Surrey University Stag

PARALLEL COMPUTING - 2022/3

Module code: COM2039

Module Overview

The course introduces concepts of parallel and distributed computing by considering different architectures that support this, and working through different categories of examples. The implementation of such solutions and their subsequent analysis gives practical experience and an understanding of the difficulties involved. Special consideration will be given to performance issues of resulting architectures, leading to a foundation for the design of high performance computing for distributed real-time control.

Digital Capabilities

The theory and practical components of this module provide students digital skills to develop solutions to problems that make use of parallel architectures. Problems that require these architectures are widespread in engineering and modelling and this module teaches both practical skills and a different way of thinking about this class of problem.

Employability

Some problems in engineering and computer science that require high performance and low latency can be solved by parallel architectures. These problems generally complex with extremely tight time constraints. The theory and practical session on this module will allow students to explore how problems can be parallelised to optimise performance.

Global and Cultural Skills

Computer Science is a global language and the tools and languages used on this module can be used internationally. This module allows students to develop skills that will allow them to develop applications with global reach and collaborate with their peers around the world.

Resourcefulness and Resilience

This module teaches a new way of developing a solution to a particular class of problem. The theory provides a grounding in how we can take advantage of parallel architectures and in the lab, students will be able to take advantage of hardware that allows them to test out the theory in practice.

Module provider

Computer Science

Module Leader

DE Suparna (Elec Elec En)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 5

JACs code: I115

Module cap (Maximum number of students): N/A

Overall student workload

Independent Learning Hours: 106

Lecture Hours: 11

Laboratory Hours: 22

Captured Content: 11

Module Availability

Semester 2

Prerequisites / Co-requisites

None

Module content


  • Scope of Parallel Computing:

    • From GPU to real time IoT;



  • Control Structure of Parallel Platforms

  • Communication Model of Parallel Platforms

  • Physical Organisation of Parallel Platforms

  • Communication Costs and Routing Mechanisms

  • Principles of Parallel Algorithm Design

    • Decomposition Techniques

    • Load Balancing

    • Containing Interaction Overheads

    • Parallel Algorithm Models



  • Communication Models

  • Analytical Modelling of Parallel Algorithms

  • Performance Characteristics:

    • Response times; throughputs; queue lengths; utilizations



  • Basics of Control Theory:

    • Dynamics of resource management; stability;



  • Advanced examples from feedback control of biological systems


Assessment pattern

Assessment type Unit of assessment Weighting
Online Scheduled Summative Class Test In-Lab class test (administered through SurreyLearn) 30
Coursework Coursework 70

Alternative Assessment

N/A

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate that they have achieved the module learning outcomes.

 

Thus, the summative assessment for this module consists of:

·        An in-lab class test through SurreyLearn, on the whole course content. This addresses LO1, LO3 and LO4.

·        An individual coursework on sets of problems that students are required to solve. This addresses LO1, LO2, LO3 and LO4.

Formative assessment and feedback

Interactive quizzes during the lectures (through Polleverywhere) and online tests on SurreyLearn offering revision of the week's lecture/lab content, are used to explain and test understanding of the theory and application of the concepts. Students will work through progressively structured weekly lab exercises, where completion of each is necessary to progress to the next. Solutions to lab exercises will be made available in subsequent lab sessions and explained to the students as part of preparation for the coursework and test. 

Module aims

  • The module aims to develop the student's ability to think clearly about the relationship between a problem abstraction and architectural implementation details. We focus on the techniques for the development of solutions of parallel computing problems as leads to high-performance computing and distributed architectures. A number of case studies will be considered to illustrate facets of the subject. On completion of the module, the students will have a good understanding of methods for optimizing the performance of parallel, distributed, and concurrent architectures

Learning outcomes

Attributes Developed
1 Explain the major benefits and limitations of parallel computing KC
2 Identify and explain the differences between common current parallel architectures KC
3 Develop parallel solutions for computationally intensive problems on distributed architectures P
4 Analyse the performance of a parallel/distributed solutions KCT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

The learning and teaching strategy is designed to:


  • Help students understand the distinctive features of a broad range of parallel programming techniques

  • Show the application of design techniques for solving distributed programming problems

  • Explain students how to analyse and optimise the performance characteristics of concurrent and distributed architectures

  • Equip students with necessary mathematical background to prepare them for exposure to more advanced analytical techniques

  • Enable students to apply taught techniques to solve concrete problems



 

The learning and teaching methods include:

Lectures (in-person) (11 hours; 1hour/week) using detailed lecture slides and class quizzes to gauge the students’ understanding.

Lectures through Captured content (11 hours)

Labs (22 hours; 2 hour/week) - including class tests/assessments and lab exercises using lab worksheets (and their solutions).

Students are expected to spend time outside of the contact hours on self-study to prepare and revise the lecture and lab material.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: COM2039

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Computer Science BSc (Hons) 2 Compulsory A weighted aggregate mark of 40% is required to pass the module

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2022/3 academic year.