COMP 322: Fundamentals of Parallel Programming (Spring
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2022)
InstructorInstructors: | Mackale Joyner, DH 2071 | Head TA: | Srdan Milakovic | Co-Instructor: | 2063 Zoran Budimlić, DH 31343003 | Graduate TAs: | Jonathan SharmanAdrienne 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: | Liam Bonnage, Harrison Brown, Mustafa El-Gamal, Krishna Goel, Ryan Green, Ryan Han, Rishu Harpavat, Namanh Kapur, Tian Lan, Tam Le, Will LeVine, Eva Ma, Hamza Nauman, Rutvik Patel, Aryan Sefidi, Jeemin Sim, Tory Songyang, Jiaqi Wang, Erik Yamada, Yifan Yang, Aydin Zanagar |
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Piazza site: | Piazza site: | https://piazza.com/class/jmwfpr1i85n7l4 (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: | Sewall Hall 301Herzstein Amphitheater (online 1st 2 weeks) | Lecture times: | MWF 1:00pm - 1:50pm | ||||
Lab locations: | Sewall Hall 301Keck 100 (online 1st 2 weeks) | Lab times: | Mon 3:00pm - 3:50pm (Austin, Claire) Wed 4:30pm - 5:20pm (Hunena, Mantej, Yidi, Victor, Rose, Adrienne, Diep, Maki)Thursday, 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.
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, MapReduce. 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
Finally, here are some additional resources that may be helpful for you:
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- Multiprocessor Programming by Maurice Herlihy and Nir Shavit
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 | 1 | Mon | Jan 08 | Lecture 1: Task Creation and Termination (Async, Finish) | Module 1: Section 1.1 | Topic 1.1 Lecture, Topic 1.1 DemonstrationWork Due | Worksheet Solutions | |||||||
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1 | Mon | worksheet1 | lec1-slides |
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| Wed | Jan 10 | Lecture 21: 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 | Introduction |
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| FriWed | Jan 12 | Lecture | 3: Abstract Performance Metrics, Multiprocessor SchedulingModule 1: Section 1.4 | Topic 1.4 Lecture, Topic 1.4 Demonstration | worksheet3 | lec3-slides |
| 2 | Mon | Jan 15 | No lecture, School Holiday (Martin Luther King, Jr. Day)2: Functional Programming | GList.java | worksheet2 | lec02-slides |
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Fri | Jan 14 | Lecture 3: Higher order functions | worksheet3 | lec3-slides |
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2 | WedMon | Jan 17 | No lecture, Rice closed due to weatherclass: MLK | Quiz for Unit 1 | |||||||||||||||||||
| FriWed | Jan 19 | 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 | WS4-solution | |||||||||||||
| Fri | 3 | Mon | Jan 2221 | Lecture 5: Future Tasks, Functional Parallelism ("Back to the Future") | Module 1: Section 2.1 | Java Streams | Topic 2.1 Lecture, Topic 2.1 Demonstration | worksheet5 | lec5-slides-slides | Homework 1 | WS5-solution | |||||||||||
3 | WedMon | Jan 24 | Lecture 7: Finish Accumulators6: Map Reduce with Java Streams | Module 1: Section 2.34 | Topic 2.3 4 Lecture, Topic 2.3 4 Demonstration | worksheet7worksheet6 | lec7lec6-slides | Homework 2 |
| WS6-solution | Homework 1 | ||||||||||||
| FriWed | Jan 26 | Lecture 8: Memoization, Map Reduce7: Futures | Module 1: Section 2.2 & 2.41 | Topic 2.2 Lecture, Topic 2.2 Demonstration, Topic 2.4 1 Lecture , Topic 2.4 1 Demonstration | worksheet8worksheet7 | lec8lec7-slides |
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| Fri | Jan 28 | Lecture 8: Computation Graphs, Ideal Parallelism | 4 | Mon | Jan 29 | Lecture 9: Data Races, Functional & Structural Determinism | Module 1: 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 | worksheet9worksheet8 | lec9lec8-slides | WS8-solution | |||||||||||
4 | Mon Wed | Jan 31 | Lecture 10: Java’s Fork/Join Library9: Async, Finish, Data-Driven Tasks | Module 1: Sections 2Section 1. 71, 24. 85
| Topic 2.71.1 Lecture, Topic 1.1 Demonstration, Topic 4.5 Lecture, Topic 2.8 Lecture, | worksheet10 | lec10-slides | 4.5 Demonstration | worksheet9 | lec9-slides | WS9-solutionQuiz for Unit 2 | ||||||||||||
Fri | Wed | Feb 02 | Lecture | 11: Loop-Level Parallelism, Parallel Matrix Multiplication, Iteration Grouping (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 | worksheet11 | lec11-slides | ||||||||||||||||
5 | Mon | Feb 05 | Lecture 12: Barrier Synchronization | Module 1: Section 3.4 | Topic 3.4 Lecture , Topic 3.4 Demonstration | worksheet12 | lec12-slides | ||||||||||||||||
Wed | Feb 07 | Lecture 13: Parallelism in Java Streams, Parallel Prefix Sums | Topic 3.7 Java Streams, Topic 3.7 Java Streams Demonstration | worksheet13 | lec13-slides | Homework 3 (includes 2 intermediate checkpoints) | Homework 2 | ||||||||||||||||
- | Fri | Feb 09 | Spring Recess | ||||||||||||||||||||
6 | Mon | Feb 12 | Lecture 14: 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 | worksheet14 | lec14-slides | Quiz for Unit 3 | Quiz for Unit 2 | ||||||||||||||
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-slides | WS12-solution | |||||||||||||||
| Wed | Feb 09 | Lecture 13: Parallel Speedup, Critical Path, Amdahl's Law | Module 1: Section 1.5 | Topic 1.5 Lecture , Topic 1.5 Demonstration | worksheet13 | lec13-slides | WS13-solution | |||||||||||||||
| Fri | Feb 11 | No class: Spring Recess
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6 | Mon | Feb 14 | Lecture 14: Accumulation and reduction. Finish accumulators | Module 1: Section 2.3 | Topic 2.3 Lecture Topic 2.3 Demonstration | worksheet14 | lec14-slides | WS14-solution | |||||||||||||||
| Wed | Feb 16 | Lecture 15: Recursive Task Parallelism |
| Wed | Feb 14 | Lecture 15: Data-Driven Tasks | Module 1: Sections 4.5, 4.2, 4.3 | Topic 4.5 Lecture Topic 4.5 Demonstration, Topic 4.3 Lecture, Topic 4.3 Demonstration | worksheet15 | lec15-slides |
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Fri | Feb | 1618 | Lecture 16: | Point-to-point Synchronization with PhasersData Races, Functional & Structural Determinism | Module 1: Sections 42.5, 2.6 | Topic | 4.22.5 Lecture , Topic 2.5 Demonstration, Topic 2.6 Lecture, Topic | 42. | 26 Demonstration | worksheet16 | lec16-slides | Homework 3 | Homework 2 | WS16-solution | Quiz for Unit 3 | ||||||||
7 | Mon | Feb 1921 | Lecture 17: Midterm Summary | lec17-slides |
| Wed | Feb 21 | Midterm Review (interactive Q&A)Midterm Review | lec17-slides | ||||||||||||||
| FriWed | Feb 23 | Lecture 18: Limitations of Functional parallelism. | worksheet18 | lec18lec18-slides | Homework 3, Checkpoint-1 | WS18-solution | ||||||||||||||||
| Fri | Feb 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, | 8 | Mon | Feb 26 | 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 28 | 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 Lecture, Topic 5.3 Demonstration, 5.6 Lecture, Topic 5.6 Demonstration | worksheet20 | lec20-slides | WS20-solution | |||||||||||||||
| Wed | Mar 02 | Lecture 21: Atomic variables, Synchronized statements | Module 2: Sections 5.4, 7.2 | Topic 5.4 Lecture, Topic 5.4 Demonstration, Topic 57.6 Lecture, Topic 5.6 Demonstration | worksheet20 | lec20-slides | 2 Lecture | worksheet21 | lec21-slides | WS21-solution | ||||||||||||
| Fri | Mar 0204 | Lecture 21: Read-Write Isolation, Review of Phasers | Module 2: Section 5.5 | Topic 5.5 Lecture, Topic 5.5 Demonstration | worksheet21 | lec21-slides | Quiz for Unit 5 | 22: Parallel Spanning Tree, other graph algorithms | worksheet22 | lec22-slides | Homework 4 | Homework 3 | WS22-solution | Quiz for Unit 4 | ||||||||
9 | Mon | Mar 0507 | 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 0709 | Lecture 23: Actors (contd) | Module 2: 6.3, 6.4, 6.5, 6.6 | Topic 6.3 Lecture, Topic 6.3 Demonstration, Topic 6.4 Lecture , Topic 6.4 Demonstration, Topic 6.5 Lecture, Topic 6.5 Demonstration, Topic 6.6 Lecture, Topic 6.6 Demonstration | worksheet23 | lec23-slides | Quiz for Unit 6 | 24: Java Locks - Soundness and progress guarantees | Module 2: 7.5 | Topic 7.5 Lecture | worksheet24 | lec24-slides |
| WS24-solution | Homework 3, Checkpoint-2 | |||||||
| Fri | Mar 0911Lecture 24: Java Threads, Java synchronized statement | Lecture 25: Dining Philosophers Problem | Module 2: 7.1, 7.26 | Topic 7.1 Lecture, Topic 7.2 Lecture | worksheet24 | 6 Lecture | worksheet25 | lec25lec24-slides | Quiz for Unit 5
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Mon | Mar 14 | No class: | M-F | Mar 12 - Mar 16 | Spring Break |
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Wed | Mar | 1916 | 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 | 2118 | 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 |
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10 | Mon | Mar 21 | Lecture 26: N-Body problem, applications and implementations |
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Homework 3 (all) |
| FriWed | Mar 23 | 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 7 | Quiz for Unit 6 | 11 | Mon |
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| Fri | Mar 25 | Mar 26Lecture 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 | Topic 8.1 Lecture, Topic 8 | .2 Lecture, Topic | 8.3 Lecture,6.2 Demonstration | worksheet28 | lec28-slides |
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11 | MonWed | Mar 28 | 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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| WS29-solutionQuiz for Unit 8 | ||||||||||||
| FriWed | Mar 30 | Lecture 30: | 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 | Task Affinity and locality. Memory hierarchy | worksheet30 | lec30-slides |
| Quiz for Unit 7 | 12 | Mon | WS30-solution | |||||||||||
| Fri | Apr 01Apr 02 | Lecture 31: TF-IDF and PageRank Algorithms with Map-Reduce | 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 9.4 Lecture, Topic 9.5 Lecture, Unit 9 Demonstration | worksheet31 | lec31-slides | Homework 5 | Homework 4 | WS31-solutionQuiz for Unit 9 | ||||||||||||
12 | WedMon | Apr 04 | Lecture 32: Partitioned Global Address Space (PGAS) programming models | Barrier Synchronization with Phasers | Module 1: Section 3.4 | Topic 3.4 Lecture, Topic 3.4 Demonstration | worksheet32 | lec32-slides |
| Homework 4 Checkpoint-1 | WS32-solution | ||||||||||||
| FriWed | Apr 06 | Lecture 33: Combining Distribution and Multithreading | 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 Lectures 10.1 - 10.5, Unit 10 Demonstration (all videos optional – unit 10 has no quiz) | worksheet33 | lec33-slides |
| Quiz for Unit 8 | WS33-solution | ||||||||||||
| Fri | Apr 08 | 13 | Mon | Apr 09 | 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 | MonWed | Apr 11 | Lecture 35: Eureka-style Speculative Task Parallelism |
| worksheet35 | lec35-slides | Homework 5 | Homework 4 (all) |
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Wed | FriApr 13 | Lecture 36: | GPU ComputingScan Pattern. Parallel Prefix Sum |
| worksheet36 | lec36-slides | Quiz for Unit 9 | 14 | Mon | WS36-solution | |||||||||||||
Fri | Apr 15 | Apr 16Lecture 37: | Algorithms based onParallel Prefix | (Scan) operationsSum applications | worksheet37 | lec37-slides | |||||||||||||||||
14 | Mon | WedApr 18 | Lecture 38: | Algorithms based on Parallel Prefix (Scan) operations, contd.Overview of other models and frameworks | worksheet38 | lec38-slides | |||||||||||||||||
Wed | FriApr 20 | Lecture 39: Course Review (Lectures | 1819-38) | lec39-slides | Homework 5 | - | |||||||||||||||||
Fri | Apr 22 | Lecture 40: Course Review (Lectures 19-38) | lec40-slides | Homework 5 |
Lab Schedule
Lab # | Date (20182022) | Topic | Handouts | Code Examples | 0 | Infrastructure Setup | lab0-handout | -Handouts | Examples | ||
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1 | Jan 1110 | Infrastructure setup | lab0-handoutAsync-Finish Parallel Programming with abstract metrics lab1-handout - | ||||||||
2 | Jan | 2517 | FuturesFunctional Programming | lab2-handout | -|||||||
3 | Feb 01 | Jan 24 | Java Streams Cutoff Strategy and Real World Performance | lab3-handout - | |||||||
4 | Feb 15 | Java's ForkJoin Framework | lab4-handout | - | - | Feb 22
| No lab this week - Midterm ExamJan 31 | Futures | lab4-handout | - | |
5 | Mar 01 | Feb 07 | Data-Driven Tasks | lab5-handout- | |||||||
6 | Mar 05 | Feb 14 | Async / FinishLoop-level Parallelism | lab6-handout handout- | |||||||
- | Mar 15Feb 21 | No lab this week | - Spring Break(Midterm) | ||||||||
7 | Feb 28 | Recursive Task Cutoff Strategy | lab7-handout | 7 | |||||||
8 | Mar | 2207 | Java Threads | lab8 | Isolated Statement and Atomic Variables | lab7-handout | |||||
8- | Mar 29 | Actors | 14 | No lab this week (Spring Break) | lab8-handout | ||||||
9 | Apr 05 | Mar 21 | Concurrent ListsJava Threads, Java Locks | lab9-handout | |||||||
10 | Apr 12 | Apache SparkMar 28 | Actors | lab10-handout | |||||||
11 | Apr | 19Message Passing Interface (MPI)04 | Loop Parallelism | lab11-handout | |||||||
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| Apr 11 | No lab this weekEureka-style Speculative Task Parallelism | ||||||||
- | 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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