COMPX553-22A (HAM)

Extremely Parallel Programming

15 Points

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Division of Health Engineering Computing & Science
School of Computing and Mathematical Sciences
Department of Computer Science

Staff

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Convenor(s)

Shaoqun Wu

Shaoqun Wu
4782
G.2.21
See Moodle
shaoqun.wu@waikato.ac.nz

Lecturer(s)

Administrator(s)

: maria.admiraal@waikato.ac.nz
: buddhika.subasinghe@waikato.ac.nz

Placement/WIL Coordinator(s)

Tutor(s)

Student Representative(s)

Lab Technician(s)

Librarian(s)

: alistair.lamb@waikato.ac.nz

You can contact staff by:

  • Calling +64 7 838 4466 select option 1, then enter the extension.
  • Extensions starting with 4, 5, 9 or 3 can also be direct dialled:
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Paper Description

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This paper covers advanced parallel programming for large-scale parallelism. A variety of programming techniques will be covered, with application to cluster computers, GPU computing and many-core computing. The learning outcomes for this paper are linked to Washington Accord graduate attributes WA1-WA11. Explanation of the graduate attributes can be found at: https://www.ieagreements.org/
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Paper Structure

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There are one two-hour lecture and a weekly two hours lab in Lab1 or Lab 6 (R-Block). All online resources, support and discussion forums are available via Moodle.
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Learning Outcomes

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Students who successfully complete the paper should be able to:

  • explain the basic concepts, benefits, and challenges of parallel programming
    Linked to the following assessments:
    • GPGPU Programming Quiz (3)
    • Java Executors Quiz (6)
    • Apache Spark Project (11)
  • develop parallel programs using a variety of techniques, such as Hadoop MapReduce, Apache Spark, the OpenCL language for GPU programming, and Java thread pools and streams (WA1, WA2, WA3, WA4,WA5)
    Linked to the following assessments:
    • Photo-Editor Assignment (1)
    • GPGPU Programming Assignment 1 (2)
    • GPGPU Programming Assignment 2 (4)
    • Java Streams Assignment (5)
    • Java CompletableFuture Assignment (7)
    • Hadoop MapReduce Assignment (8)
    • Apache Spark Assignment 1 (9)
    • Apache Spark Assignment 2 (10)
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Assessment

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If you are enrolled in a BE(Hons), samples of your work may be required as part of the Engineering New Zealand accreditation process for BE(Hons) degrees. Any samples taken will have the student name and ID redacted. If you do not want samples of your work collected then please email the engineering administrator, Natalie Shaw (natalie.shaw@waikato.ac.nz), to opt out.
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Assessment Components

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The internal assessment/exam ratio (as stated in the University Calendar) is 100:0. There is no final exam. The final exam makes up 0% of the overall mark.

The internal assessment/exam ratio (as stated in the University Calendar) is 100:0 or 0:0, whichever is more favourable for the student. The final exam makes up either 0% or 0% of the overall mark.

Component DescriptionDue Date TimePercentage of overall markSubmission MethodCompulsory
1. Photo-Editor Assignment
18 Mar 2022
11:30 PM
10
  • Other: Submit through GitLab
2. GPGPU Programming Assignment 1
25 Mar 2022
11:30 PM
5
  • Other: Submit through GitLab
3. GPGPU Programming Quiz
1 Apr 2022
11:30 PM
5
  • Online: Submit through Moodle
4. GPGPU Programming Assignment 2
8 Apr 2022
10:30 PM
20
  • Other: Submit through GitLab
5. Java Streams Assignment
15 Apr 2022
11:30 PM
5
  • Other: Submit through GitLab
6. Java Executors Quiz
22 Apr 2022
11:30 PM
3
  • Online: Submit through Moodle
7. Java CompletableFuture Assignment
22 Apr 2022
11:30 PM
7
  • Other: Submit through GitLab
8. Hadoop MapReduce Assignment
20 May 2022
11:30 PM
15
  • Other: Submit through GitLab
9. Apache Spark Assignment 1
27 May 2022
11:30 PM
10
  • Other: Submit through GitLab
10. Apache Spark Assignment 2
3 Jun 2022
11:30 PM
10
  • Other: Submit through GitLab
11. Apache Spark Project
17 Jun 2022
11:30 PM
10
  • Other: Submit through GitLab
Assessment Total:     100    
Failing to complete a compulsory assessment component of a paper will result in an IC grade
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Required and Recommended Readings

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Required Readings

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The required and recommended reading for the paper will be specified on the Moodle website.There is no required textbook for this paper.

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Online Support

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All online resources, support and discussion forums are available via Moodle.
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Workload

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The expected workload is twelve hours per week, for 12.5 weeks = 150 hours.

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Linkages to Other Papers

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Prerequisite(s)

Prerequisite papers: COMPX202 or COMPX242 or COMP204 or COMP242, or equivalent Java and jUnit experience.

Corequisite(s)

Equivalent(s)

Restriction(s)

Restricted papers: COMP453, COMP553

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