COMP 322: Fundamentals of Parallel Programming (Spring
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2022)
InstructorInstructors: | Mackale Joyner, DH 2063 Zoran Budimlić, DH 3003 | Head TAs: | Adrienne Li, Austin Hushower, Claire Xu, Diep Hoang, Hunena Badat, Maki Yu, Mantej Singh, Rose Zhang, Victor Song, Yidi Wang | ||
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Admin Assistant: | Annepha Hurlock, annepha@rice.edu , DH 3122, 713-348-5186 | Undergraduate TAs: |
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Piazza site: | Piazza site: | https://piazza.com/class/khclqrtu2133zo (Piazza is the preferred medium for all course communications) | Cross-listing: | ELEC 323 | |
Lecture location: | Fully OnlineHerzstein Amphitheater (online 1st 2 weeks) | Lecture times: | MWF 1:30pm 00pm - 21:25pm50pm | ||
Lab locations: | Fully OnlineKeck 100 (online 1st 2 weeks) | Lab times: | Tu 1Mon 3:30pm 00pm - 23:25pm, Th 50pm (Austin, Claire) Wed 4:50pm 30pm - 5:45pm20pm (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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- Module 1 handout (Parallelism)
- Module 2 handout (Concurrency)There is no lecture handout for Module 3 (Distribution and Locality). The instructors will refer you to optional resources to supplement the lecture slides and videos.
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
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Finally, here are some additional resources that may be helpful for you:
- Slides titled "MPI-based Approaches for Java" by Bryan Carpenter
Week | Day | Date (2022 |
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Lecture Schedule
Week | Day | Date (2021) | 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 2510 | Lecture 1: Task Creation and Termination (Async, Finish) | Module 1: Section 1.1 | Topic 1.1 Lecture, Topic 1.1 DemonstrationIntroduction |
| worksheet1 | lec1-slides | worksheet1 | lec1-slides |
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| Wed | Jan 2712 | 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 |
| Functional Programming | GList.java | worksheet2 | lec02-slides |
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Fri | Jan 2914 | Lecture 3: Abstract Performance Metrics, Multiprocessor Scheduling | Module 1: Section 1.4 | Topic 1.4 Lecture, Topic 1.4 Demonstration | worksheet3 | Higher order functions | worksheet3 | lec3-slides lec3-slides |
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2 | Mon | Jan 17 | No class: MLK | |||||||||||||||||||||||||
| Wed | Jan 19 | Lecture 4: Lazy Computation | LazyList.java Lazy.java | Feb 01 | Lecture 4: Parallel Speedup and Amdahl's Law | Module 1: Section 1.5 | Topic 1.5 Lecture, Topic 1.5 Demonstration | worksheet4 | lec4-slides | Quiz for Unit 1 | WS4-solution | ||||||||||||||||
| WedFri | Feb 03Jan 21 | Lecture 5: | Future Tasks, Functional Parallelism ("Back to the Future")Java Streams | Module 1: Section 2.1 | Topic 2.1 Lecture, Topic 2.1 Demonstrationworksheet5 | lec5-slides | Homework 1 | WS5-solution | |||||||||||||||||||
3 | FriMon | Feb 05Jan 24 | Lecture 6: Finish Accumulators Map Reduce with Java Streams | Module 1: Section 2.34 | Topic 2.3 4 Lecture, Topic 2.3 4 Demonstration | worksheet6 | lec6-slides |
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| 3Wed | MonJan 26 | Feb 08 | Lecture 7: | Map ReduceFutures | Module 1: Section 2.41 | Topic 2. | 41 Lecture , Topic 2. | 41 Demonstration | worksheet7 | lec7-slidesHomework 2 |
| Homework 1 | WS7-solution | ||||||||||||||
| WedFri | Feb 10Jan 28 | Lecture 8: Data Races, Functional & Structural Determinism Computation Graphs, Ideal Parallelism | Module 1: Section Sections 1.2.5, 21.63 | Topic 1.2 .5 Lecture, Topic 1.2 .5 Demonstration, Topic 21.6 3 Lecture, Topic 21.6 3 Demonstration | worksheet8 | lec8-slides | Quiz for Unit 1 | WS8-solution | |||||||||||||||||||
4 | Mon
| Fri | Feb 12 | Jan 31 | Lecture 9: Java’s Fork/Join LibraryAsync, Finish, Data-Driven Tasks | Module 1: Sections 2Section 1. 71, 24. 85
| Topic 21. 7 Lecture1 Lecture, Topic 1.1 Demonstration, Topic 24. 8 Lecture5 Lecture, Topic 4.5 Demonstration | worksheet9 | lec9-slidesslides | Quiz for Unit 2 | WS9-solution | |||||||||||||||||
4 | MonWed | Feb | 1502 | Lecture 10: | Loop-Level Parallelism, Parallel Matrix Multiplication Event-based programming model
| Module 1: Sections 3.1, 3.2 | Topic 3.1 Lecture , Topic 3.1 Demonstration , Topic 3.2 Lecture, Topic 3.2 Demonstration | worksheet10 | lec10-slides | WS10-solution | ||||||||||||||||||
WedFri | Feb 17Spring "Sprinkle" Day (no class)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 | FriMon | Feb | 1907 | Lecture | 11: Iteration Grouping (Chunking), Barrier Synchronization12: Scheduling/executing computation graphs Abstract performance metrics | Module 1: Sections 3.3, 3Section 1.4 | Topic | 3.3 Lecture , Topic 3.3 Demonstration, Topic 3.1.4 Lecture | , | Topic | 31.4 Demonstration | worksheet11worksheet12 | lec11lec12-slides | Quiz for Unit 2 | WS12-solution | |||||||||||||
| 5 | MonWed | Feb 2209 | Lecture 12: Parallelism in Java Streams, Parallel Prefix Sums 13: Parallel Speedup, Critical Path, Amdahl's Law | Module 1: Section 31.75 | Topic Topic 3.7 Java Streams1.5 Lecture , Topic 31. 7 Java Streams5 Demonstration | worksheet12worksheet13 | lec12lec13-slides | WS13-solution | |||||||||||||||||||
| WedFri | Feb | 2411 | Lecture 13: Iterative Averaging Revisited, SPMD pattern | Module 1: Sections 3.5, 3.6 | Topic 3.5 Lecture , Topic 3.5 Demonstration , Topic 3.6 Lecture, Topic 3.6 Demonstration | worksheet13 | lec13-slides | Homework 3 (includes one intermediate checkpoint) Quiz for Unit 3 | Homework 2 |
| Fri | Feb 26 | Lecture 14: Data-Driven Tasks | Module 1: Sections 4.5 | Topic 4.5 Lecture Topic 4.5No 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 | ||||||||||||||||||||
| 6Wed | Mon | Mar 01 | Feb 16 | Lecture 15: Recursive Task Parallelism | Spring "Sprinkle" Day (no class) | worksheet15 | lec15-slides |
| WS15-solution | ||||||||||||||||||
Wed | Fri | Mar 03Feb 18 | Lecture | 15: Point-to-point Synchronization with Phasers16: Data Races, Functional & Structural Determinism | Module 1: Section 4Sections 2.5, 2, 4.36 | Topic | 42. | 25 Lecture , Topic | 42. | 25 Demonstration, Topic | 42. | 36 Lecture, | Topic 4.3Topic 2.6 Demonstration | worksheet15worksheet16 | lec15lec16-slides | Homework 3 | Homework 2 | WS16-solution | ||||||||||
7 | FriMon | Mar 05Feb 21 | 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 4 | 17: Midterm Review | lec17-slides | Quiz for Unit 3 | |||||||||||||||||
7 | MonWed | Mar 08Feb 23 | Lecture 17: Midterm Review18: Limitations of Functional parallelism. | worksheet18 | lec17lec18-slides | WS18-solution | ||||||||||||||||||||||
| WedFriMar 10 | Feb 25 | Lecture 18: Abstract vs. Real Performance | worksheet18 | 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 lec18-slides | WS20-solution | ||||||||||||||||||||
| FriWed | Mar 1202 | Lecture 19: Critical Sections, Isolated construct (start of Module 2)21: Atomic variables, Synchronized statements | Module 2: Sections 5. 14, 57.2 , 5.6, | Topic 5.1 4 Lecture, Topic 5.1 Demonstration, Topic 5.2 Lecture, Topic 5.2 Demonstration, Topic 5.6 Lecture, Topic 5.6 Demonstration | worksheet19 | lec19-slides | 4 Demonstration, Topic 7.2 Lecture | worksheet21 | lec21-slides | WS21-solutionHomework 3, Checkpoint-1 | |||||||||||||||||
| 8 | MonFri | Mar 1504 | Lecture 2022: Parallel Spanning Tree algorithm, Atomic variables , 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 57.31, 5.4, 5.57.3 | Topic 5.3 Demonstration, Topic 5.47.1 Lecture, Topic 5.4 Demonstration, Topic 5.5 Lecture, Topic 5.5 Demonstration | worksheet20 | 7.3 Lecture | worksheet23 | lec23lec20-slides |
| WS23-solution | |||||||||||||||||
| Wed | Mar 1709 | Lecture 21: Actors24: Java Locks - Soundness and progress guarantees | Module 2: 6.1, 6.27.5 | Topic 67.1 Lecture , Topic 6.1 Demonstration , Topic 6.2 Lecture, Topic 6.2 Demonstration | worksheet21 | 5 Lecture | worksheet24 | lec24lec21-slides |
| WS24-solution | |||||||||||||||||
| Fri | Mar 1911 | Lecture 22: Actors (contd) | Module 2: 6.3, 6.4, 6.5 | Topic 6.3 Lecture, Topic 6.3 Demonstration, Topic 6.4 Lecture , Topic 6.4 Demonstration, Topic 6.5 Lecture, Topic 6.5 Demonstration | worksheet22 | lec22-slides | Quiz for Unit 4 | 9 | Mon | Mar 22 | Lecture 23: Actors (contd) | Module 2: 6.6 | Topic 6.6 Lecture, Topic 6.6 Demonstration | lec23-slides | Quiz for Unit 5Lecture 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 2416 | Lecture 24: Java Threads, Java synchronized statement | Module 2: 7.1, 7.2 | Topic 7.1 Lecture, Topic 7.2 Lecture | No class: Spring Break | lec24-slides |
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| Fri | Mar | 2618 | Spring "Sprinkle" Day (no class)No class: Spring Break |
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10 | Mon | Mar 2921 | Lecture 25: Java Threads, Java synchronized statement (contd), wait/notify | Module 2: 7.1, 7.2 | Topic 7.1 Lecture, Topic 7.2 Lecture | lec25 | 26: N-Body problem, applications and implementations | worksheet26 | lec26-slides | WS26-solution | ||||||||||||||||||
| Wed | Mar 3123 | Lecture 26: Java Threads (exercise) | lec26-handout | Homework 3 (all) | 27: Read-Write Locks, Linearizability of Concurrent Objects | Module 2: 7.3, 7.4 | Topic 7.3 Lecture, Topic 7.4 Lecture | worksheet27 | lec27-slides |
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| Fri | Apr 02Mar 25 | Lecture | 27: Java Locks 28: Message-Passing programming model with Actors | Module 2: 7.3 | Topic 7.3 Lecture | lec27-slides | Quiz for Unit 6 | Quiz for Unit 5 | 6.1, 6.2 | Topic 6.1 Lecture, Topic 6.1 Demonstration, Topic 6.2 Lecture, Topic 6.2 Demonstration | worksheet28 | lec28-slides |
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11 | Mon | Apr 05Mar 28 | Lecture | 28: Linearizability of Concurrent Objects29: Active Object Pattern. Combining Actors with task parallelism | Module 2: 76.3, 6.4Topic 7 | Topic 6.3 Lecture, Topic 6.3 Demonstration, Topic 6.4 Lecture | , Topic 6.4 Demonstration | worksheet29 | lec29-slides | lec28-slides | Homework 4 (includes one intermediate checkpoint) |
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| WedApr | 07Mar 30 | Lecture 29: Java Locks (exercise)30: Task Affinity and locality. Memory hierarchy | worksheet30 | lec29lec30-handout slides |
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| Fri | Apr 0901 | Lecture 30: Safety and Liveness Properties, Java Synchronizers, Dining Philosophers Problem | Module 2: 7.5, 7.6 | Topic 7.5 Lecture, Topic 7.6 Lecture | lec30-slides | Quiz for Unit 7 | Quiz for Unit 6 | 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 1204 | Lecture 31: Message Passing Interface (MPI), (start of Module 3) | Topic 8.1 Lecture, Topic 8.2 Lecture, Topic 8.3 Lecture | 32: Barrier Synchronization with Phasers | Module 1: Section 3.4 | Topic 3.4 Lecture, Topic 3.4 Demonstration | worksheet32 | lec32lec31-slides |
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| Wed | Apr 14 | Lecture 32: Message Passing Interface (MPI, contd) | Topic 8.4 Lecture | lec32 | 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 |
| Homework 4 Checkpoint-1 | WS33-solution | ||||||||||||
| Fri | Apr 16 | Lecture 33: Message Passing Interface (MPI, contd) | Topic 8.5 Lecture, Topic 8 Demonstration Video | 08 | Lecture 34: Fuzzy Barriers with Phasers | Module 1: Section 4.1 | Topic 4.1 Lecture, Topic 4.1 Demonstration | worksheet34 | lec34lec33-slides |
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13 | Mon | Apr 1911 | Lecture 34: Task Affinity with Places35: Eureka-style Speculative Task Parallelism |
| worksheet35 | lec34lec35-slides | Quiz for Unit 8 | Quiz for Unit 7
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Wed | Apr | 2113 | Lecture | 35: Eureka-style Speculative Task Parallelism36: Scan Pattern. Parallel Prefix Sum |
| worksheet36 | lec35lec36-slides | WS36-solution | ||||||||||||||||||||
Fri | Apr | 2415 | Lecture | 3637: | Algorithms based onParallel Prefix | (Scan) operationsSum applications | worksheet37 | lec36lec37-slides | ||||||||||||||||||||
14 | Mon | Apr | 2618 | Lecture | 37: Course Review (Lectures 19-34)38: Overview of other models and frameworks | lec37lec38-slides | ||||||||||||||||||||||
Wed | Apr 28TBD20 | Lecture 39: Course Review (Lectures 19-38) | lec39-slides | Homework 4 (all) | ||||||||||||||||||||||||
Fri | Apr 30TBD22 | Lecture 40: Course Review (Lectures 19-38) | lec40-slides | Quiz for Unit 8 | Homework 5 |
Lab Schedule
Lab # | Date (20212022) | Topic | Handouts | Examples | 0 | Infrastructure Setup | lab0-handout | |||||
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1 | Jan 2610 | Infrastructure setup | lab0-handoutAsync-Finish Parallel Programming with abstract metrics lab1-handout | - | ||||||||
Feb 02 | No lab this week | 2 | Feb 09 | FuturesJan 17 | Functional Programming | lab2-handout | ||||||
3 | Feb 16 | Jan 24 | Java Streams Cutoff Strategy and Real World Performance | lab3-handout | ||||||||
4 | Feb 23Jan 31 | DDFsFutures | lab4 | -handout- | Mar 02 | No lab this week-handout | ||||||
5 | Mar 09Feb 07Loop | Data- Driven Tasks | lab5-handout lab5-introhandout | |||||||||
6 | Feb 14 | Async / Finish | lab6-handout | |||||||||
- | Mar 16Feb 21 | No lab this week ( | Spring "Sprinkle" DayMidterm) | |||||||||
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| Isolated Statement and Atomic Variables | Feb 28 | Recursive Task Cutoff Strategy | lab7-handout | - | Actors | |||||
8 | Mar 07 | Java Threads, Java Locks | lab8-handout | |||||||||
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| Message Passing Interface (MPIMar 14 | No lab this week (Spring Break) | |||||||||
9 | Mar 21 | Concurrent Lists | lab9-handout | |||||||||
10 | Mar 28 | Actors | lab10-handout | Apache Spark|||||||||
11 | Apr 04 | Loop Parallelism | lab11-handout | -|||||||||
- | Apr 11 | No lab this weekEureka-style Speculative Task Parallelism | ||||||||||
- | Java's ForkJoin Framework | Apr 18 | 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 in-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.be denied.
Labs must be submitted by the following Wednesday at 4:30pm. Labs Labs must be checked off by a TA by the following Monday at 11:59pm.
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, 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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