edX site | Autograder Guide |
COMP 322: Fundamentals of Parallel Programming (Spring
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2024)
Instructor: |
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Mackale Joyner, DH |
2063 |
Graduate TA:
Please send all emails to comp322-staff at rice dot edu
Graduate TA:
Alina Sbirlea
Assistant:
Amanda Nokleby, akn3@rice.edu, DH 3137
Graduate TA:
Dragos Sbirlea
Undergrad TA:
Max Grossman
Undergrad TA:
Damien Stone
Undergrad TA:
Andrew Whiddon
Research Programmer:
Vincent Cave
Lectures:
Duncan Hall (DH) 1070
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 |
Undergrad TA:
Yunming Zhang
Lecture location: | Herzstein Amp |
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Lecture times: | MWF 1: |
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00pm - 1:50pm |
Lab locations: |
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Mon (Brockman 101) Tue (Herzstein Amp) | Lab times: |
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Tuesday, 4:00 - 5:20pm (Section 3)
Wednesday, 3:30 - 4:50pm (Section 2)
Thursday, 4:00 - 5:20pm (Section 1)
Introduction
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 The goal of COMP 322 is to introduce you to the fundamentals of parallel programming and parallel algorithms, using by following a pedagogical pedagogic approach that exposes you to the intellectual challenges in parallel software without enmeshing you in lowthe jargon and lower-level details of different today's parallel systems. To that end, the main pre-requisite course requirement is COMP 211 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.
The pedagogical approach will introduce you to the following foundations of parallel programming:
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A strong grasp of the course fundamentals will enable you to quickly pick up any specific parallel programming system that you may encounter in the future, and also prepare you for studying advanced topics related to parallelism and concurrency in courses such as COMP 422.
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
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(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 Laboratory assignments will explore these topics through a simple parallel extension to the Java language called Habanero-Java (HJ), developed in the Habanero Multicore Software Research project at Rice University. The use of Java will be confined to a subset of the Java 1.4 language that should also be accessible to C programmers --- no advanced Java features (e.g., generics) will be used. An abstract performance model for HJ programs will be available to aid you in complexity analysis of parallel programs before you embark on performance evaluations on real parallel machines. We will conclude the course by introducing you to some real-world parallel programming models including the Java Concurrency Utilities, Google's MapReduce, CUDA and MPI. The foundations gained in this course will prepare you for advanced courses on Parallel Computing offered at Rice (COMP 422, COMP 522).
Since the aim of the course is for you to gain both theoretical and practical knowledge of the foundations of parallel programming, the weightage for course work will be balanced across homeworks, exams, and lab attendance.
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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. You will be . 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 However, there are 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
Lecture Schedule
Week | Day | Date ( |
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2024) |
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Topic
Lecture | Assigned Reading | Assigned Videos (see Canvas site for video links) | In-class Worksheets | Slides |
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Work Assigned |
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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: |
HW2
HW1
Higher order functions | worksheet3 | lec3-slides | WS3-solution | |||||
2 |
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Mon | Jan |
School Holiday
4
15 | No class: MLK | ||||||||
Wed | Jan |
17 | Lecture 4: |
Lazy Computation | worksheet4 | lec4-slides | WS4-solution |
Fri | Jan |
19 | Lecture 5: |
HW2
Java Streams | worksheet5 | lec5-slides | Homework 1 | WS5-solution | ||||
3 |
Mon | Jan |
22 | Lecture 6: |
HW3
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 |
8
Fri
Jan 27
Lecture 8: Parallel Quicksort
9
Mon
Jan 30
Lecture 9: PRAM model, Amdahl's Law
10
Wed
Feb 01
Lecture 10: Critical sections and the Isolated statement
11
Fri
Feb 03
Lecture 11: Abstract vs Real Performance, Work-sharing & Work-stealing schedulers
12
Mon
Feb 06
Lecture 12: Barrier Synchronization in Forall Loops
HW3
13
Wed
Feb 08
Lecture 13: Barrier Synchronization in Forall Loops (contd)
HW4
14
Fri
Feb 10
Lecture 14: Point-to-point Synchronization and Phasers
15
Mon
Feb 13
Lecture 15: Point-to-point Synchronization and Phasers (contd)
16
Wed
Feb 15
Lecture 16: Advanced Phaser topics
17
Fri
Feb 17
Lecture 17: Dataflow Programming and Data-Driven Futures
HW4
18
Mon
Feb 20
Lecture 18: Dataflow Programming and Data-Driven Futures, contd.
19
Wed
Feb 22
Lecture 19: Midterm Summary
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Fri
Feb 24
Exam 1 (in class)
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M-F
Feb 27 - Mar 02
Spring Break
20
Mon
Mar 05
Lecture 20: Java Atomic Variables
HW5
21
Wed
Mar 07
Lecture 21: Java Concurrent Collections
22
Fri
Mar 09
Lecture 22: Linearizability of Concurrent Objects
23
Mon
Mar 12
Lecture 23: Task Affinity with Places
24
Wed
Mar 14
Lecture 24: Task Affinity with Places, contd.
25
Fri
Mar 16
Lecture 25: Map Reduce
26
Mon
Mar 19
Lecture 26: Map Reduce, contd.
HW5
27
Wed
Mar 21
Lecture 27: Dataflow Programming with Intel Concurrent Collections
HW6
-
Fri
Mar 23
Midterm Recess
28
Mon
Mar 26
Lecture 28: Java Threads
29
Wed
Mar 28
Lecture 29: Java Threads (contd), synchronized statement
30
Fri
Mar 30
Lecture 30: Java synchronized statement with wait/notify
31
Mon
Apr 02
Lecture 31: Advanced locking in Java
32
Wed
Apr 04
Lecture 32: Java Executors and Synchronizers
HW6
33
Fri
Apr 06
Lecture 33: Volatile Variables and Java Memory Model
HW7
34
Mon
Apr 09
Lecture 34: GPGPU programming with CUDA
35
Wed
Apr 11
Lecture 35: CUDA contd.
36
Fri
Apr 13
Lecture 36: Liveness and Progress Guarantees
37
Mon
Apr 16
Lecture 37: Introduction to MPI
38
Wed
Apr 18
Lecture 38: Introduction to MPI (contd)
39
Fri
Apr 20
Lecture 39: Course Summary
Exam 2 (Take-home)
HW7
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Fri
Apr 27
Exam 2 due
Exam 2
Lab Schedule ||
: 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 |
Lab #
Date (2011)
Topic
Handouts
1
Jan 10, 11, 12
Infrastructure setup
2
Jan 17, 18, 19
Abstract performance metrics with async & finish
3
Jan 23, 25, 26
Data race detection
4
Jan 30 Feb 01, 02
Points, regions, forall loops
5
Feb 07, 08, 09
Abstract vs Real Performance, Work-sharing & Work-stealing schedulers
6
Feb 14, 15, 16
Barriers and Phasers
-
Feb 21, 22, 23
No lab (midterm week)
7
Mar 06, 07, 08
Atomic Variables
8
Mar 13, 14, 15
Places
9
Mar 20, 21, 22
Data Driven Tasks
10
Mar 27, 28, 29
Java Concurrency I
11
Apr 03, 04, 05
Java Concurrency II
12
Apr 10, 11, 12
CUDA
13
Apr 17, 18, 19
MPI
Grading, Honor Code Policy, Processes and Procedures
Grading will be based on your performance on homeworks (worth 50%) and exams (20% for first exam, and 30% for the second examfour 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 homeworks homework is to train you to solve problems and to help give you practice in solving problems that deepen your understanding of concepts introduced in class. Homeworks and programming assignments are Homework is 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 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 homeworks and exams. All submitted homeworks are expected 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 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
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- .
- 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.
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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.
Past Offerings of COMP 322
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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.