COMP 322: Fundamentals of Parallel Programming (Spring
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2022)
Instructors: | 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 | Instructor: | Prof. Vivek Sarkar, DH 3080 | Head TA: | Max Grossman |
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Admin Assistant: | Annepha Hurlock, annepha@rice.edu , DH | 30803122, 713-348-5186 | Graduate TAs: | Prasanth Chatarasi, Arghya Chatterjee, Yuhan Peng, Jonathan Sharman | |||
Co-Instructor: | Dr. Shams Imam | Undergraduate TAs: | Prudhvi Boyapalli, Peter Elmers, Nicholas Hanson-Holtry, Ayush Narayan, Timothy Newton, Alitha Partono, Tom Roush, Hunter Tidwell, Bing Xue | ||||
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Piazza site: | https://piazza.com/rice/classspring2022/iirz0u74egl2q9comp322 (Piazza is the preferred medium for all course communications, but you can also send email to comp322-staff at rice dot edu if needed)) | Cross-listing: | ELEC 323 | ||||
Lecture location: | Herzstein Hall 210Amphitheater (online 1st 2 weeks) | Lecture times: | MWF 1:00pm - 1:50pm (followed by office hours in Duncan Hall 3092 during 2pm - 3pm) | ||||
Lab locationlocations: | DH 1064 (Section A01), DH 1070 (Section A02Keck 100 (online 1st 2 weeks) | Lab times: | Wednesday, 07Mon 3:00pm - 08:30pm3:50pm (Austin, Claire) Wed 4:30pm - 5:20pm (Hunena, Mantej, Yidi, Victor, Rose, Adrienne, Diep, Maki) |
Course Syllabus
A summary PDF file containing the course syllabus for the course can be found here. Much of the syllabus information is also included below in this course web site, along with some additional details that are not included in the syllabus.
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The desired learning outcomes fall into three major areas (course modules):
1) Parallelism: functional programming, Java streams, creation and coordination of parallelism (async, finish), abstract performance metrics (work, critical paths), Amdahl's Law, weak vs. strong scaling, data races and determinism, data race avoidance (immutability, futures, accumulators, dataflow), deadlock avoidance, abstract vs. real performance (granularity, scalability), collective & point-to-point synchronization (phasers, barriers), parallel algorithms, systolic algorithms.
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3) Locality & Distribution: memory hierarchies, locality, cache affinity, data movement, message-passing (MPI), communication overheads (bandwidth, latency), MapReduce, accelerators, GPGPUs, CUDA, OpenCL.
To achieve these learning outcomes, each class period will include time for both instructor lectures and in-class exercises based on assigned reading and videos. The lab exercises will be used to help students gain hands-on programming experience with the concepts introduced in the lectures.
To ensure that students gain a strong knowledge of parallel programming foundations, the classes and homeworks homework will place equal emphasis on both theory and practice. The programming component of the course will mostly use the Habanero-Java Library (HJ-lib) pedagogic extension to the Java language developed in the Habanero Extreme Scale Software Research project at Rice University. The course will also introduce you to real-world parallel programming models including Java Concurrency, MapReduce, MPI, OpenCL and CUDA. An important goal is that, at the end of COMP 322, you should feel comfortable programming in any parallel language for which you are familiar with the underlying sequential language (Java or C). Any parallel programming primitives that you encounter in the future should be easily recognizable based on the fundamentals studied in COMP 322.
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The prerequisite course requirements are COMP 182 and COMP 215. COMP 322 should be accessible to anyone familiar with the foundations of sequential algorithms and data structures, and with basic Java programming. COMP 321 is also recommended as a co-requisite.
Textbooks and Other Resources
There are no required textbooks for the class. Instead, lecture handouts are provided for each module as follows. The links to the latest versions on Owlspace are included below:
- Module 1 handout (Parallelism)
- Module 2 handout (Concurrency)
- Module 3 handout (Distribution and Locality)
You are You are expected to read the relevant sections in each lecture handout before coming to the lecture. We will also provide a number of references in the slides and handouts.The links to the latest versions of the lecture handouts are included below:
There are also There are also a few optional textbooks that we will draw from quite heavilyduring the course. You are encouraged to get copies of any or all of these books. They will serve as useful references both during and after this course:
- Fork-Join Parallelism with a Data-Structures Focus (FJP) by Dan Grossman (Chapter 7 in Topics in Parallel and Distributed Computing)
- Java Concurrency in Practice by Brian Goetz with Tim Peierls, Joshua Bloch, Joseph Bowbeer, David Holmes and Doug Lea
- Principles of Parallel Programming by Calvin Lin and Lawrence Snyder
- The Art of Multiprocessor Programming by Maurice Herlihy and Nir Shavit
Past Offerings of COMP 322
Lecture Schedule
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Lecture Schedule
Week | Day | Date (2022) | 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 10 | Lecture 1: Introduction |
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| Wed | Jan 12 | Lecture 2: Functional Programming | GList.java | worksheet2 | lec02-slides |
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Fri | Jan 14 | Lecture 3: Higher order functions | worksheet3 | lec3-slides |
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2 | Mon | Jan 17 | No class: MLK | ||||||||||||||||||||||||
Week | Day | Date (2016) | Topic | Assigned Reading | Assigned Videos (Quizzes due by Friday of each week) | In-class Worksheets | Slides | Work Assigned | Work Due | ||||||||||||||||||
1 | Mon | Jan 11 | Lecture 1: Task Creation and Termination (Async, Finish) | Module 1: Section 1.1 | Topic 1.1 Lecture, Topic 1.1 Demonstrationworksheet1 | lec1-slides |
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| Wed | Jan 13 | Lecture 2: 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 | worksheet2 | lec2-slides||||||||||||
| FriWed | Jan | 1519 | Lecture | 3: Abstract Performance Metrics, Multiprocessor Scheduling4: Lazy Computation | LazyList.java Lazy.java | worksheet4 | lec4-slides | WS4-solution | ||||||||||||||||||
| Fri | Jan 21 | Lecture 5: Java Streams | worksheet5 | lec5 | Module 1: Section 1.4 | Topic 1.4 Lecture, Topic 1.4 Demonstration | worksheet3 | lec3-slides | Homework 1 (2 weeks) | Lecture & demo quizzes for topics 1.1, 1.2, 1.3, 1.4 | WS5-solution | |||||||||||||||
3 | Mon | Jan 24 | 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 | 2 | Mon | Jan 18 | No lecture, School Holiday (Martin Luther King, Jr. Day) | ||||||||||||||
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| Wed | Jan 2026 | Lecture 4: Parallel Speedup and Amdahl's Law7: Futures | Module 1: Section 2.1.5 | Topic 2.1 .5 Lecture , Topic 2.1 .5 Demonstration | worksheet4worksheet7 | lec4lec7-slides |
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| Fri | Jan 2228 | Lecture 5: Future Tasks, Functional 8: Computation Graphs, Ideal Parallelism | Module 1: Section Sections 1.2, 1.13 | Topic 1.2 Lecture, Topic 1.2 Demonstration, Topic 1.3 Lecture, Topic 21.1 3 Demonstration | worksheet5worksheet8 | lec5lec8-slides | Lecture & demo quizzes for topics 1.5, 2.1 (topic 1.6 is optional) | 3 | Mon | Jan 25 | WS8-solution | |||||||||||||||
4 | Mon
| Jan 31 | Lecture 9: Async, Finish, Data-Driven Tasks | Lecture 6: MemoizationModule 1: Section 2.2 | Topic 2.2 Lecture , Topic 2.2 Demonstration | worksheet6 | lec6-slides | Worksheet5.java1.1, 4.5
| Topic 1.1 Lecture, Topic 1.1 Demonstration, Topic 4.5 Lecture, Topic 4.5 Demonstration | worksheet9 | lec9-slides | WS9-solution | |||||||||||||||
WedJan | 27Feb 02 | Lecture 7: Finish Accumulators | Module 1: Section 2.3 | Topic 2.3 Lecture , Topic 2.3 Demonstration | worksheet7 | lec7-slides | 10: Event-based programming model
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Fri | Jan 29Feb 04 | Lecture | 8: Data Races, Functional & Structural Determinism11: GUI programming as an example of event-based, futures/callbacks in GUI programming | worksheet11 | lec11 | Module 1: Sections 2.5, 2.6 | Topic 2.5 Lecture , Topic 2.5 Demonstration, Topic 2.6 Lecture , Topic 2.6 Demonstration | worksheet8 | lec8-slides | Homework 2 | Homework 1 | WS11-solution | |||||||||||||||
5 | 2 JARs (optional)Homework 1, Lecture & demo quizzes for topics 2.2, 2.3, 2.5, 2.6 | 4Mon | Feb | 0107 | Lecture | 9: Map Reduce12: Scheduling/executing computation graphs Abstract performance metrics | Module 1: Section 21.4 | Topic | 21.4 Lecture | , | Topic | 21.4 Demonstration | worksheet9 | worksheet12 | lec9lec12-slides | WS12-solution | |||||||||||
| Wed | Feb 0309 | Lecture 10: Java’s Fork/Join Library | FJP chapter: Sections 7.3 & 7.5 | worksheet10 | lec10-slides | 13: Parallel Speedup, Critical Path, Amdahl's Law | Module 1: Section 1.5 | Topic 1.5 Lecture , Topic 1.5 Demonstration | worksheet13 | lec13-slides | WS13-solutionArraySumFourWay.java | |||||||||||||||
| Fri | Feb 0511 | No class: Spring Recess
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6 | Mon | Feb 14 | 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 | |||||||||||||||||||
| Wed | Feb 16 | Lecture 15: Recursive Task Parallelism | worksheet15 | lec15 | Lecture 11: Loop-Level Parallelism, Parallel Matrix Multiplication, Iteration Grouping (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 | worksheet11 | lec11-slides | Lecture & demo quizzes for topics 2.4, 3.1, 3.2, 3.3 | 5 | Mon | Feb 08 | Lecture 12: Barrier Synchronization | Module 1: Section 3.4 | Topic 3.4 Lecture , Topic 3.4 Demonstration | worksheet12 | lec12-slides |
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Fri | Feb | 1018 | Lecture | 13: Iterative Averaging Revisited, SPMD pattern16: Data Races, Functional & Structural Determinism | Module 1: Sections 32.5, 32.6 | Topic | 32.5 Lecture , Topic | 32.5 Demonstration, Topic | 32.6 Lecture, Topic | 32.6 Demonstration | worksheet13worksheet16 | lec13lec16-slides | Worksheet12.javaHomework 3 | Homework 2 | WS16-solution | ||||||||||||
7 | FriMon | Feb 1221 | Lecture 1417: Data-Driven Tasks and Data-Driven Futures | Module 1: Section 4.5 | Topic 4.5 Lecture, Topic 4.5 Demonstration | worksheet14 | lec14-slides | (5 weeks, with two intermediate checkpoints) | Homework 2, Lecture & demo quizzes for topics | 6 | Mon | Feb 15 | Lecture 15: Phasers, Point-to-point Synchronization | Midterm Review | lec17-slides | ||||||||||||
| Wed | Feb 23 | Lecture 18: Limitations of Functional parallelism. | worksheet18 | lec18-slides | WS18-solution | |||||||||||||||||||||
| Fri | Feb 25 | 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 | Module 1: Sections 4.2, 4.3 | Topic 4.2 Lecture, Topic 4.2 Demonstration, Topic 4.3 Lecture, Topic 4.3 Demonstration | worksheet15 | lec15-slides | WS19-solution | ||||||||||||||||
8 | WedMon | Feb 1728 | Lecture 16: Pipeline Parallelism, Signal Statement, Fuzzy Barriers | Module 1: Sections 4.4, 4.1 | Topic 4.4 Lecture, Topic 4.4 Demonstration, Topic 4.1 Lecture, Topic 4.1 Demonstration, | worksheet16 | lec16-slides | 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 | Fri | Feb 19 | Lecture 17: Abstract vs. Real Performance | WS20-solution | |||||||||||
| Wed | Mar 02 | 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 | |||||||||||||||||||
| Fri | Mar 04 | worksheet17 | lec17-slides | Lecture & demo quizzes for topics 4.1, 4.2, 4.3, 4.4 | 7 | Mon | Feb 22 | Lecture 18: Midterm Summary | lec18-slides | Lecture 22: Parallel Spanning Tree, other graph algorithms |
| Wed | Feb 24 | Midterm Review (Interactice Q&A using PollEverywhere) | Exam 1 held during lab time (7:00pm - 10:00pm) | |||||||||||
| Fri | Feb 26 | Lecture 19: Task Scheduling Policies | Topic 4.6 Lecture, Topic 4.6 Demonstration | worksheet19 | lec19-slides | Lec19HelpFirstWorkStealing.java | Homework 3 Checkpoint-1, Lecture & demo quizzes for topic 4.6 | |||||||||||||||||||
- | M-F | Feb 29- Mar 04 | Spring Break |
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8 | Mon | Mar 07 | Lecture 20: Critical sections, Isolated construct, Parallel Spanning Tree algorithm | Module 2: Sections 5.1, 5.2, 5.3, 5.6 | Topic 5.1 Lecture, Topic 5.1 Demonstration, Topic 5.2 Lecture, Topic 5.2 Demonstration, Topic 5.3 Lecture, Topic 5.3 Demonstration | worksheet20 | lec20-slides |
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| Wed | Mar 09 | Lecture 21: Atomic variables, Read-Write Isolation | Module 2: Sections 5.4, 5.5 | Topic 5.4 Lecture, Topic 5.4 Demonstration, Topic 5.5 Lecture, Topic 5.5 Demonstration, Topic 5.6 Lecture, Topic 5.6 Demonstration | worksheet21 | lec21-slides |
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| Fri | Mar 11 | Lecture 22: Parallel computing using Java Streams | worksheet22 | lec22-slides | Homework 3 Checkpoint-2, Lecture & demo quizzes for topics 5.1 to 5.6 | |||||||||||||||||||||
worksheet22 | lec22-slides | Homework 4 | Homework 3 | WS22-solution | |||||||||||||||||||||||
9 | Mon | Mar 07 | Lecture 23: Java Threads and Locks | Module 2: Sections 7.1, 7.3 | Topic 7.1 Lecture, Topic 7.3 Lecture | worksheet23 | lec23-slides |
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| Wed | Mar 09 | Lecture 24: Java Locks - Soundness and progress guarantees | Module 2: 7.5 | Topic 7.5 Lecture | worksheet24 | lec24-slides |
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| Fri | Mar 11 | Lecture 25: Dining Philosophers Problem | Module 2: 7.6 | Topic 7.6 Lecture | worksheet25 | lec25-slides |
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Mon | Mar 14 | No class: Spring Break |
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Wed | Mar 16 | No class: Spring Break |
| 9 | Mon | Mar 14 | Lecture 23: Java Threads, Java synchronized statement | Topic 7.1 Lecture, Topic 7.2 Lecture | worksheet23 | lec23-slides | |||||||||||||||||
| WedFri | Mar 1618Lecture 24: Java synchronized statement (contd), advanced locking | No class: Spring Break | Topic 7.3 Lecture | worksheet24 | lec24-slides
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10 | FriMon | Mar 1821 | Lecture 25: Concurrent Objects, Linearizability of Concurrent Objects26: N-Body problem, applications and implementations | Topic 7.4 Lecture | worksheet25worksheet26 | lec25lec26-slides | Homework 4 (3 weeks, with one intermediate checkpoint) | Homework 3, Lecture quizzes for topics 7.1 - 7.4 | 10 | Mon | Mar 21 | Lecture 26: Safety and Liveness Properties | Topic 7.5 Lecture | worksheet26 | WS26-solution | ||||||||||||
| Wed | Mar 23 | Lecture 27: Read-Write Locks, Linearizability of Concurrent Objects | Module 2: 7.3, 7.4 | Topic 7.3 Lecture, Topic 7.4 Lecture | worksheet27 | lec27lec26-slides |
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| Fri | Mar | 2325 | Lecture | 27: Actors28: 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 | , Topic 6.3 Lecture, Topic 6.3 Demonstrationworksheet28 | lec28-slides |
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11 | FriMon | Mar 2528 | Lecture 28: Actors (contd) | 29: Active Object Pattern. Combining Actors with task parallelism | Module 2: 6.3, 6.4 | Topic 6.3 Topic 6.4 Lecture, Topic 6.4 Demonstration, Topic 6.5 Lecture, Topic 6.5 3 Demonstration, Topic 6.6 4 Lecture, Topic 6.6 4 Demonstration | worksheet28worksheet29 | lec28lec29-slides |
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| Lecture & demo quizzes for topics 7.5, 6.1 - 6.6 | 11 | Mon | Mar 28 | Lecture 29: Dining Philosophers Problem | Topic 7.6 Lecture | worksheet29 | lec29-slides | WS29-solution | ||||||||
| Wed | Mar 30 | Lecture 30: Task Affinity and locality. Memory hierarchy | Wed | Mar 30 | Lecture 30: Eureka-style Speculative Task Parallelism worksheet30 | lec30-slides |
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| Fri | Apr 01 | 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 | Homework 4 | WS31-solution | |||||||||||||||||
12 | Mon | Apr 04 | Lecture 32: Barrier Synchronization with Phasers | Module 1: Section 3.4 | Topic 3.4 Lecture, Topic 3.4 Demonstration | worksheet32 | lec32-slides |
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| WS32-solution | Midterm Recess | Homework 4 Checkpoint-1, Lecture quiz for topic 7.6 | 12 | Mon | Apr 04 | Lecture 31: Task Affinity with Places | worksheet31 | lec31-slides | |||||||||
| Wed | Apr 06 | Lecture 32: Apache Spark framework for cluster computing | worksheet32 | lec32-slides | 33: Stencil computation. Point-to-point Synchronization with Phasers | Module 1: Section 4.2, 4.3 | Topic 4.2 Lecture, Topic 4.2 Demonstration, Topic 4.3 Lecture, Topic 4.3 Demonstration | worksheet33 | lec33-slides |
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| Fri | Apr 08 | Lecture 33: Message Passing Interface (MPI) | worksheet33 | lec33-slides | Homework 5 (2 weeks, with 1-week automatic extension) | 34: Fuzzy Barriers with Phasers | Module 1: Section 4.1 | Topic 4.1 Lecture, Topic 4.1 Demonstration | worksheet34 | lec34-slides |
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13 | Mon | Apr 11 | Lecture 34: Message Passing Interface (MPI, contd)35: Eureka-style Speculative Task Parallelism |
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Wed | Apr 13 | Lecture | 35: PGAS languages36: Scan Pattern. Parallel Prefix Sum |
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Fri | Apr 15 | Lecture | 36: Memory Consistency Models37: Parallel Prefix Sum applications | worksheet36worksheet37 | lec36lec37-slides | ||||||||||||||||||||||
14 | Mon | Apr 18 | Lecture | 37: GPU Computing38: Overview of other models and frameworks | worksheet-37 | lec37lec38-slides | |||||||||||||||||||||
Wed | Apr 20 | Lecture | 38: Fortress language39: Course Review (Lectures 19-38) | lec38lec39-slides | |||||||||||||||||||||||
Fri | Apr 22 | Lecture | 3940: Course Review ( | lectures 20Lectures 19- | 3738) | , Last day of classeslec39lec40-slides | Homework 5 | (automatic extension till April 29)- | TBD | TBD | Scheduled final exam |
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Lab Schedule
Lab # | Date (20152022) | Topic | Handouts | Code Examples | 0|||
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1 | Jan 10 | Infrastructure | Setupsetup | lab0-handout lab1-handout | 1|||
2 | Jan | 13Async-Finish Parallel17 | Functional Programming | lab1lab2-handout | , lab1-slideslab_1.zip | ||
2 | Jan 20 | Abstract performance metrics with async & finish | lab2-handout, lab2-slides | lab_2.zip | |||
3 | Jan 27 | Futures and HJ-Viz | lab3-handout, lab3-slides | lab_3.zip | |||
4 | Feb 03 | Finish Accumulators and Loop-Level Parallelism | lab4-handout and lab4-slides | lab_4.zip | |||
5 | Feb 10 | Loop Chunking and Barrier Synchronization | lab5-handout and lab5-slides | lab_5.zip | |||
6 | Feb 17 | Data-Driven Futures and Phasers | lab6-handout | lab_6.zip | |||
- | Feb 24 | No lab this week — Exam 1 | - | - | |||
- | Mar 02 | No lab this week — Spring Break | - | - | |||
7 | Mar 09 | Isolated Statement and Atomic Variables | lab7-handout | ||||
8 | Mar 16 | Java Threads and Locks | lab8-handout | ||||
3 | Jan 24 | Java Streams | lab3-handout | ||||
4 | Jan 31 | Futures | lab4-handout | ||||
5 | Feb 07 | Data-Driven Tasks | lab5-handout | ||||
6 | Feb 14 | Async / Finish | lab6-handout | ||||
- | Feb 21 | No lab this week (Midterm) | |||||
7 | Feb 28 | Recursive Task Cutoff Strategy | lab7-handout | ||||
8 | Mar 07 | Java Threads | lab8-handout | ||||
- | Mar 14 | No lab this week (Spring Break) | |||||
9 | Mar 21 | Concurrent Lists | 9 | Mar 23 | Actors and Selectors | lab9-handout | |
10 | Mar | 30Eureka-style Speculative Task Parallelism28 | Actors | lab10-handout | |||
11 | Apr | 06Parallel Pretty Pictures04 | Loop Parallelism | lab11-handout | |||
12- | Apr 13 | Spark | 11 | No lab this week | lab12-handout | 13 | |
- | Apr | 20Message Passing Interface (MPI) | lab13-handout18 | No lab this week |
Grading, Honor Code Policy, Processes and Procedures
Grading will be based on your performance on five homeworks four homework assignments (weighted 40% in all), two exams (weighted 40% in all), weekly lab exercises (weighted 10% in all), and class participation including worksheets, in-class Q&A, Piazza participation, and online quizzes (weighted 10% 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 are Homework is due on the dates and times specified in the course schedule. Please turn in all your homeworks using the subversion system set up for the class. Homework is worth full credit when turned in on time. No No late submissions (other than those using slip days mentioned below) will be accepted.
As in COMP 321, all 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.). If you use slip days, you must submit a SLIPDAY.txt file in your SVN homework folder before the actual submission deadline indicating the number of slip days that you plan to useSlip 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 Last minute requests are likely to be denied. If you do receive an extension from the instructor, please indicate this by placing an EXTENSION.txt file in your SVN homework folder before the actual submission deadline indicating the date that the extension was granted by the instructor as well as the length of the extension.
Labs must be submitted by the following Wednesday at 4:30pm. Labs must Labs must be checked off by a TA prior to the start of the lab the following week.
Worksheets are due by the beginning of the class after they are distributed, 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 homework and exams. All submitted homeworks are expected to be the result of your individual effort. The following policies will apply to different work products in the course:
- In-class worksheets: You are free to discuss
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- all aspects of in-class worksheets with your other classmates, the teaching assistants and the professor
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- 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.
- 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 test your individual understanding and knowledge of the material. Exams are closed-book, and collaboration on exams is strictly forbidden. Finally, it is also your responsibility to protect your homeworks and exams from unauthorized access. Graded homeworks will be returned to you via email, and exams as marked-up hardcopies. If you believe we have made an error in grading your homework or exam, please bring the matter to our attention within one week.
Accommodations for Students with Special Needs
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