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 3131 | Head TA: | Max Grossman |
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Admin Assistant: | Annepha Hurlock, annepha@rice.edu , DH | 30803122, 713-348-5186 | Graduate TAs: | Jonathan Sharman, Ryan Spring, Bing Xue, Lechen Yu | |||
Co-Instructor: | Dr. Mackale Joyner | Undergraduate TAs: | Marc Canby, Anna Chi, Peter Elmers, Joseph Hungate, Cary Jiang, Gloria Kim, Kevin Mullin, Victoria Nazari, Ashok Sankaran, Sujay Tadwalkar, Anant Tibrewal, Vidhi Vakharia, Eugene Wang, Yufeng Zhou | ||||
Piazza site: | https://piazza.com/class/ixdqx0x3bjl6en (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 210 | Lecture times: | MWF 1:00pm - 1:50pm (followed by group office hours during 2pm - 3pm, usually in DH 3092) | ||||
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Piazza site: | https://piazza.com/rice/spring2022/comp322 (Piazza is the preferred medium for all course communications) | Cross-listing: | ELEC 323 | ||||
Lecture location: | Herzstein Amphitheater (online 1st 2 weeks) | Lecture times: | MWF 1:00pm - 1:50pm | ||||
Lab locations: | Keck 100 (online 1st 2 weeks) | Lab times: | Mon 3:00pm - 3:50pm (Austin, Claire) Wed 4:30pm - 5:20pm (Hunena, Mantej, Yidi, Victor, Rose, Adrienne, Diep, Maki) | Lab locations: | DH 1042, DH 1064 | Lab times: | Wednesday, 07:00pm - 08:30pm |
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., 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 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 Canvas are included below:
- Module 1 handout (Parallelism)
- Module 2 handout (Concurrency)
- Module 3 handout (Distribution and Locality)
You 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 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
- Spring 2016 (Rice University)
- Spring 2015 (Rice University)
- Spring 2014 (Rice University)
- Spring 2013 (Rice University)
- Fall 2012 (Harvey Mudd College CS 181E, half-semester class, co-instructor: Prof. Ran Libeskind-Hadas)
- Spring 2012 (Rice University)
- Spring 2011 (Rice University)
- Fall 2009 (Rice University)
- 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 (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 | ||||||||
| Wed | Jan 19 | Lecture 4: Lazy Computation | LazyList.java Lazy.java | worksheet4 | lec4 |
Lecture Schedule
Week | Day | Date (2017) | Lecture | Assigned Reading | Assigned Videos | In-class Worksheets | Slides | Work Assigned | Work Due | ||||||||||||
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1 | Mon | Jan 09 | Lecture 1: Task Creation and Termination (Async, Finish) | Module 1: Section 1.1 | Topic 1.1 Lecture, Topic 1.1 Demonstrationworksheet1 | lec1-slides | WS4-solution | ||||||||||||||
| WedFri | Jan | 1121 | Lecture 5: Java Streams | worksheet5 | lec5 | 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 | Homework 1 | WS5-solution | ||||||||
3 | FriMon | Jan 1324 | Lecture 3: Abstract Performance Metrics, Multiprocessor Scheduling6: Map Reduce with Java Streams | Module 1: Section 12.4 | Topic 12.4 Lecture, Topic 12.4 Demonstration | worksheet3worksheet6 | lec3lec6-slides |
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| Wed | Jan 26 | Lecture 7: Futures | Module 1: Section 2.1 | Topic 2.1 Lecture , Topic 2.1 Demonstration | worksheet7 | lec7-slides | 2 | Mon | Jan 16 | No lecture, School Holiday (Martin Luther King, Jr. Day) |
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| WedFri | Jan 1828 | Lecture 48: Parallel Speedup and Amdahl's LawComputation Graphs, Ideal Parallelism | Module 1: Section Sections 1.2, 1.53 | Topic 1.5 2 Lecture, Topic 1.2 Demonstration, Topic 1.3 Lecture, Topic 1.5 3 Demonstration | worksheet4worksheet8 | lec4lec8-slides | WS8-solution | ||||||||||||
4 | Mon Fri | Jan 2031 | Lecture 5: Future Tasks, Functional Parallelism9: Async, Finish, Data-Driven Tasks | Module 1: Section 21.1, 4.5
| Topic 21.1 Lecture, Topic 21.1 Demonstration, Topic 4.5 Lecture, Topic 4.5 Demonstration | worksheet5 worksheet9 | lec5lec9-slidesslides | WS9-solution | |||||||||||||
MonWed | Jan 23Feb 02 | Lecture 6: Memoization | Module 1: Section 2.2 | Topic 2.2 Lecture , Topic 2.2 Demonstration | worksheet6 | lec6-slides | 10: Event-based programming model
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WedFri | Jan 25Feb 04 | Lecture 7: Finish Accumulators | Module 1: Section 2.3 | Topic 2.3 Lecture , Topic 2.3 Demonstration | worksheet7 | 11: GUI programming as an example of event-based, futures/callbacks in GUI programming | worksheet11 | lec11lec7-slides | Homework 2 | Homework 1 | WS11-solution | ||||||||||
5 | FriMon | Jan 27Feb 07 | Lecture | 8: Data Races, Functional & Structural Determinism12: Scheduling/executing computation graphs Abstract performance metrics | 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 |
| Quiz for Unit 1 | Section 1.4 | Topic 1.4 Lecture , Topic 1.4 Demonstration | worksheet12 | lec12-slides | WS12-solution | ||||||
| Wed | Feb 09 | Lecture 13: Parallel Speedup, Critical Path, Amdahl's Law | 4 | Mon | Jan 30 | Lecture 9: Map Reduce | Module 1: Section 21.45 | Topic 21. 45 Lecture , Topic 21. 45 Demonstration | worksheet9worksheet13 | lec9lec13-slides | WS13-solution | |||||||||
| WedFri | Feb 0111 | Lecture 10: Java’s Fork/Join Library | FJP chapter: Sections 7.3 & 7.5No class: Spring Recess
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6 | FriMon | Feb | 03Lecture 11: Loop-Level Parallelism, Parallel Matrix Multiplication, Iteration Grouping (Chunking) 14 | Lecture 14: Accumulation and reduction. Finish accumulators | Module 1: Sections 3.1, 3.2, 3Section 2.3 | Topic | 3.1 Lecture , Topic 3.1 Demonstration , Topic 3.2 Lecture, Topic 3.2 Demonstration, Topic 3.3 Lecture , Topic 3.3 Demonstration2.3 Lecture Topic 2.3 Demonstration | worksheet14 | lec14-slides | WS14-solution | worksheet11 | lec11-slides | |||||||||
5 | Wed | Mon | Feb 0616 | Lecture 1215: Recursive Task Parallelism Barrier Synchronization | Module 1: Section 3.4 | Topic 3.4 Lecture , Topic 3.4 Demonstration | worksheet12 | worksheet15 | lec15 lec12-slides |
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FriWed | Feb 0818 | 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 | Homework 3 (includes two intermediate checkpoints) | Homework 2 | WS16-solution | Fri||||||||||||
7 | Spring Recess Mon | Feb 21 | Lecture 17: Midterm Review | Quiz for Unit 2 | 6 | Mon | Feb 13 | Lecture 14: Data-Driven Tasks and Data-Driven Futures | Module 1: Section 4.5 | Topic 4.5 Lecture , Topic 4.5 Demonstration | worksheet14 | lec14-slides | lec17-slides | ||||||||
| Wed | Feb 23 | Lecture 18: Limitations of Functional parallelism. | worksheet18 | lec18-slides | WS18-solution | |||||||||||||||
| WedFri | Feb 1525 | Lecture 15: Phasers, Point-to-point Synchronization | Module 1: Sections 4.2, 4.3 | Topic 4.2 Lecture , Topic 4.2 Demonstration, Topic 4.3 Lecture, Topic 4.3 Demonstration | worksheet15 | 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 lec15-slides | WS19-solution | ||||||||||
8 | MonFri | 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 | Quiz for Unit 3 | |||||||||||||
7 | Mon | Feb 20 | Lecture 17: Midterm Summary | lec18-slides | |||||||||||||||||
| Wed | Feb 22 | Midterm Review (interactive Q&A, no lecture) | Exam 1 held during lab time (7:00pm - 10:00pm), scope of exam limited to lectures 1-17 | Homework 3, Checkpoint-1 | ||||||||||||||||
| Fri | Feb 24 | Lecture 18: Abstract vs. Real Performance | worksheet17 | lec17-slides | Quiz for Unit 4 | |||||||||||||||
8 | Mon | Feb 27 | Lecture 19: Task Scheduling Policies | worksheet19 | lec19-slides |
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| Wed | Mar 01 | Lecture 20: Critical sections, Isolated construct, Parallel Spanning Tree algorithm (start of Module 2) | 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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| Fri | Mar 03 | 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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9 | Mon | Mar 06 | Lecture 22: Parallelism in Java Streams, Parallel Prefix Sums
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| Wed | Mar 08 | Lecture 23: Java Threads, Java synchronized statement | Topic 7.1 Lecture, Topic 7.2 Lecture | worksheet23 | lec23-slides |
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| Fri | Mar 10 | Lecture 24: Java synchronized statement (contd), wait/notify | Topic 7.3 Lecture | worksheet24 | lec24-slides | Quiz for Unit 5 | ||||||||||||||
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 | ||||||||||||||||
| 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 | Lecture 22: Parallel Spanning Tree, other graph algorithms | 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 |
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| Fri | Mar 18 | No class: Spring Break | - | M-F | Mar 13 - Mar 17 | Spring Break |
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10 | Mon | Mar 2021 | Lecture 25: Concurrent Objects, Linearizability of Concurrent Objects26: N-Body problem, applications and implementations | Topic 7.4 Lecture | worksheet25 | worksheet26 | lec26 lec25-slides | WS26-solution | |||||||||||||
| Wed | Mar 2223 | Lecture 26: Linearizability (contd), Java locks27: Read-Write Locks, Linearizability of Concurrent Objects | Module 2: 7.3, 7.4 | Topic 7.3 Lecture (recap), Topic 7.4 Lecture (recap) | worksheet26 worksheet27 | lec26lec27-slides | Homework 3 (all) |
| Fri | Mar 24 | Lecture 27: Parallel Design Patterns, Safety and Liveness Properties | Topic 7.5 Lecture | worksheet27 | WS27-solution lec27-slides | ||||||
| 11 | MonFri | Mar | 2725 | 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, Topic 6.3 Lecture, Topic 6.3Demonstration | worksheet28 | lec28-slides |
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11 | MonWed | Mar 2928 | Lecture 29: Actors (contd) | 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 | worksheet29 | lec29-slides |
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| FriWed | Mar | 3130 | Lecture 30: | Java Synchronizers, Dining Philosophers ProblemTask Affinity and locality. Memory hierarchy | Topic 7.6 Lecture | worksheet30 | lec30-slides |
| 12 | Mon | Apr 03 | Lecture 31: Eureka-style Speculative Task Parallelism WS30-solution | ||||||||
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| Wed | Apr 05 | Lecture 32: Task Affinity with Places (start of Module 3) | worksheet32 | lec32-slides |
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| Fri | Apr 07 | Lecture 33: Message Passing Interface (MPI) | worksheet33 | lec33-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 | 13 | Mon | Apr 10 | Lecture 34: Message Passing Interface (MPI, contd) | worksheet34 | lec34-slides |
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| Wed | Apr 12 | Lecture 35: GPU ComputingWS32-solution | |||
| worksheet35 | lec35-slides | Homework 5 (Due April 21st, with automatic extension until May 1st after which slip days may be used) | Wed | Apr 06 | 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 | worksheet33 | lec33-slides |
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| Fri | Apr 1408 | Lecture 36: Partitioned Global Address Space (PGAS) programming models | worksheet36 | lec3634: Fuzzy Barriers with Phasers | Module 1: Section 4.1 | Topic 4.1 Lecture, Topic 4.1 Demonstration | worksheet34 | lec34-slides |
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1314 | Mon | Apr 1711 | Lecture 37: Apache Spark framework35: Eureka-style Speculative Task Parallelism |
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Wed | Apr | 1913 | Lecture | 38: Topic TBD36: Scan Pattern. Parallel Prefix Sum |
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Fri | Apr 15 | Lecture 37: Parallel Prefix Sum applications | Fri | Apr 21 | Lecture 39: Course Review (lectures 20-37), Last day of classesworksheet37 | lec37-slides | lec38-slides | Homework 5 (automatic extension until May 1st, after which slip days may be used) | |||||||||||||
-14 | Mon | Apr 24Review session / Office Hours, 1pm - 3pm, location TBD18 | Lecture 38: Overview of other models and frameworks | lec38-slides | |||||||||||||||||
- | Wed | Apr 2620 | Review session / Office Hours, 1pm - 3pm, location TBDLecture 39: Course Review (Lectures 19-38) | - | Fri | Apr 28 | Review session / Office Hours, 1pm - 3pm, location TBD | lec39-slides | |||||||||||||
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| April 26 - May 3 | Scheduled final exam (Exam 2 – scope of exam limited to lectures 18-37), location and time TBD by registrarFri | Apr 22 | Lecture 40: Course Review (Lectures 19-38) | lec40-slides | Homework 5 |
Lab Schedule
Lab # | Date (20172022) | Topic | Handouts | Code Examples | ||||
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1 | Jan 1110 | Infrastructure setup | lab0-handoutAsync-Finish Parallel Programming with abstract metrics lab1-handout , lab1-slides | lab_1.zip | ||||
2 | Jan | 1817 | TBDFunctional Programming | lab2-handout | , lab2-slides||||
3 | Jan 2524 | Java Streams DIY HJ-lib Programming, Futures | lab3-handout, lab3-slides | lab_3.zip | ||||
4 | Feb 01 | Finish Accumulators and Loop-Level ParallelismJan 31 | Futures | lab4-handout | , lab4-slides lab_4.zip||||
5 | Feb 08Loop Chunking and Barrier Synchronization07 | Data-Driven Tasks | lab5-handout, lab5-slides | lab_5.zip | ||||
6 | Feb 1514 | Async / FinishData-Driven Futures and Phasers | lab6-handout | lab_6.zip | ||||
- | Feb | 2221 | No lab this week | — Exam 1(Midterm) | - | -|||
7 | Mar 01 | Isolated Statement and Atomic VariablesFeb 28 | Recursive Task Cutoff Strategy | lab7-handout | ||||
8 | Mar | 0807 | Java Threads | lab8-handout | ||||
- | Mar 1514 | No lab this week — (Spring Break) | ||||||
9 | Mar 2221 | Java LocksConcurrent Lists | lab9-handout | |||||
10 | Mar | 2928 | Actors | and Selectorslab10-handout | ||||
11 | Apr | 05Eureka-style Speculative Task 04 | Loop Parallelism | lab11-handout | ||||
12- | Apr 12 | Message Passing Interface (MPI) | 11 | No lab this week | lab12-handout | |||
- | Apr | 19Apache Spark | 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), online quizzes (weighted 5% in all), and class participation including in-class Q&A, worksheets, Piazza participation 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. 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.).). 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. 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 checked off by a TA prior to the start of the lab the following week.
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 Worksheets are due by the beginning of the class after they are distributed, 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. 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).
- HomeworksHomework: All submitted homeworks are 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 closedopen-book, closedopen-notes, and closedopen-computer individual written test, which must be completed within a specified time limit. No class notes or external materials may be consulted when taking the exams.
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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 codeGraded 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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