Mackale Joyner, DH 2063
Zoran Budimlić, DH 3003
|TAs:||Adrienne Li, Austin Hushower, Claire Xu, Diep Hoang, Hunena Badat, Maki Yu, Mantej Singh, Rose Zhang, Victor Song, Yidi Wang|
|Admin Assistant:||Annepha Hurlock, email@example.com , DH 3122, 713-348-5186|
https://piazza.com/rice/spring2022/comp322 (Piazza is the preferred medium for all course communications)
Herzstein Amphitheater (online 1st 2 weeks)
MWF 1:00pm - 1:50pm
Keck 100 (online 1st 2 weeks)
Mon 3:00pm - 3:50pm (Austin, Claire)
Wed 4:30pm - 5:20pm (Hunena, Mantej, Yidi, Victor, Rose, Adrienne, Diep, Maki)
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.
The primary goal of COMP 322 is to introduce you to the fundamentals of parallel programming and parallel algorithms, 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 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 (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.
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.
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:
Assigned Videos (see Canvas site for video links)
Lecture 1: Introduction
Lecture 2: Functional Programming
|Fri||Jan 14||Lecture 3: Higher order functions||worksheet3||lec3-slides|
No class: MLK
|Lecture 4: Lazy Computation||worksheet4||lec4-slides||WS4-solution|
Lecture 5: Java Streams
Lecture 6: Map Reduce with Java Streams
|Module 1: Section 2.4||Topic 2.4 Lecture, Topic 2.4 Demonstration||worksheet6||lec6-slides|
Lecture 7: Futures
|Module 1: Section 2.1||Topic 2.1 Lecture , Topic 2.1 Demonstration||worksheet7||lec7-slides|
Lecture 8: Computation Graphs, Ideal Parallelism
|Module 1: Sections 1.2, 1.3||Topic 1.2 Lecture, Topic 1.2 Demonstration, Topic 1.3 Lecture, Topic 1.3 Demonstration||worksheet8||lec8-slides||WS8-solution|
|Jan 31||Lecture 9: Async, Finish, Data-Driven Tasks|
Module 1: Section 1.1, 4.5
Topic 1.1 Lecture, Topic 1.1 Demonstration, Topic 4.5 Lecture, Topic 4.5 Demonstration
|Wed||Feb 02||Lecture 10: Event-based programming model|
|Fri||Feb 04||Lecture 11: GUI programming as an example of event-based,|
futures/callbacks in GUI programming
|worksheet11||lec11-slides||Homework 2||Homework 1||WS11-solution|
|Lecture 12: Scheduling/executing computation graphs|
Abstract performance metrics
|Module 1: Section 1.4||Topic 1.4 Lecture , Topic 1.4 Demonstration||worksheet12||lec12-slides||WS12-solution|
Lecture 13: Parallel Speedup, Critical Path, Amdahl's Law
|Module 1: Section 1.5|
Topic 1.5 Lecture , Topic 1.5 Demonstration
|No class: Spring Recess|
Lecture 14: Accumulation and reduction. Finish accumulators
|Module 1: Section 2.3||Topic 2.3 Lecture Topic 2.3 Demonstration||worksheet14||lec14-slides||WS14-solution|
Lecture 15: Recursive Task Parallelism
Lecture 16: 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||lec16-slides||Homework 3||Homework 2||WS16-solution|
Lecture 17: Midterm Review
Lecture 18: Limitations of Functional parallelism.
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|
Lecture 20: Confinement & Monitor Pattern. Critical sections
|Module 2: Sections 5.1, 5.2, 5.6||Topic 5.1 Lecture, Topic 5.1 Demonstration, Topic 5.2 Lecture, Topic 5.2 Demonstration, Topic 5.6 Lecture, Topic 5.6 Demonstration||worksheet20||lec20-slides||WS20-solution|
Lecture 21: Atomic variables, Synchronized statements
Module 2: Sections 5.4, 7.2
|Topic 5.4 Lecture, Topic 5.4 Demonstration, Topic 7.2 Lecture||worksheet21||lec21-slides||WS21-solution|
Lecture 22: Parallel Spanning Tree, other graph algorithms
Lecture 23: Java Threads and Locks
|Module 2: Sections 7.1, 7.3|
Topic 7.1 Lecture, Topic 7.3 Lecture
Lecture 24: Java Locks - Soundness and progress guarantees
|Module 2: 7.5||Topic 7.5 Lecture||worksheet24||lec24-slides|
|Lecture 25: Dining Philosophers Problem||Module 2: 7.6||Topic 7.6 Lecture||worksheet25||lec25-slides|
No class: Spring Break
|Wed||Mar 16||No class: Spring Break|
No class: Spring Break
Lecture 26: N-Body problem, applications and implementations
Lecture 27: Read-Write Locks, Linearizability of Concurrent Objects
|Module 2: 7.3, 7.4||Topic 7.3 Lecture, Topic 7.4 Lecture||worksheet27|
Lecture 28: Message-Passing programming model with Actors
|Module 2: 6.1, 6.2||Topic 6.1 Lecture, Topic 6.1 Demonstration, Topic 6.2 Lecture, Topic 6.2 Demonstration||worksheet28|
Lecture 29: Active Object Pattern. Combining Actors with task parallelism
|Module 2: 6.3, 6.4||Topic 6.3 Lecture, Topic 6.3 Demonstration, Topic 6.4 Lecture, Topic 6.4 Demonstration||worksheet29||lec29-slides|
Lecture 30: Task Affinity and locality. Memory hierarchy
Lecture 31: Data-Parallel Programming model. Loop-Level Parallelism, Loop Chunking
|Module 1: Sections 3.1, 3.2, 3.3||Topic 3.1 Lecture, Topic 3.1 Demonstration , Topic 3.2 Lecture, Topic 3.2 Demonstration, Topic 3.3 Lecture, Topic 3.3 Demonstration||worksheet31||lec31-slides||Homework 5|
|Lecture 32: Barrier Synchronization with Phasers||Module 1: Section 3.4||Topic 3.4 Lecture, Topic 3.4 Demonstration||worksheet32||lec32-slides|
Lecture 33: Stencil computation. Point-to-point Synchronization with Phasers
|Module 1: Section 4.2, 4.3|
Topic 4.2 Lecture, Topic 4.2 Demonstration, Topic 4.3 Lecture, Topic 4.3 Demonstration
Lecture 34: Fuzzy Barriers with Phasers
|Module 1: Section 4.1||Topic 4.1 Lecture, Topic 4.1 Demonstration||worksheet34||lec34-slides|
|Lecture 35: Eureka-style Speculative Task Parallelism|
|Wed||Apr 13||Lecture 36: Scan Pattern. Parallel Prefix Sum|
|Fri||Apr 15||Lecture 37: Parallel Prefix Sum applications||worksheet37||lec37-slides|
|14||Mon||Apr 18||Lecture 38: Overview of other models and frameworks||lec38-slides|
|Wed||Apr 20||Lecture 39: Course Review (Lectures 19-38)||lec39-slides|
|Fri||Apr 22||Lecture 40: Course Review (Lectures 19-38)||lec40-slides||Homework 5|
|2||Jan 17||Functional Programming||lab2-handout|
Async / Finish
No lab this week (Midterm)
|7||Feb 28||Recursive Task Cutoff Strategy||lab7-handout|
|8||Mar 07||Java Threads||lab8-handout|
No lab this week (Spring Break)
|9||Mar 21||Concurrent Lists||lab9-handout|
No lab this week
No lab this week
Grading will be based on your performance on four homework assignments (weighted 40% in all), two exams (weighted 40% in all), lab exercises (weighted 10% in all), online quizzes (weighted 5% in all), and in-class worksheets (weighted 5% in all).
The purpose of the homework is to give you practice in solving problems that deepen your understanding of concepts introduced in class. Homework is due on the dates and times specified in the course schedule. No late submissions (other than those using slip days mentioned below) will be accepted.
The slip day policy for COMP 322 is similar to that of COMP 321. All students will be given 3 slip days to use throughout the semester. When you use a slip day, you will receive up to 24 additional hours to complete the assignment. You may use these slip days in any way you see fit (3 days on one assignment, 1 day each on 3 assignments, etc.). Slip days will be tracked using the README.md file. Other than slip days, no extensions will be given unless there are exceptional circumstances (such as severe sickness, not because you have too much other work). Such extensions must be requested and approved by the instructor (via e-mail, phone, or in person) before the due date for the assignment. Last minute requests are likely to be denied.
Labs must be submitted by the following Wednesday at 4:30pm. Labs must be checked off by a TA.
Worksheets should be completed by the deadline listed in Canvas so that solutions to the worksheets can be discussed in the next class.
You will be expected to follow the Honor Code in all homework and exams. The following policies will apply to different work products in the course:
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.
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.