Units / FIT3143

FIT3143 · Parallel computing

Official Handbook

2026 Handbook6 credit pointsLevel 3Faculty of Information Technology

Last checked: 22 Aug 2026 UTC

Overview

Modern computer systems contain parallelism in both hardware and software. This unit covers parallelism in both general purpose and application specific computer architectures and the programming paradigms that allow parallelism to be exploited in software. The unit examines shared memory and message passing paradigms in hardware and software; concurrency, multithreading and synchronicity; parallel, clustered and distributed supercomputing algorithms, languages and software tools and development environments. You will learn to design and develop parallel algorithms in these paradigms, and apply technical writing and presentation to communicate parallel computing.

Areas of study: Advanced computer science

Offerings

CampusTeaching periodMode
ClaytonSecond semesterTeaching activities are on-campus (ON-CAMPUS)
MalaysiaSecond semesterTeaching activities are on-campus (ON-CAMPUS)

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

#AssessmentTypeWeightHurdle
1LaboratoriesDemonstration30%
2Applied SessionsDemonstration20%
3Applied Problem Solving Tasks (Quiz)Quiz / Test50%

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

prerequisite

  • FIT2004 — Algorithms and data structures

Learning outcomes

  1. Examine the design principles of parallel computing architectures;
  2. Evaluate and contrast different types of parallel architectures using Flynn's taxonomy;
  3. Analyse the design principles of distributed parallel computing systems;
  4. Criticise and assess common performance models used for parallel applications;
  5. Research and solve synchronisation problems typical in the design of parallel applications;
  6. Compare the fundamental principles of parallel hardware using vector processing, Graphics Processing Units (GPU) and Neural Processing Units (NPU);
  7. Judge modern parallel computing applications and the impact of exponential growth in hardware

Workload

This unit has a requirement of 1-2 hours per week of asynchronous learning. Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled teaching activities.

ActivityDuration
Laboratories12 hours
Applied sessions12 hours
Workshops24 hours

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