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FIT3143 - ASSIGNMENT 3

RESEARCH IN PARALLEL & DISTRIBUTED COMPUTING

Unit learning outcomes (LO) for this assignment:

a)  Compare  and  contrast  different parallel  computing  architectures,  algorithms  and  communication schemes using research-based knowledge and methods (LO2).

b)  Apply technical writing and presentation to effectively communicate parallel computing to a range of academic and expert audiences (LO5).

I.     Overview - Literature Survey

Your task is to communicate a summary of one peer reviewed academic publication (such as conference and journal  papers)  that  cover at least  one  of  the  topic  areas  listed  in  point number II below.  You  will  also demonstrate a proof of concept from the selected paper.

This assignment comprises of the following deliverables:

a)          Presentation video.

b)          Proof of concept demonstration in the video.

c)        Presentation slides.

d)          Proof of concept source code.

e)          Peer-reviewed paper which was referred to in the presentation.

II.     What needs to be done

A) You  need  to  produce  a video which communicates a specific  piece of  research in Parallel  and Distributed Computing.

a)   You should choose at least one topic from the following list to construct a presentation and demonstration of the proof of concept. You may opt to choose more than one topic, but the presentation (based on the selected paper) should include how the topics are interrelated.

●    Physical and logical clocks

●    Message passing system

●    Distributed Mutual Exclusion

●    Deadlock detection, prevention, and avoidance

●    Distributed data structures

●    Distributed communication

●    General Purpose Computing on Graphic Processing Units

●    Parallel debugging

●   Asynchronous programming

●    Parallel computing applications

●   Any other topic related to parallel and distributed computing, as approved by the teaching team (E.g., Quantum computing, CISC/RISC architectures in parallel computing, parallel computing for machine learning/deep learning)

b)   In your video presentation:

●    Literature survey presentation must not exceed 15 minutes including proof of concept demonstration.

●    Please  display  your  full  name  and/or student  ID at the beginning of the presentation slides. Must appear in the video rather than voice over.

C)   Proof of concept demonstration:

●    Could be implemented in any language of your choice.

●    Could use PThreads/OpenMP/OpenMPI/CUDA/OpenCL, etc. as introduced in this unit.

III.     Submission

Individual Assignment: Yes. As part of the authentic assessment structure for technical and taught based units, each student is to work individually in this assignment. While you may have discussions with your peers about the assignment, you must produce and submit your own work. Submission of the assignment is made via Moodle.

You could show your interim work to your tutor during the lab sessions or during the consultation times. This will give you an opportunity to fix issues and improve your presentation content.

The   assignment   3    (Presentation   Video   which    includes   the    Proof-of-Concept   Demonstration) submission is due in Moodle in Week 14 (Friday). Please refer to Moodle for submission instructions and due date.

IV.     Marking Guide

This assignment is worth 20 marks (or 20 percentage points) of your overall unit score.

Marked individually

Introduction and background

2 mark

Problem statement and hypothesis from the reviewed paper

1 mark

Related work discussion from the reviewed paper

3 marks

Methodology from the reviewed paper

3 marks

Analysis of results from the reviewed paper

3 marks

Proof of concept implementation & demonstration

3 marks

Quality of delivery

●    Clarity and depth of explanation.

●    Level of comprehension (for both the presented topic(s) and proof of concept).

●    Level of focus during the presentation and proof of concept demonstration

5 marks

Note: There is a 1-mark penalty per day including weekends for the late submission.

Important: Please refer to the FAQ document to guide you in completing this assignment.

V.     Academic Integrity & Generative AI:

Please do not attempt to plagiarise or collude your assignment work.

Please refer to the University’s policy on academic integrity.

Generative AI tools cannot be used for any assessments in this unit.

In this unit, you must not use generative artificial intelligence (AI) to generate any materials or content in relation to your assessment.

VI.     TIPS / HINTS

-     Look for paper from peer-reviewed conferences/journals.

-     Estimate how long to implement a proof-of-concept. Ask yourself: Is it feasible?

-     Do seek help from the teaching team early. Not on the last day!