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 |
Lecture Schedule
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Week | Day | Date (2017) | Lecture | Assigned Reading | Assigned Videos (see Canvas site for video links) | 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 |
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Fri | Jan | 1114 | Lecture | 23: | Computation Graphs, Ideal ParallelismHigher order functions | worksheet3 | lec3-slides |
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2 | Mon | Jan 17 | No class: MLK | 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 | |||||||||||||||||||
| FriWed | Jan | 1319 | Lecture | 3: Abstract Performance Metrics, Multiprocessor Scheduling4: Lazy Computation | LazyList.java Lazy.java | worksheet4 | lec4 | Module 1: Section 1.4 | Topic 1.4 Lecture, Topic 1.4 Demonstration | worksheet3 | lec3-slides | 2 | WS4-solution | |||||||||||||
| Fri | MonJan | 16No lecture, School Holiday (Martin Luther King, Jr. Day)21 | Lecture 5: Java Streams | worksheet5 | lec5-slides | Homework 1 |
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3 | Mon | WedJan | 1824 | Lecture | 4: Parallel Speedup and Amdahl's Law6: Map Reduce with Java Streams | Module 1: Section 12.54 | Topic | 12. | 54 Lecture, Topic | 12. | 54 Demonstration | worksheet4worksheet6 | lec4lec6-slides |
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| FriWed | Jan 2026 | Lecture 5: Future Tasks, Functional Parallelism ("Back to the Future")7: Futures | Module 1: Section Module 1: Section 2.1 | Topic 2.1 Lecture , Topic 2.1 Demonstration | worksheet5worksheet7 | lec5lec7-slides |
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| MonFri | 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
| WedJan | 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 | lec7lec9-slides | Homework 1 | slides | Fri | Jan 27 | Lecture 8: Map Reduce | 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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Wed | Feb 02 | Lecture 10: Event-based programming model
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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.Section 1, 3.2, 3.3.5 | Topic 31.1 5 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 | WS13-solution | |||||||||||||||||
| Fri | Feb 11 | No class: Spring Recess
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6 | 5 | Mon | Feb | 0614 | Lecture | 12: Barrier Synchronization 14: Accumulation and reduction. Finish accumulators | Module 1: Section 2.3.4 | Topic 2.3 | .4Lecture | ,Topic 2.3 | .4Demonstration | worksheet12 worksheet14 | lec12lec14-slides | WS14-solution | |||||||||||||
| Wed | Feb | 0816 | Lecture | 1315: | Parallelism in Java Streams, Parallel Prefix SumsRecursive Task Parallelism | 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 | worksheet14 | worksheet16 | lec14lec16-slides | Homework 3 | Homework 2 | WS16-solution | |||||||||
7 | WedMon | Feb 1521 | Lecture 1517: 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 | lec15-slidesMidterm Review | lec17-slides | |||||||||||||||||||
| FriWed | Feb 1723 | Lecture 16: Phasers Review | Module 1: Sections 4.2 | Topic 4.2 Lecture , Topic 4.2 Demonstration | worksheet16 | lec16-slides | Quiz for Unit 3 | 7 | Mon | Feb 20 | Lecture 17: Midterm Summary | lec17-slides | 18: Limitations of Functional parallelism. | worksheet18 | lec18-slides | WS18-solution | ||||||||||
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| WedFri | 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 | |||||||||||||||||||||
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-slides | WS19-solution | ||||||||||||||||||||||
8 | Mon | Feb 28 | 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 | 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 | |||||||||||
| Wed | Mar 0102 | Lecture 20: Critical sections, Isolated construct, Parallel Spanning Tree algorithm, Atomic variables (start of Module 2)21: Atomic variables, Synchronized statements | Module 2: Sections 5. 14, 57.2 , 5.3, 5.4, 5.6 | Topic 5.1 4 Lecture, Topic 5.1 4 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-slides7.2 Lecture | worksheet21 | lec21-slides | WS21-solution | |||||||||||||||||
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| Fri | Mar 0304 | Lecture 21: Read-Write Isolation, Review of Phasers | Module 2: Section 5.5 | Topic 5.5 Lecture, Topic 5.5 Demonstration | worksheet21 | lec21-slides | 22: Parallel Spanning Tree, other graph algorithms | worksheet22 | lec22-slides | Homework 4 | Homework 3 | WS22-solution | Quiz for Unit 4 | ||||||||||||
9 | Mon | Mar 0607 | Lecture 22: Actors23: Java Threads and Locks | Module 2: 6Sections 7.1, 67.23 | Topic 67.1 Lecture, Topic 6.1 Demonstration , Topic 6.2 Lecture, Topic 6.2 Demonstration | worksheet22 | 7.3 Lecture | worksheet23 | lec23lec22-slides |
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| Wed | Mar 0809 | Lecture 23: Actors (contd)24: Java Locks - Soundness and progress guarantees | 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-2 | ||||||||||||||||||
| Fri | Mar 10 | 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 | |||||||||||||||||||
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 | - | M-F | Mar 13 - Mar 17 | Spring Break |
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10 | Wed | Mon | Mar | 2016 | Lecture 25: Java synchronized statement (contd), wait/notify | Module 2: 7.2 | Topic 7.2 Lecture | worksheet25 | No class: Spring Break | lec25-slides | |||||||||||||||||
| WedFri | Mar 2218 | No class: Spring Break |
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10 | Mon | Mar 21 | Lecture 26: N-Body problem, applications and implementations | Lecture 26: Java Locks, Linearizability of Concurrent Objects | Module 2: 7.3, 7.4 | Topic 7.3 Lecture, Topic 7.4 Lecture | worksheet26 | lec26-slides | WS26-solution | Homework 3 (all) | |||||||||||||||||
| FriWed | Mar 2423 | Lecture 27: Safety and Liveness Properties, Java Synchronizers, Dining Philosophers Problem Read-Write Locks, Linearizability of Concurrent Objects | Module 2: 7.53, 7.64 | Topic 7.5 3 Lecture, Topic 7.6 4 Lecture | worksheet27 | lec27-slides |
| Quiz for Unit 6 | 11 | Mon | Mar 27WS27-solution | |||||||||||||||
| Fri | Mar 25 | Lecture 28: Message | Passing Interface (MPI), (start of Module 3)-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 8.1 Lecture, Topic 8.2 Lecture, Topic 8.3 Lecture, | worksheet28 | lec28-slides |
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11 | MonWed | Mar 2928 | Lecture 29: Message Passing Interface (MPI, contd) | 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 DemonstrationTopic 8.4 Lecture, Topic 8.5 Lecture, Topic 8 Demonstration Video | worksheet29 | lec29-slides |
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| FriWed | Mar | 3130 | Lecture 30: | Distributed Map-Reduce using Hadoop and Spark frameworksTask Affinity and locality. Memory hierarchy | 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 | 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 03 | Lecture 31: TF-IDF and PageRank Algorithms with Map-Reduce | Topic 9.4 Lecture, Topic 9.5 Lecture, Unit 9 Demonstration | worksheet31 | lec31-slides | Homework 5 | Homework 4 | WS31-solution | ||||||||
12 | WedMon | Apr 0504 | Lecture 32: Combining Distribution and Multithreading | Barrier Synchronization with Phasers | Module 1: Section 3.4 | Topic 3.4 Lecture, Topic 3.4 DemonstrationLectures 10.1 - 10.5, Unit 10 Demonstration (all videos optional – unit 10 has no quiz) | worksheet32 | lec32-slides | Homework 4 Checkpoint-1 |
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| FriWed | Apr 0706 | Lecture 33: Partitioned Global Address Space (PGAS) programming models | 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 |
| Quiz for Unit 8 | WS33-solution | ||||||||||||||||
| Fri | Apr 08 | 13 | Mon | Apr 10 | Lecture 34: Task Affinity with Places | 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 | WedMon | Apr 1211 | Lecture 35: Eureka-style Speculative Task Parallelism |
| worksheet35 | lec35-slides | Homework 5 (Due April 21st, with automatic extension until May 1st after which slip days may be used) |
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Fri | Wed | Apr | 1413 | Lecture 36: | Algorithms based onScan Pattern. Parallel Prefix | (Scan) operationsSum |
| worksheet36 | lec36-slides | Quiz for Unit 9 | 14 | Mon | WS36-solution | ||||||||||||||
Fri | Apr 15 | Apr 17Lecture 37: | GPU ComputingParallel Prefix Sum applications | worksheet37 | lec37-slides | ||||||||||||||||||||||
14 | WedMon | Apr | 1918 | Lecture 38: | Course Summary (Lectures 18-37)Overview of other models and frameworks | lec38-slides | |||||||||||||||||||||
Wed | FriApr | 2120 | Lecture 39: Course Review ( | interactive Q&A), Last day of classesLectures 19-38) | lec39-slides | Homework 5 (automatic extension until May 1st, after which slip days may be used) | |||||||||||||||||||||
Fri | Apr 22 | Lecture 40: Course Review (Lectures 19-38) | - | Mon | Apr 24 | Review session / Office Hours, 1pm - 3pm, location TBD | lec40-slides | Homework 5 | - |
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 |
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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