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Details of Grant 

EPSRC Reference: EP/L000555/1
Title: Abstraction-Level Energy Accounting and Optimisation in Many-core Programming Languages
Principal Investigator: Nikolopoulos, Professor D
Other Investigators:
Leather, Dr H O'Boyle, Professor M de Supinski, Professor B
Researcher Co-Investigators:
Dr Z Wang
Project Partners:
ARM Ltd BluWireless Technology Freescale Semiconductor
Herta Security IBM UK Ltd
Department: Sch of Electronics, Elec Eng & Comp Sci
Organisation: Queen's University of Belfast
Scheme: Standard Research
Starts: 31 December 2013 Ends: 28 April 2017 Value (£): 661,061
EPSRC Research Topic Classifications:
Fundamentals of Computing
EPSRC Industrial Sector Classifications:
Electronics Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
29 May 2013 SADEA Full Announced
Summary on Grant Application Form
Energy efficiency is becoming increasingly important in today's world of battery powered mobile devices and power limited servers. While

performance optimisation is a familiar topic for developers, few are even aware of the effects that source code changes will have on the

energy profiles of their programs. Without knowledge of these effects, compiler and operating system writers cannot create automatic energy

optimisers. To realise the needed energy savings, we require the capability to track energy consumption and associate it to code

and data at a fine granularity. Furthermore, compilers and operating systems must exploit this capability to optimise applications

automatically.

This proposal presents a novel approach to software-centric modelling, measurement, accounting and optimisation of energy-efficiency on

many-core systems. Energy consumption will be matched against programming language abstractions, from basic-blocks to functions,

loops, and parallel constructs, and from variables to data structures, providing developers with the information that they need. The project will use this fine grained accounting to build novel compiler optimisations that target energy consumption. It will create low energy runtime systems that adapt to environmental changes. It will develop energy efficient operating system scheduling that manages multi-tasking for heterogeneous many-cores. The project aims to improve performance per Watt by at least 40%.
Key Findings
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Potential use in non-academic contexts
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Impacts
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Summary
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Further Information:  
Organisation Website: http://www.qub.ac.uk