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TOAST: Applying Answer Set Programming to Superoptimisation / Tom, Crick

Logic Programming, Volume: 4079

Swansea University Author: Tom, Crick

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DOI (Published version): 10.1007/11799573_21

Abstract

Answer set programming (ASP) is a form of declarative programming particularly suited to difficult combinatorial search problems. However, it has yet to be used for more than a handful of large-scale applications, which are needed to demonstrate the strengths of ASP and to motivate the development o...

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Published in: Logic Programming
ISBN: 978-3-540-36635-5 978-3-540-36636-2
ISSN: 0302-9743 1611-3349
Published: Seattle, USA Springer 2006
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URI: https://cronfa.swan.ac.uk/Record/cronfa43401
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spelling 2018-10-15T15:00:25.3999833 v2 43401 2018-08-14 TOAST: Applying Answer Set Programming to Superoptimisation 200c66ef0fc55391f736f6e926fb4b99 0000-0001-5196-9389 Tom Crick Tom Crick true false 2018-08-14 EDUC Answer set programming (ASP) is a form of declarative programming particularly suited to difficult combinatorial search problems. However, it has yet to be used for more than a handful of large-scale applications, which are needed to demonstrate the strengths of ASP and to motivate the development of tools and methodology. This paper describes such a large-scale application, the TOAST (Total Optimisation using Answer Set Technology) system, which seeks to generate optimal machine code for simple, acyclic functions using a technique known as superoptimisation. ASP is used as a scalable computational engine to handle searching over complex, non-regular search spaces, with the experimental results suggesting that this is a viable approach to the optimisation problem and demonstrates the scalability of a variety of solvers. Book chapter Logic Programming 4079 284 Springer Seattle, USA 978-3-540-36635-5 978-3-540-36636-2 0302-9743 1611-3349 17 8 2006 2006-08-17 10.1007/11799573_21 https://link.springer.com/chapter/10.1007%2F11799573_21 22nd International Conference on Logic Programming (ICLP 2006) COLLEGE NANME School of Education COLLEGE CODE EDUC Swansea University 2018-10-15T15:00:25.3999833 2018-08-14T15:45:19.1884819 College of Science Computer Science Martin Brain 1 Tom Crick 0000-0001-5196-9389 2 Marina De Vos 3 John Fitch 4 0043401-12092018070152.pdf ICLP-camera-ready.pdf 2018-09-12T07:01:52.5130000 Output 109622 application/pdf Accepted Manuscript true 2018-09-12T00:00:00.0000000 true eng
title TOAST: Applying Answer Set Programming to Superoptimisation
spellingShingle TOAST: Applying Answer Set Programming to Superoptimisation
Tom, Crick
title_short TOAST: Applying Answer Set Programming to Superoptimisation
title_full TOAST: Applying Answer Set Programming to Superoptimisation
title_fullStr TOAST: Applying Answer Set Programming to Superoptimisation
title_full_unstemmed TOAST: Applying Answer Set Programming to Superoptimisation
title_sort TOAST: Applying Answer Set Programming to Superoptimisation
author_id_str_mv 200c66ef0fc55391f736f6e926fb4b99
author_id_fullname_str_mv 200c66ef0fc55391f736f6e926fb4b99_***_Tom, Crick
author Tom, Crick
format Book chapter
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publishDate 2006
institution Swansea University
isbn 978-3-540-36635-5
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issn 0302-9743
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doi_str_mv 10.1007/11799573_21
publisher Springer
college_str College of Science
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url https://link.springer.com/chapter/10.1007%2F11799573_21
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description Answer set programming (ASP) is a form of declarative programming particularly suited to difficult combinatorial search problems. However, it has yet to be used for more than a handful of large-scale applications, which are needed to demonstrate the strengths of ASP and to motivate the development of tools and methodology. This paper describes such a large-scale application, the TOAST (Total Optimisation using Answer Set Technology) system, which seeks to generate optimal machine code for simple, acyclic functions using a technique known as superoptimisation. ASP is used as a scalable computational engine to handle searching over complex, non-regular search spaces, with the experimental results suggesting that this is a viable approach to the optimisation problem and demonstrates the scalability of a variety of solvers.
published_date 2006-08-17T13:58:03Z
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