COMP 322: Fundamentals of Parallel Programming (Spring
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2023)
Instructor: | Mackale Joyner, DH 2071 | Head TA: | Srdan Milakovic | |||
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Co-Instructor: | Zoran Budimlić, DH 3134 | Graduate TAs: | Jonathan Sharman | |||
2063 | 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 | Admin Assistant: | Annepha Hurlock, annepha@rice.edu, DH 3122, 713-348-5186 | Undergraduate TAs: | Liam Bonnage, Harrison Brown, Mustafa El-Gamal, Krishna Goel, Ryan Green, Ryan Han, Rishu Harpavat, Namanh Kapur, Tian Lan, Tam Le, Eva Ma, Hamza Nauman, Rutvik Patel, Aryan Sefidi, Tory Songyang, Jiaqi Wang, Erik Yamada, Yifan Yang|
Piazza site: | https://piazza.com/rice/classspring2022/jmwfpr1i85n7l4comp322 (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-Cross-listing: | ELEC 323 | |||
Lecture location: | Herring Hall 100TBD | Lecture times: | MWF 1:00pm - 1:50pm | |||
Lab locations: | Herring Hall 100TBD | Lab times: | Mon 3:00pm - 3: | 50pm () Tue 4Thursday, 4:00pm - 4:50pm () |
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 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 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
Week | Day | Date (20182022) | 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 0709 | 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 0911 | 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 | Functional Programming | GList.java | worksheet2 | lec02-slides |
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| WS2-solutionHomework 1 | ||||||||||||
Fri | Jan 1113 | 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 1416 | Lecture 4: Parallel Speedup and Amdahl's Law | Module 1: Section 1.5 | Topic 1.5 Lecture, Topic 1.5 Demonstration | worksheet4 | lec4-slides | No class: MLK | ||||||||||||||||||
| Wed | Jan 18 | Lecture 4: Lazy Computation | LazyList.java Lazy.java | worksheet4 | lec4-slides | WS4-solution | |||||||||||||||||||
| Fri | Jan 20 | Lecture 5: Java Streams |
| Wed | Jan 16 | Lecture 5: Future Tasks, Functional Parallelism ("Back to the Future") | Module 1: Section 2.1 | Topic 2.1 Lecture, Topic 2.1 Demonstration | worksheet5 | lec5-slides | Quiz for UnitHomework 1 | WS5-solution | Fri | ||||||||||||
3 | Mon | Jan | 1823 | Lecture 6: | Memoization Map Reduce with Java Streams | Module 1: Section 2.24 | Topic 2. | 24 Lecture, Topic 2. | 24 Demonstration | worksheet6 | lec6-slides |
| 3 | WS6-solution | Mon | Jan 21 | No lecture, School Holiday (Martin Luther King, Jr. Day) | |||||||||
| Wed | Jan | 2325 | Lecture 7: | Finish AccumulatorsFutures | Module 1: Section 2.31 | Topic 2. | 31 Lecture , Topic 2. | 31 Demonstration | worksheet7 | lec7-slides | Homework 1 |
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| Fri | Jan 2527 | Lecture 8:Map Reduce Computation Graphs, Ideal Parallelism | Module 1: Section Sections 1.2, 1.43 | Topic 1.2 .4 Lecture, Topic 1.2 Demonstration, Topic 1.3 Lecture, Topic 21.4 3 Demonstration | worksheet8 | lec8-slides | WS8-solution | Quiz for Unit 1 | |||||||||||||||||
4 | Mon
| Jan 2830 | Lecture 9: Data Races, Functional & Structural DeterminismAsync, Finish, Data-Driven Tasks | Module 1: Sections 2Section 1. 51, 24. 65
| Topic 21. 51 Lecture, Topic 21. 51 Demonstration, Topic 24. 65 Lecture, Topic 24. 65 Demonstration | worksheet9 | lec9-slidesslides | WS9-solution | ||||||||||||||||||
Wed | Jan 30Feb 01 | Lecture 10: | Java’s Fork/Join LibraryModule 1: Sections 2.7, 2.8 | Topic 2.7 Lecture, Topic 2.8 Lecture, Event-based programming model
| worksheet10 | lec10-slides | Quiz for Unit 2Homework 1 | WS10-solution | ||||||||||||||||||
Fri | Feb | 0103 | Lecture 11: | Loop-Level Parallelism, Parallel Matrix Multiplication, Iteration Grouping (Chunking) GUI programming as an example of event-based, futures/callbacks in GUI programming | worksheet11 | lec11-slides | Homework 2 | WS11-solution | ||||||||||||||||||
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 | worksheet11 | lec11-slides | 5 | Mon | Feb | 0406 | Lecture 12: | Barrier Synchronization Scheduling/executing computation graphs Abstract performance metrics | Module 1: Section 31.4 | Topic | 31.4 Lecture , Topic | 31.4 Demonstration | worksheet12 | lec12-slides | WS12-solution | ||||||||||
| Wed | Feb | 0608 | Lecture 13: | Parallelism in Java Streams, Parallel Prefix SumsParallel Speedup, Critical Path, Amdahl's Law | Module 1: Section 1.5 | Topic 1.5 Lecture , Topic 1.5 | Topic 3.7 Java Streams, Topic 3.7 Java Streams | Demonstration | worksheet13 | lec13-slides | Homework 3 (includes 2 intermediate checkpoints) | Homework 2 | WS13-solution | ||||||||||||
- | Fri | Feb 0810 | No class: Spring Recess
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6 | Mon | Feb | 1113 | Lecture 14: | Iterative Averaging Revisited, SPMD pattern Accumulation and reduction. Finish accumulators | Module 1: Sections 3Section 2.5, 3.6 | Topic | 3.5 Lecture , Topic 3.5 Demonstration , Topic 3.6 Lecture, Topic 3.6 Demonstration2.3 Lecture Topic 2.3 Demonstration | worksheet14 | lec14-slides | Quiz for Unit 3 | Quiz for Unit 2WS14-solution | ||||||||||||||
| Wed | Feb 1315 | Lecture 15: Data-Driven Tasks | Module 1: Section 4.5 | Recursive Task Parallelism | Topic 4.5 Lecture Topic 4.5 Demonstration | worksheet15 | lec15-slides |
| WS15-solution | ||||||||||||||||
Fri | Feb | 1517 | Lecture 16: | Point-to-point Synchronization with PhasersData Races, Functional & Structural Determinism | Module 1: Sections 42.5, 2, 4.36 | Topic | 42. | 25 Lecture , Topic | 42. | 25 Demonstration, Topic | 42. | 36 Lecture, | Topic 4.3Topic 2.6 Demonstration | worksheet16 | lec16-slides | Quiz for Unit 3 | Homework 3 | Homework 2 | WS16-solution | |||||||
7 | Mon | Feb 1820 | Lecture 17: Midterm Summary Midterm Review | lec17-slides | Wed | Feb 20 | Midterm Review (interactive Q&A) | |||||||||||||||||||
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| FriWed | Feb 22 | Lecture 18: Abstract Limitations of Functional parallelism. | worksheet18 | lec18lec18-slides | Homework 3, Checkpoint-1 | WS18-solution | ||||||||||||||||||
| Fri | Feb 24 | 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, | 8 | Mon | Feb 25 | 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 | Quiz for Unit 4 | WS19-solution | ||||||||||||
8 | WedMon | Feb 27 | Lecture 20: Critical sections, Isolated construct, Parallel Spanning Tree algorithm, Atomic variables (start of Module 2) Confinement & Monitor Pattern. Critical sections | 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 6 Lecture, Topic 5.3 Demonstration, Topic 5.4 Lecture, Topic 5.4 Demonstration, Topic 5.6 Lecture, Topic 5.6 Demonstration6 Demonstration | worksheet20 | lec20-slides | WS20-solution | ||||||||||||||||||
| FriWed | Mar 01 | Lecture 21: Read-Write Isolation, Review of Phasers Atomic variables, Synchronized statements | Module 2: SectionSections 5. 54, 7.2 | Topic 5.5 4 Lecture, Topic 5.5 Demonstration4 Demonstration, Topic 7.2 Lecture | worksheet21 | lec21-slides | Quiz for Unit 5 | Quiz for Unit 4 | 9 | Mon | WS21-solution | ||||||||||||||
| Fri | Mar 03Mar 04 | Lecture 22: Actors | Module 2: 6.1, 6.2 | Parallel Spanning Tree, other graph algorithms | Topic 6.1 Lecture , Topic 6.1 Demonstration , Topic 6.2 Lecture, Topic 6.2 Demonstration | worksheet22 | lec22-slides |
| Homework 4 | Homework 3 | WS22-solution | ||||||||||||||
9 | WedMon | Mar 06 | Lecture 23: Actors (contd)Java Threads and Locks | Module 2: 6Sections 7.31, 6.4, 6.5, 6.67.3 | Topic 67. 31 Lecture, Topic 67.3 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 | Quiz for Unit 6 | Homework 3, Checkpoint-2 | Lecture | worksheet23 | lec23-slides |
| WS23-solution | ||||||||||||
| Wed |
| Fri | Mar 08 | Lecture 24: Java Threads, Java synchronized statementLocks - Soundness and progress guarantees | Module 2: 7.1, 7.25 | Topic 7.1 Lecture, Topic 7.2 Lecture | worksheet24 | 5 Lecture | worksheet24 | lec24-slides | Quiz for Unit 5
| WS24- | M-F | Mar 11 - Mar 15 | Spring Break | solution | |||||||||
| Fri | Mar 10 | Lecture 25: Dining Philosophers Problem | 10 | Mon | Mar 18 | Lecture 25: Java synchronized statement (contd), wait/notify | Module 2: 7.26 | Topic 7.2 6 Lecture | worksheet25 | lec25-slides |
| WS25-solution | |||||||||||||
WedMon | Mar 2013 | 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 | No class: Spring Break | (includes one intermediate checkpoint) |
| Homework 3 (all) | ||||||||||||||||
Fri | Wed | Mar | 2215 | Lecture 27: Safety and Liveness Properties, Java Synchronizers, Dining Philosophers Problem | Module 2: 7.5, 7.6 | Topic 7.5 Lecture, Topic 7.6 Lecture | worksheet27 | lec27-slides | Quiz for Unit 7 | Quiz for Unit 6 | 11 | Mon | Mar 25 | Lecture 28: Message Passing Interface (MPI), (start of Module 3) | Topic 8.1 Lecture, Topic 8.2 Lecture, Topic 8.3 Lecture, | worksheet28 | lec28-slidesNo class: Spring Break |
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| Fri | Mar 17 | No class: Spring Break |
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10 | WedMon | Mar 2720 | Lecture 29: Message Passing Interface (MPI, contd) | Topic 8.4 Lecture, Topic 8.5 Lecture, Topic 8 Demonstration Video | worksheet29 | lec29-slides | 26: N-Body problem, applications and implementations | worksheet26 | lec26-slides | WS26-solutionQuiz for Unit 8 | ||||||||||||||||
| FriWed | Mar | 2922 | Lecture | 3027: | Distributed Map-Reduce using Hadoop and Spark frameworksTopic 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 01 | Lecture 31: TF-IDF and PageRank Algorithms with Map-Reduce | Topic 9.4 Lecture, Topic 9.5 Lecture, Unit 9 Demonstration | worksheet31 | lec31-slides | Quiz for Unit 9 |
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| Wed | Apr 03 | Guest Lecture |
| Homework 4 Checkpoint-1 | |||||||||||||||||||||
| Fri | Apr 05 | Lecture 32: Partitioned Global Address Space (PGAS) programming models | Lectures 10.1 - 10.5, Unit 10 Demonstration (all videos optional – unit 10 has no quiz) | worksheet32 | lec32-slides |
| Quiz for Unit 8 | 13 | Mon | Apr 08 | Lecture 33: Combining Distribution and Multithreading | worksheet33 | lec33-slides | ||||||||||||
Read-Write Locks, Linearizability of Concurrent Objects | Module 2: 7.3, 7.4 | Topic 7.3 Lecture, Topic 7.4 Lecture | worksheet27 | lec27-slides |
| WS27-solution | ||||||||||||||||||||
| Fri | Mar 24 | Lecture 28: Message-Passing programming model with Actors | Module 2: 6.1, 6.2 | Topic 6.1 Lecture, Topic 6.1 Demonstration, Topic 6.2 Lecture, Topic 6.2 Demonstration | worksheet28 | lec28-slides |
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| WS28-solution | ||||||||||||||||
11 | Mon | Mar 27 | Lecture 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 29 | Lecture 30: Task Affinity and locality. Memory hierarchy | worksheet30 | lec30-slides |
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| Fri | Mar 31 | Lecture 31: Data-Parallel Programming model. Loop-Level Parallelism, Loop Chunking | Module 1: Sections 3.1, 3.2, 3.3 | Topic 3.1 Lecture, Topic 3.1 Demonstration , Topic 3.2 Lecture, Topic 3.2 Demonstration, Topic 3.3 Lecture, Topic 3.3 Demonstration | worksheet31 | lec31-slides | Homework 5 | Homework 4 | WS31-solution | ||||||||||||||||
12 | Mon | Apr 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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| 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 |
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| 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 | ||||||||||||||||||||||||
| Wed | Apr 10 | Lecture 34: Task Affinity with Places | worksheet34 | lec34-slides | Homework 4 (all) | ||||||||||||||||||||
| Fri | Apr 12 | Lecture 35: Eureka-style Speculative Task Parallelism |
| worksheet35 | lec35-slides |
| Quiz for Unit 9 | 14 | Mon |
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Wed | Apr 12 | Apr 15Lecture 36: | Algorithms based onScan Pattern. Parallel Prefix | (Scan) operationsSum |
| worksheet36 | lec36-slides | WS36-solution | ||||||||||||||||||
Fri | Apr 14 | Lecture 37: Parallel Prefix Sum applications | worksheet37 | lec37-slides | Wed | |||||||||||||||||||||
14 | Mon | Apr 17 | Lecture | 37: Algorithms based on Parallel Prefix (Scan) operations, contd.38: Overview of other models and frameworks | worksheet37 | lec37lec38-slides | ||||||||||||||||||||
Fri | Wed | Apr 19 | Lecture | 3839: Course Review (Lectures | 1819-38) | lec38lec39-slides | Homework 5 | - | ||||||||||||||||||
Fri | Apr 21 | Lecture 40: Course Review (Lectures 19-38) | lec40-slides | Homework 5 |
Lab Schedule
Lab # | Date (20192023) | Topic | Handouts | Examples | 0||||||||
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1 | Jan 09 | Infrastructure | Setupsetup | lab0-handout lab1-handout | 1 | |||||||
- | Jan | 10Async-Finish Parallel Programming with abstract metrics | lab1-handout | 16 | No lab this week (MLK) | - | ||||||
2 | Jan | 1723 | FuturesFunctional Programming | lab2-handout | -||||||||
3 | Jan 2430 | Java Streams Cutoff Strategy and Real World Performance | lab3-handout - | |||||||||
4 | Jan 31 | Java's ForkJoin FrameworkFeb 06 | Futures | lab4-handout | ||||||||
5 | - | Feb 7
13 | Data-Driven Tasks | lab5-handoutNo lab this week - Spring Recess | ||||||||
- | 5 | Feb | 1420 | No lab this week (Midterm) | DDFslab5-handout | - | ||||||
6 | Feb 2827 | Async / FinishLoop-level Parallelism | lab6-handout | lab6-intro | ||||||||
7 | Mar | 7Isolated Statement and Atomic Variables06 | Recursive Task Cutoff Strategy | lab7-handout | -||||||||
- | Mar | 1413 | No lab this week | -(Spring Break) | ||||||||
8 | Mar 2120 | ActorsJava Threads | lab8-handout- | |||||||||
9 | Mar 2827 | Concurrent ListsJava Threads, Java Locks | lab9-handout- | |||||||||
10 | Apr | 4Message Passing Interface (MPI)03 | Actors | lab10-handout | -||||||||
11 | Apr | 1110 | Loop Parallelism | lab11-handout | Apache Spark | lab11-handout | - |
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| Eureka-style Speculative Task Parallelism | ||
- | 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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