COMP 322: Fundamentals of Parallel Programming (Spring
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2023)
Instructor: | Mackale Joyner, DH 2063 | Head TAs: | ||
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Admin Assistant: | Annepha Hurlock, annepha@rice.edu, DH 3122, 713-348-5186 | Undergraduate TAs: | ||
Mohamed Abead, Chase Hartsell, Taha Hasan, Harrison Huang, Jerry Jiang, Jasmine Lee, Michelle Lee, Hung Nguyen, Quang Nguyen, Ryan Ramos, Oscar Reynozo, Delaney Schultz, Tina Wen, Raiyan Zannat, Kailin Zhang | ||||
Piazza site: | Piazza site: | https://piazza.com/configure-classesrice/spring2021spring2022/comp322 (Piazza is the preferred medium for all course communications) | Cross-listing: | ELEC 323 |
Lecture location: | Fully OnlineTBD | Lecture times: | MWF 1:30pm 00pm - 21:25pm50pm | |
Lab locations: | Fully OnlineTBD | Lab times: | Tu 1Mon 3:30pm 00pm - 2:25pm, Th 3:50pm () Tue 4:50pm 00pm - 54:45pm50pm () |
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:
- 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
Finally, here are some additional resources that may be helpful for you:
- Slides titled "MPI-based Approaches for Java" by Bryan Carpenter
Lecture Schedule
WeekWeek | Day | Date (20202022) | 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 1309 | 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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| WS1-solution | |||||||||||||||||
| Wed | Jan 1511 | 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 1Functional Programming | GList.java | worksheet2 | lec02-slides |
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| WS2-solution | ||||||||||||||||
Fri | Jan 1713 | 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 |
| WS3-solution | ||||||||||||||||||||
2 | Mon | Jan 2016 | No lecture, School Holiday (Martin Luther King, Jr. Day)class: MLK | |||||||||||||||||||||||||||
| Wed | Jan 2218 | Lecture 4: Parallel Speedup and Amdahl's Law | Module 1: Section 1.5 | Lazy Computation | LazyList.java Lazy.java | Topic 1.5 Lecture, Topic 1.5 Demonstration | worksheet4 | lec4-slides | Quiz for Unit 1 | WS4-solution | |||||||||||||||||||
| Fri | Jan | 2420 | 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 | Mon | Jan 2723 | 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 |
| WS6-solution | |||||||||||||||||||||
| Wed | Jan | 2925 | 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 | ||||||||||||||||
| Fri | Jan 3127 | 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 Feb 03 | Jan 30 | Lecture 9: Java’s Fork/Join LibraryAsync, Finish, Data-Driven Tasks | Module 1: Sections 2Section 1. 71, 24. 85
| Topic 21. 71 Lecture, Topic 2.8 Lecture1.1 Demonstration, Topic 4.5 Lecture, Topic 4.5 Demonstration | worksheet9 | lec9-slidesslides | Quiz for Unit 2 | WS9-solution | |||||||||||||||||||||
Wed | Feb | 0501 | 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 | Homework 1 | WS10-solution | ||||||||||||||||||||
Fri | Feb | 0703 | Lecture 11: | Iteration Grouping (Chunking), Barrier SynchronizationModule 1: Sections 3.3, 3.4 | Topic 3.3 Lecture , Topic 3.3 Demonstration, Topic 3.4 Lecture , Topic 3.4 Demonstration GUI programming as an example of event-based, futures/callbacks in GUI programming | worksheet11 | lec11-slides | Homework 2 | WS11-solution | |||||||||||||||||||||
5 | Mon | Feb | 1006 | Lecture 12: | Parallelism in Java Streams, Parallel Prefix Sums Scheduling/executing computation graphs Abstract performance metrics | Module 1: Section 31.74 | Topic | Topic 3.7 Java Streams1.4 Lecture , Topic | 31. | 7 Java Streams4 Demonstration | worksheet12 | lec12-slides | Quiz for Unit 2 | WS12-solution | ||||||||||||||||
| Wed | Feb | 1208 | Lecture 13: | Iterative Averaging Revisited, SPMD pattern Parallel Speedup, Critical Path, Amdahl's Law | Module 1: Sections 3Section 1.5, 3.6 | Topic | 31.5 Lecture , Topic | 31.5 Demonstration | , Topic 3.6 Lecture, Topic 3.6 Demonstrationworksheet13 | lec13-slides | Homework 3 (includes 2 intermediate checkpoints) Quiz for Unit 3 | Homework 2WS13-solution | |||||||||||||||||
| - | Fri | Feb 1410 | No class: Spring Recess
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6 | Mon | Feb | 1713 | Lecture 14: | Data-Driven Tasks Accumulation and reduction. Finish accumulators | Module 1: Sections 4Section 2.53 | Topic | 42. | 53 Lecture Topic | 42. | 53 Demonstration | worksheet14 | lec14-slides | WS14-solution | ||||||||||||||||
| Wed | Feb 1915 | Lecture 15: Recursive Task Parallelism | 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 | worksheet15 | lec15-slides |
| WS15-solution | ||||||||||||||||||||
Fri | Feb | 2117 | Lecture 16: | Pipeline Parallelism, Signal Statement, Fuzzy BarriersData Races, Functional & Structural Determinism | Module 1: Sections 42.45, 42.16 | Topic | 42. | 45 Lecture , Topic | 42. | 45 Demonstration, Topic | 42. | 16 Lecture, | Topic 4.1Topic 2.6 Demonstration | worksheet16 | lec16-slides | Quiz for Unit 4 | Quiz for Unit 3 | Homework 3 | Homework 2 | WS16-solution | ||||||||||
7 | Mon | Feb 2420 | Lecture 17: Midterm Midterm Review | lec17-slides | ||||||||||||||||||||||||||
| Wed | Feb 2622 | Lecture 18: Limitations of Functional parallelism. | worksheet18 | lec18lec18-slides | WS18-solution | ||||||||||||||||||||||||
| Fri | Feb 2824 | Lecture 19: Critical Sections, Isolated construct (start of Module 2) | 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 | worksheet19 | lec19-slides | Homework 3, Checkpoint-1 | 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 | MonMar | 02Feb 27 | Lecture 20: Parallel Spanning Tree algorithm, Atomic variables Confinement & Monitor Pattern. Critical sections | Module 2: Sections 5.31, 5.42, 5.56 | Topic 5.1 Lecture, Topic 5.3 1 Demonstration, Topic 5.4 2 Lecture, Topic 5.4 2 Demonstration, Topic 5.5 6 Lecture, Topic 5.5 6 Demonstration | worksheet20 | lec20-slides | WS20-solution | ||||||||||||||||||||||
| Wed | Mar 0401 | Lecture 21: Actors Atomic variables, Synchronized statements | Module 2: 6Sections 5. 14, 67.2 | Topic 65.1 4 Lecture, Topic 65.1 4 Demonstration, Topic 67.2 Lecture, Topic 6.2 Demonstration | worksheet21 | lec21-slides | WS21-solution | ||||||||||||||||||||||
| Fri | Mar 0603 | Lecture 22: Actors (contd) | Module 2: 6.3, 6.4, 6.5 | Parallel Spanning Tree, other graph algorithms | 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 | Homework 4 | Homework 3 | WS22-solution | ||||||||||||||||||
9 | Mon | Mar 09 | No class | Quiz for Unit 5 | 06 | Lecture 23: Java Threads and Locks | Module 2: Sections 7.1, 7.3 | Topic 7.1 Lecture, Topic 7.3 Lecture | worksheet23 | lec23-slides |
| WS23-solution | ||||||||||||||||||
| Wed | Mar 11 | No class | 08 | Lecture 24: Java Locks - Soundness and progress guarantees | Module 2: 7.5 | Topic 7.5 Lecture | worksheet24 | lec24-slides |
| WS24-solution | |||||||||||||||||||
| Fri | Mar 13 | No class | 10 | Lecture 25: Dining Philosophers Problem | Module 2: 7.6 | Topic 7.6 Lecture | worksheet25 | lec25-slides |
| WS25-solution | |||||||||||||||||||
- Mon | M-F | Mar 16 - Mar 20 | Mar 13 | No class: Spring Break |
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Mon | Wed | Mar | 2315 | Lecture 23: Actors (contd) | Module 2: 6.6 | Topic 6.6 Lecture, Topic 6.6 Demonstration | No class: Spring Break | lec23-slides |
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| WedFri | Mar | 2517 | 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 |
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10 | Mon | Mar 20 | Lecture 26: N-Body problem, applications and implementations | worksheet26 | lec26lec24-slides | WS26-solution | ||||||||||||||||||||||||
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| FriWed | Mar 2722 | Lecture 25: Java Threads, Java synchronized statement (contd), wait/notify27: Read-Write Locks, Linearizability of Concurrent Objects | Module 2: 7.13, 7.24 | Topic 7.1 3 Lecture, Topic 7.2 4 Lecture | worksheet27 | lec25lec27-slides |
| Homework 3, Checkpoint-2WS27-solution | ||||||||||||||||||||
| 11 | MonFri | Mar | 3024 | Lecture | 26: Java Threads (exercise)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 | worksheet28 | lec28 | lec26-handout | Quiz for Unit 6 | Quiz for Unit 5 |
| Wed | Apr 01 | Lecture 27: Java Locks | Module 2: 7.3 | Topic 7.3 Lecture | lec27-slides |
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| WS28-solution | |||||||
11 | FriMon | Apr 03Mar 27 | 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 | lec28-slides |
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| Wed | 12 | Mon | Apr 06 | Mar 29 | Lecture 30: Task Affinity and locality. Memory hierarchy Lecture 29: Java Locks (exercise) | worksheet30 | lec29lec30-handout slides |
| Quiz for Unit 6 | WS30-solution | |||||||||||||||||||
| WedFri | Apr 08Mar 31 | 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 |
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| Fri | Apr 10 | Lecture 31: Message Passing Interface (MPI), (start of Module 3) | Topic 8.1 Lecture, Topic 8.2 Lecture, Topic 8.3 Lecture | lec31-slides |
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13 | Mon | Apr 13 | Lecture 32: Message Passing Interface (MPI, contd) | Topic 8.4 Lecture | lec32-slides | Homework 4 Checkpoint-1 | ||||||||||||||||||||||||
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 03 | Lecture 32: Barrier Synchronization with Phasers | Module 1: Section 3.4 | Topic 3.4 Lecture, Topic 3.4 Demonstration | worksheet32 | lec32-slides |
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| WS32-solution | ||||||||||||||||||||
| Wed | Apr 05 | 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 |
| WS33-solution | |||||||||||||||||||||
| Fri | Apr 07 | Lecture 34: Fuzzy Barriers with Phasers | Module 1: Section 4.1 | Topic 4.1 Lecture, Topic 4.1 Demonstration | worksheet34 | lec34-slides |
| WS34-solution | |||||||||||||||||||||
13 | Mon | Apr 10 | Lecture 35: Eureka-style Speculative Task Parallelism |
| worksheet35 | lec35 |
| Wed | Apr 15 | Lecture 33: Message Passing Interface (MPI, contd) | Topic 8.5 Lecture, Topic 8 Demonstration Video | lec33-slides |
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| WS35-solution | |||||||||||||||
Fri | Wed | Apr | 1712 | Lecture | 34: Task Affinity with Places36: Scan Pattern. Parallel Prefix Sum |
| worksheet36 | lec34lec36-slides | Quiz for Unit 8 | WS36-solution | Quiz for Unit 7||||||||||||||||||||
14 | MonFri | Apr | 2014 | Lecture | 35: Eureka-style Speculative Task Parallelism37: Parallel Prefix Sum applications | worksheet37 | lec35lec37-slides | |||||||||||||||||||||||
14 | WedMon | Apr | 2217 | Lecture | 36: Algorithms based on Parallel Prefix (Scan) operations38: Overview of other models and frameworks | lec36lec38-slides | Homework 4 (all) | |||||||||||||||||||||||
Fri | Wed | Apr | 2419 | Lecture | 3739: Course Review (Lectures 19- | 3438) | lec37lec39-slides | Quiz for Unit 8 | - | |||||||||||||||||||||
Fri | Apr 21 | Lecture 40: Course Review (Lectures 19-38) | lec40-slides | Homework 5 |
Lab Schedule
Lab # | Date (20202023) | Topic | Handouts | Examples | 0||||||
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1 | Jan 09 | Infrastructure | Setupsetup | lab0-handout | 1 | Jan 16 | Async-Finish Parallel Programming with abstract metrics | lab1-handout | ||
- | Jan 16 | No lab this week (MLK) | ||||||||
2 | Jan | 3023 | FuturesFunctional Programming | lab2-handout | ||||||
3 | Feb 06 | Jan 30 | Java Streams Cutoff Strategy and Real World Performance | lab3-handout | ||||||
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| No lab this week - Spring Recess | Feb 06 | Futures | lab4-handout | |||||
4 5 | Feb 20 | DDFs | 13 | Data-Driven Tasks | lab5-handoutlab4-handout | |||||
- | Feb | 2720 | No lab this week ( | midterm examMidterm) | ||||||
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5 | Mar 05 | Loop-level Parallelism | lab5-handout | lab5-intro | ||||||
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Feb 27 | Async / Finish | lab6-handout | ||||||||
7 | Mar 06 | Recursive Task Cutoff Strategy | lab7-handout | - |
| Isolated Statement and Atomic Variables | ||||
- | Mar 13 | No lab this week (Spring Break)Actors | ||||||||
8 | Mar 20 | Java Threads, Java Locks | - | lab8-handout | Message Passing Interface (MPI) | |||||
9 | Mar 27 | Concurrent Lists | lab9-handout | -|||||||
10 | Apr 03 | Actors | lab10-handout | |||||||
11 | Apr 10 | Loop Parallelism | lab11-handout | Eureka-style Speculative Task Parallelism | ||||||
- | Java's ForkJoin Framework | Apr 17 | 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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