Fri
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Feb 07
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Lecture 10: Abstract vs. Real Performance
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5
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Mon
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Feb 10
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Lecture 11: Loop-Level Parallelism, Parallel Matrix Multiplication
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Wed
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Feb 12
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Lecture 12: Iteration Grouping (Chunking), Barrier Synchronization
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Fri
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Feb 14
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Lecture 13: Iterative Averaging Revisited
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Demo File: OneDimAveragingGrouped.java, OneDimAveragingBarrier.java
Worksheet File: OneDimAveragingBarrier.java
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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 and Data-Driven Futures
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Wed
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Feb 19
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Lecture 15: Review of Module-1 HJ-lib API's
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Fri
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Feb 21
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Lecture 16: Point-to-point Synchronization with Phasers
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7
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Mon
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Feb 24
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Lecture 17: Phasers (contd), Signal Statement, Fuzzy Barriers
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Wed
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Feb 26
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Lecture 18: Midterm Summary, Take-home Exam 1 distributed
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F
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Feb 28
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No Lecture (Exam 1 due by 4pm today)
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-
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M-F
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Feb 28- Mar 09
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Spring Break
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8
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Mon
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Mar 10
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Lecture 19: Critical sections, Isolated construct, Parallel Spanning Tree algorithm
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Wed
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Mar 12
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Lecture 20: Speculative parallelization of isolated constructs (Guest lecture by Prof. Swarat Chaudhuri)
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Homework 3
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Fri
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Mar 14
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Lecture 21: Read-Write Isolation, Atomic variables
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9
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Mon
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Mar 17
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Lecture 22: Actors
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Homework 4 Files: hw4_files.zip
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Wed
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Mar 19
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Lecture 23: Actors (contd)
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Fri
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Mar 21
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Lecture 24: Monitors, Java Concurrent Collections, Linearizability of Concurrent Objects
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10
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Mon
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Mar 24
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Lecture 25: Linearizability (contd), Intro to Java Threads
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Wed
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Mar 26
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Lecture 26: Java Threads (contd), Java synchronized statement
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Fri
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Mar 28
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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 31
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Lecture 28: Safety and Liveness Properties
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Wed
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Apr 02
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Lecture 29: Dining Philosophers Problem
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Homework 4 (due by 11:55pm on April 2nd)
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-
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Fri
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Apr 04
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Midterm Recess
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12
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Mon
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Apr 07
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Lecture 30: Message Passing Interface (MPI)
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Wed
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Apr 09
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Lecture 31: Partitioned Global Address Space (PGAS) languages (Guest lecture by Prof. John Mellor-Crummey)
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Fri
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Apr 11
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Lecture 32: Message Passing Interface (MPI, contd)
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13
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Mon
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Apr 14
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Lecture 33: Task Affinity with Places
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Wed
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Apr 16
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Lecture 34: GPU Computing
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Fri
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Apr 18
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Lecture 35: Memory Consistency Models
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Homework 6 (written only)
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14
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Mon
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Apr 21
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Lecture 36: Comparison of Parallel Programming Models
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Homework 5 (due by 11:55pm on Monday, April 21st)
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Wed
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Apr 23
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Lecture 37: TBD
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Fri
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Apr 25
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Lecture 38: Course Review, Take-home Exam 2 distributed (last day of classes)
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Fri
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May 02
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Exam 2 due by 4pm today
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Exam 2
Lab Schedule
Lab # | Date (2014) | Topic | Handouts | Code Examples |
|---|---|---|---|---|
1 | Jan 13, 15 | Infrastructure setup, Async-Finish Parallel Programming | lab1-handout | HelloWorldError.java, ReciprocalArraySum.java |
- | Jan 20, 22 | No lab this week — Jan 20 is Martin Luther King, Jr. Day | ||
2 | Jan 27, 29 | Abstract performance metrics with async & finish | lab2-handout | ArraySum1.java , ArraySumUtil.java Search2.java , ArraySumLoop.java , ArraySumRecursive.java |
3 | Feb 03, 05 | Futures | lab3-handout | ArraySum2.java, ArraySum4.java, BinaryTrees.java |
4 | Feb 10, 12 | Real Performance from Finish Accumulators and Loop-Level Parallelism | Nqueens.java, OneDimAveraging.java, Linux/Sugar Tutorial | |
5 | Feb 17, 19 | Futures vs. Data-Driven Futures | lab5-handout | MatrixEval.java, test.txt |
6 | Feb 24, 26 | Barriers and Phasers | lab6-handout | OneDimAveraging.java |
- | Mar 03, 05 | No lab this week — Spring Break | ||
7 | Mar 10, 12 | Isolated Statement and Atomic Variables | lab7-handout | spanning_tree_seq.java |
8 | Mar 17, 19 | Actors | lab8-handout | PiSerial1.java PiActor1.java PiSerial2.java PiActor2.java PiUtil.java Sieve.java SieveSerial.java |
| 9 | Mar 24, 26 | Java Threads | lab9-handout | nqueens_hj.java spanning_tree_atomic_hj.java |
10 | Mar 31, Apr 02 | Java Locks | lab10-handout | lab10.zip |
11 | Apr 07, 09 | Message Passing Interface (MPI) | lab11-handout | lab_11.zip |
12 | Apr 14, 16 | Map Reduce | lab12-handout | |
| - | Apr 21, 23 | No lab this week — Last Week of Classes |
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:
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 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
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 | 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 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 |
|---|---|---|---|---|
1 | Jan 08 | Infrastructure setup | ||
| - | 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 are 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
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