COMP 322: Fundamentals of Parallel Programming (Spring
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2022)
InstructorsInstructor: | Prof. Vivek SarkarMackale Joyner, DH 3131 | Head TA: | Max Grossman | ||||
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Co-Instructor: | Dr. Mackale Joyner | Graduate TAs: | Jonathan Sharman, Ryan Spring, Bing Xue, Lechen Yu | ||||
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, annepha@rice.edu , DH 3122 | Admin Assistant: | Annepha Hurlock, annepha@rice.edu, DH 3080, 713-348-5186 | Undergraduate TAs: | Marc Canby, Anna Chi, Peter Elmers, Joseph Hungate, Cary Jiang, Gloria Kim, Kevin Mullin, Victoria Nazari, Ashok Sankaran, Sujay Tadwalkar, Anant Tibrewal, Eugene Wang, Yufeng Zhou |
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Piazza site: | https://piazza.com/rice/spring2022/comp322 (Piazza is the | 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 210Amphitheater (online 1st 2 weeks) | Lecture times: | MWF 1:00pm - 1:50pm | ||||
Lab locations: | DH 1064, DH 1070Keck 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., 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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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
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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
Finally, here are some additional resources that may be helpful for you:
- Slides titled "MPI-based Approaches for Java" by Bryan Carpenter
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)
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-slides | WS4-solution | ||||
| Fri | Jan 21 | Lecture 5: Java Streams | worksheet5 | lec5-slides | Homework 1 | 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 |
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| Wed | Jan 26 | Lecture 7: Futures |
Lecture Schedule
Week | Day | Date (2017) | Lecture | Assigned Reading | Assigned Videos (see Canvas site for video links) | In-class Worksheets | Slides | Work Assigned | Work Due | 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 |
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| Wed | Jan 11 | 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 |
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Fri | Jan 13 | Lecture 3: Abstract Performance Metrics, Multiprocessor Scheduling | Module 1: Section 1.4 | Topic 1.4 Lecture, Topic 1.4 Demonstration | worksheet3 | lec3-slides |
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2 | Mon | Jan 16 | No lecture, School Holiday (Martin Luther King, Jr. Day) | |||||||||||||||||||
| Wed | Jan 18 | Lecture 4: Parallel Speedup and Amdahl's Law | Module 1: Section 1.5 | Topic 1.5 Lecture, Topic 1.5 Demonstration | worksheet4 | lec4-slides | |||||||||||||||
| Fri | Jan 20 | Lecture 5: Future Tasks, Functional Parallelism ("Back to the Future") | Module 1: Section 2.1 | Topic 2.1 Lecture , Topic 2.1 Demonstration | worksheet5worksheet7 | lec5lec7-slides |
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| Fri | Jan | 2328 | Lecture | 6: Memoization8: Computation Graphs, Ideal Parallelism | Module 1: Section Sections 1.2, 1.23 | Topic 1.2 Lecture, Topic 1.2 Demonstration, Topic 1.3 Lecture, Topic | 21. | 23 Demonstration | worksheet6worksheet8 | lec6lec8-slides | WS8-solution | ||||||||||
4 | Mon | Wed
| Jan | 2531 | Lecture | 79: | Finish AccumulatorsAsync, Finish, Data-Driven Tasks | Module 1: Section 2.3Topic 2.31.1, 4.5
| Topic 1.1 Lecture, Topic 1.1 Demonstration, Topic 4.5 Lecture, Topic | 24. | 3 5 Demonstration | worksheet7 worksheet9 | lec7-slides | Homework 1 | lec9-slides | WS9-solution | ||||||
Fri | Wed | Jan 27Feb 02 | Lecture | 8: Map Reduce10: Event-based programming model
| worksheet10 | lec10 | Module 1: Section 2.4 | Topic 2.4 Lecture, Topic 2.4 Demonstration | worksheet8 | lec8-slides | Quiz for Unit 1 | |||||||||||
4 | Mon | Jan 30 | Lecture 9: 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 | worksheet9 | lec9-slides | |||||||||||||||
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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 | |||||||||||||||
5 | Mon | Feb 07 | Lecture 12: Scheduling/executing computation graphs Abstract performance metrics | Module 1: Section 1.4 | Topic 1.4 Lecture , Topic 1.4 Demonstration | worksheet12 | lec12 |
| Wed | Feb 01 | Lecture 10: Java’s Fork/Join Library | Module 1: Sections 2.7, 2.8 | Topic 2.7 Lecture, Topic 2.8 Lecture, | worksheet10 | lec10-slides | WS12-solution | ||||||
| FriWed | Feb 0309 | Lecture 11: Loop-Level Parallelism, Parallel Matrix Multiplication, Iteration Grouping (Chunking) 13: Parallel Speedup, Critical Path, Amdahl's Law | Module 1: Sections 3.1, 3.2, 3.3Section 1.5 | Topic 1.5 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 | .5 Demonstration | worksheet13 | lec13lec11-slides | 5 | WS13-solution | |||||||||||
| FriMon | Feb 0611 | 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 | Lecture 12: Barrier Synchronization | Module 1: Section 3.4 | Topic 3.4 Lecture , Topic 3.4 Demonstration | worksheet12 | lec12-slides | WS14-solution | |||||||||
| Wed | Feb | 0816 | Lecture | 1315: Recursive Task Parallelism | Parallelism in Java Streams, Parallel Prefix Sums | worksheet13worksheet15 | lec13lec15-slides Homework 3 (includes two intermediate checkpoints) |
| Homework 2 | - | Fri | Feb 10 | Spring Recess |
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Quiz for Unit 2 | 6 | MonFri | Feb | 1318 | Lecture | 14: Iterative Averaging Revisited, SPMD pattern 16: 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 | worksheet16 | lec16-slides | Homework 3 | Homework 2 | WS16-solution | worksheet14 | ||||
7 | Mon | Feb 21 | Lecture 17: Midterm Review | lec17lec14-slides | ||||||||||||||||||
| Wed | Feb 1523 | Lecture 15: Data-Driven Tasks, Point-to-Point Synchronization with Phasers | Module 1: Sections 4.5, 4.2, 4.3 | Topic 4.5 Lecture Topic 4.5 Demonstration, Topic 4.2 Lecture , Topic 4.2 Demonstration, Topic 4.3 Lecture, Topic 4.3 Demonstration | worksheet15 | 18: Limitations of Functional parallelism. | worksheet18 | lec18lec15-slides | WS18-solution | ||||||||||||
| Fri | Feb 1725 | Lecture 16: Phasers Review | Module 1: Sections 4.2 | Topic 4.2 Lecture , Topic 4.2 Demonstration | worksheet16 | 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 | lec19lec16-slides | Quiz for Unit 3 | 7 | WS19-solution | |||||||||
8 | Mon | Feb 2028 | Lecture 17: Midterm Summary | lec17-slides | ||||||||||||||||||
| Wed | Feb 22 | Midterm Review (interactive Q&A) | Exam 1 held during lab time (7:00pm - 10:00pm), scope of exam limited to lectures 1-16 | ||||||||||||||||||
| Fri | Feb 24 | Lecture 18: Abstract vs. Real Performance | worksheet18 | lec18-slides | Homework 3, Checkpoint-1 | ||||||||||||||||
8 | Mon | Feb 27 | Lecture 19: 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, | worksheet19 | lec19-slides |
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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 |
| Wed | Mar 01 | Lecture 20: Critical sections, Isolated construct, Parallel Spanning Tree algorithm, Atomic variables (start of Module 2) | Module 2: Sections 5.1, 5.2, 5.3, 5.4, 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, Topic 5.4 Lecture, Topic 5.4 Demonstration, Topic 5.6 Lecture, Topic 5.6 Demonstration | worksheet20 | lec20-slides |
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| WedFri | Mar 0309 | Lecture 21: Read-Write Isolation, Review of Phasers24: Java Locks - Soundness and progress guarantees | Module 2: Section 57.5 | Topic 57.5 Lecture , Topic 5.5 Demonstration | worksheet21 | worksheet24 | lec24lec21-slides | Quiz for Unit 4 |
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| Fri | Mar 11 | Lecture 25: Dining Philosophers Problem | 9 | Mon | Mar 06 | Lecture 22: Actors | Module 2: 67.1, 6.2 | Topic 67.1 Lecture , Topic 6 .1 Demonstration , Topic 6.2 Lecture, Topic 6.2 Demonstration | worksheet22 | worksheet25 | lec25lec22-slides |
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Wed | Mon | Mar | 0814 | Lecture 23: Actors (contd) | Module 2: 6.3, 6.4, 6.5, 6.6 | Topic 6.3 Lecture, Topic 6.3 Demonstration, Topic 6.4 Lecture , Topic 6.4 Demonstration, Topic 6.5 Lecture, Topic 6.5 Demonstration, Topic 6.6 Lecture, Topic 6.6 Demonstration | worksheet23 | lec23-slides |
| Homework 3, Checkpoint-2No class: Spring Break |
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Wed | Mar 16 | No class: Spring Break |
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| Fri | Mar 1018 | Lecture 24: Java Threads, Java synchronized statement | Module 2: 7.1, 7.2 | Topic 7.1 Lecture, Topic 7.2 Lecture | worksheet24 | lec24-slides | Quiz for Unit 5 | No class: Spring Break | - | M-F | Mar 13 - Mar 17 | Spring Break |
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10 | Mon | Mar 2021 | Lecture 25: Java synchronized statement (contd), wait/notify | Module 2: 7.2 | Topic 7.2 Lecture | worksheet25 | 26: N-Body problem, applications and implementations | worksheet26 | lec26lec25-slides | WS26-solution | ||||||||||||
| Wed | Mar 2223 | Lecture 2627: Java Read-Write Locks, Linearizability of Concurrent Objects | Module 2: 7.3, 7.4 | Topic 7.3 Lecture, Topic 7.4 Lecture | worksheet26 worksheet27 | lec26lec27-slides |
Homework 4 (includes one intermediate checkpoint) Homework 3 (all) | WS27-solution | |||||||||||||
| Fri | Mar | 2425 | Lecture | 27: Safety and Liveness Properties, Java Synchronizers, Dining Philosophers Problem28: Message-Passing programming model with Actors | Module 2: 76.51, 76.62 | Topic | 76. | 51 Lecture, Topic | 76.1 Demonstration, Topic 6.2 Lecture | worksheet27 | lec27, Topic 6.2 Demonstration | worksheet28 | lec28-slides |
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| Quiz for Unit 6
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11 | Mon | Mar 2728 | Lecture 28: Message Passing Interface (MPI), (start of Module 3) | Topic 8.1 Lecture, Topic 8.2 Lecture, Topic 8.3 Lecture, | worksheet28 | 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 |
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| Wed | Mar 2930 | Lecture 29: Message Passing Interface (MPI, contd) | Topic 8.4 Lecture, Topic 8.5 Lecture, Topic 8 Demonstration Video | worksheet29 | 30: Task Affinity and locality. Memory hierarchy | worksheet30 | lec30lec29-slides |
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| Fri | Mar 31Apr 01 | Lecture | 3031: | Distributed Map-Reduce using Hadoop and Spark frameworksData-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 | ||||||||||
Topic 9.1 Lecture (optional, overlaps with video 2.4), Topic 9.2 Lecture, Topic 9.3 Lecture | worksheet30 | lec30-slides | Quiz for Unit 7 | 12 | Mon | Apr 0304 | Lecture 31: TF-IDF and PageRank Algorithms with Map-Reduce | 32: Barrier Synchronization with Phasers | Module 1: Section 3.4 | Topic 3Topic 9.4 Lecture, Topic 9.5 Lecture, Unit 9 Topic 3.4 Demonstration | worksheet31 worksheet32 | lec31lec32-slides |
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| Wed | Apr 0506 | Lecture 32: Combining Distribution and Multithreading | Lectures 10.1 - 10.5, Unit 10 Demonstration (all videos optional – unit 10 has no quiz) | 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 |
| WS33-solution | Homework 4 Checkpoint-1 | |||||||
| Fri | Apr 0708 | Lecture 33: Partitioned Global Address Space (PGAS) programming models | worksheet33 | lec33-slides |
| 34: Fuzzy Barriers with Phasers | Module 1: Section 4.1 | Topic 4.1 Lecture, Topic 4.1 Demonstration | worksheet34 | lec34-slides |
| WS34-solution | Quiz for Unit 8 | ||||||||
13 | Mon | Apr 1011 | Lecture 34: Task Affinity with Places35: Eureka-style Speculative Task Parallelism |
| worksheet34 worksheet35 | lec34lec35-slides |
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Wed | Apr | 1213 | Lecture | 35: Eureka-style Speculative Task Parallelism36: Scan Pattern. Parallel Prefix Sum |
| worksheet35worksheet36 | lec35lec36-slides | Homework 5 (Due April 21st, with automatic extension until May 1st after which slip days may be used) | Homework 4 (all)WS36-solution | |||||||||||||
Fri | Apr | 1415 | Lecture | 3637: | Algorithms based onParallel Prefix | (Scan) operationsSum applications | worksheet36worksheet37 | lec36lec37-slides | Quiz for Unit 9 | |||||||||||||
14 | Mon | Apr | 1718 | Lecture | 37: GPU Computing38: Overview of other models and frameworks | worksheet37 | lec37lec38-slides | |||||||||||||||
Wed | Apr | 1920 | Lecture | 3839: Course | SummaryReview (Lectures | 1819- | 3738) | lec39-slides | lec38-slides | |||||||||||||
Fri | Apr | 2122 | Lecture | 3940: Course Review ( | interactive Q&A), Last day of classesLectures 19-38) | lec40-slides | Homework 5 | (automatic extension until May 1st, after which slip days may be used)- | Mon | Apr 24 | Review session / Office Hours, 1pm - 3pm, location TBD |
Lab Schedule
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Tue
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May 2
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9am - 12noon, scheduled final exam (Exam 2 – scope of exam limited to lectures 18 - 38), location TBD by registrar
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Lab # | Date (2022) | Topic | Handouts | Examples |
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1 | Jan 10 | Infrastructure setup | lab0-handout lab1-handout | |
2 | Jan 17 | Functional Programming | lab2-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 | lab9-handout | |
10 | Mar 28 | Actors | lab10-handout | |
11 | Apr 04 | Loop Parallelism | lab11-handout | |
- | Apr 11 | No lab this week | ||
- | Apr 18 | No lab this week |
Grading, Honor Code
Lab Schedule
Lab # | Date (2017) | Topic | Handouts | Code Examples |
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0 | Infrastructure Setup | lab0-handout | - | |
1 | Jan 11 | Async-Finish Parallel Programming with abstract metrics | lab1-handout, lab1-slides | lab_1.zip |
2 | Jan 18 | Futures and HJ-Viz | lab2-handout, lab2-slides | lab_2.zip |
3 | Jan 25 | Cutoff Strategy and Real World Performance | lab3-handout, lab3-slides | lab_3.zip |
4 | Feb 01 | Java's ForkJoin Framework | lab4-handout, lab4-slides | lab_4.zip |
5 | Feb 08 | Loop-level Parallelism | lab5-handout, lab5-slides | lab_5.zip |
6 | Feb 15 | Phasers | lab6-handout | lab_6.zip |
- | Feb 22 | No lab this week — Exam 1 | - | - |
7 | Mar 01 | Isolated Statement and Atomic Variables | lab7-handout, lab7-slides | |
8 | Mar 08 | Actors | lab8-handout | |
- | Mar 15 | No lab this week — Spring Break | ||
9 | Mar 22 | Java Threads, Java Locks | lab9-handout | |
- | Mar 29 | No lab this week — Willy Week! | ||
10 | Apr 05 | Message Passing Interface (MPI) | lab10-handout | |
11 | Apr 12 | Apache Spark | lab11-handout | |
12 | Apr 19 | Eureka-style Speculative Task Parallelism | lab12-handout |
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 class worksheets (weighted 5% in all).
The purpose of the homeworks homework is to 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. 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 automatically tracked through the Autograder, more details are available later in this document and in the Autograder user guideusing 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 prior to the start of the lab the following week.
Worksheets should be completed in class for full credit. For partial credit, a worksheet can be turned in before the start of the class following the one in which the worksheet for 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. 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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