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Data-structures for maintain non-dominated sets on high performance and distributed systems. Computer Science PhD.

  • Full or part time
  • Application Deadline
    Monday, May 13, 2019
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

The University of Exeter EPSRC DTP (Engineering and Physical Sciences Research Council Doctoral Training Partnership) is offering up to 4 fully funded doctoral studentships for 2019/20 entry. Students will be given sector-leading training and development with outstanding facilities and resources. Studentships will be awarded to outstanding applicants, the distribution will be overseen by the University’s EPSRC Strategy Group in partnership with the Doctoral College.

Prof. Jonathan Fieldsend, Department of Computer Science, College of Engineering, Mathematics and Physical Sciences
Dr David Acreman, Department of Physics and Astronomy, College of Engineering, Mathematics and Physical Sciences

Project description:
Data structures are fundamental to the efficient operation of computer systems. A well-known equation in computer science (taken from the title of Niklaus Wirth’s seminal text) is “Algorithms + Data Structures = Programs”. Building on initial work by researchers from the Multi-Criterion Decision Making (MCDM) community, various specialised data structures have been developed by optimisation researchers over the last two decades for the storage and efficient update of sets of mutually non-dominating solutions (these include data structures based on bi-objective trees, quad-trees, dominated and non-dominated trees, dominance decision trees, M-fronts, ND trees and BSP Trees). These data structures maintain a set of designs discovered during a multi-objective optimisation procedure which have the mathematical relationship that no set member can be said to be any worse than any of the others discovered, due to the particular trade-off combination of values of the criteria that member represents. These sets are used to maintain designs during an optimisation run, and can be queried for e.g. online quality statistics, and a source of designs to vary whilst searching for higher quality solutions. The final set of solutions can then be processed for consideration by an end users of the design optimisation process, to consider which designs to employ.

Often analysis has been provided for these non-dominated set data structures in terms of worst/average case complexity performance in terms of domination comparisons, including how these scale with the number of criteria and set size. However, often other aspects such as rebalancing costs of underlying data structures, memory requirements, effect of cache size, distribution of queries and updates to the set, and appropriateness for distributed/parallel compute systems have been ignored. However, all additional these issues can significantly affect realised performance. Many large scale optimisation tasks are performed on high performance parallel computing architectures, and can result in the need to maintain and effectively query and update such sets. This project will entail the development and analysis of data-structures for storing non-dominated data sets on such high performance and distributed systems, including assessing performance as it scales to that considered as ‘Big Data’.

This PhD studentship would best suit a computer scientist with interests including data structures, computational geometry, high performance computing and their application to support optimisation.

Funding Notes

For successful eligible applicants the studentship comprises:

An index-linked stipend for up to 3.5 years full time (currently £14,777 per annum for 2018/19), pro-rata for part-time students.
Payment of University tuition fees (UK/EU)
Research Training Support Grant (RTSG) of £5,000 over 3.5 years, or pro-rata for part-time students

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