Computer Science

ECMM440 - High Performance Computing and Data Architectures (2019)

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MODULE TITLEHigh Performance Computing and Data Architectures CREDIT VALUE15
MODULE CODEECMM440 MODULE CONVENERUnknown
DURATION: TERM 1 2 3
DURATION: WEEKS
Number of Students Taking Module (anticipated) 20
DESCRIPTION - summary of the module content

***DATA SCIENCE AND DATA SCIENCE WITH BUSINESS STUDENTS ONLY***

Data science often requires the storage and processing of large datasets that are beyond the capacity of standard desktop computing. In this module you will learn about high-performance computing and how it can be deployed effectively to perform computational tasks with heavy demands. You will also study the diverse range of architectures for storing and processing very large datasets. The module will cover the core principles underlying software and hardware designs for handling high-demand computation, as well as modern solutions such as distributed systems, super-computers and cloud computing. You will also learn practical skills in software development for HPC and how to access computing resources made available by major commercial providers.

Pre-requisites: ECMM430 Fundamentals of Data Science
Co-requisites: None.

AIMS - intentions of the module

The aim of this module is to equip you with the necessary skills and knowledge to exploit modern computational resources for data-intensive analysis. You will learn about the core principles of data storage and processing, as well as practical skills in how to manipulate large datasets and utilise highperformance computing resources provided by major commercial suppliers. Given the fast-moving nature of the field, the emphasis will be on underlying principles that apply to all systems, but time will also be spent ensuring that you are familiar with current technologies and tools.

Content will be delivered in an intensive one-week teaching block consisting of lectures and practical work. This will be supplemented by guest lectures from industry practitioners working with highperformance computing systems and big data storage. Self-study and coursework will complete the module teaching activities.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module you should be able to:

Module Specific Skills and Knowledge

1. Demonstrate an understanding of the fundamental ideas and issues of high-performance computing and data architectures.
2. Demonstrate knowledge of computational resources for high-throughput computation and storage of large datasets.
3. Demonstrate skills in parallel processing algorithms and program design for HPC.
4. Be able to exploit commercially available high-performance and cloud-based computing resources as part of a data science workflow.

Discipline Specific Skills and Knowledge

5. Identify suitable resources and architectures for solving a given computational problem.
6. Systematically analyse information and make appropriate design choices.

Personal and Key Transferable / Employment Skills and Knowledge

7. Critically analyse academic and technical literature with reference to a given problem.
8. Effectively communicate to a technical audience using reports and documentation.

 

SYLLABUS PLAN - summary of the structure and academic content of the module

Topics will include:

• Motivation and introduction to high-performance computing and data architectures.
• Parallel computation, shared-memory multiprocessors and distributed-memory multicomputers, multi-core processors, Graphics Processing Unit (GPU).
• Interconnection networks in high-performance computers: topologies, switching, messaging, routing.
• Parallel processing algorithm and program design.
• Characteristics, challenges and architectural models for data storage.
• Heterogeneity, openness, security, scalability, failure handling, concurrency and transparency, client-server model and its variations, peer-to-peer model, cloud architectures.
• Cloud computing for computation and storage
• Modern tools and technogies
• Handling very large datasets: challenges and opportunities.

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 32.00 Guided Independent Study 118.00 Placement / Study Abroad 0.00
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled Learning and Teaching 16 Lectures
Scheduled Learning and Teaching 16 Practicals
Guided independent study 50 Coursework Preparation
Guided independent study 68 Background reading and self-study

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Practical Work 16 hours All Oral
       
       
       
       

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 80 Written Exams 0 Practical Exams 20
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Coursework 80 2000-3000 words All Written
Assessed Practical 20 1 hour All Written
         
         
         

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original Form of Assessment Form of Re-assessment ILOs Re-assessed Time Scale for Re-assessment
Coursework Coursework All Within 8 weeks
Assessed Practical Assessed Practical All Within 8 weeks
       

 

RE-ASSESSMENT NOTES

Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a reassessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to re-take some or all parts of the assessment, as decided by the Module Convenor. The final mark given for a module where re-assessment was taken as a result of referral will be capped at 50%.

RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Basic reading:

 

ELE: http://vle.exeter.ac.uk/

 

Web based and Electronic Resources:

 

Other Resources:

 

Reading list for this module:

There are currently no reading list entries found for this module.

CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES ECMM430
CO-REQUISITE MODULES
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Tuesday 10 July 2018 LAST REVISION DATE Tuesday 18 December 2018
KEY WORDS SEARCH High performance computing, data architectures, big data storage, cloud computing