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Improving SSE parallel code with grow and graft genetic programming

Langdon, WB; Lorenz, R; (2017) Improving SSE parallel code with grow and graft genetic programming. In: GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion. (pp. pp. 1537-1538). Association for Computing Machinery (ACM): New York, NY, USA. Green open access

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Abstract

RNAfold predicts the secondary structure of RNA molecules from their base sequence. We apply a mixture of manual and automated genetic improvements to its C source. GI gives a 1.6% improvement to parallel SSE4.1 code. The automatic programming evolutionary system has access to Intel library code and previous revisions. On 4 666 curated structures from RNA STRAND, GGGP gives a combined speed up of 31.9%, with no loss of accuracy (GI code run 1:4 1011 times).

Type: Proceedings paper
Title: Improving SSE parallel code with grow and graft genetic programming
Event: Genetic and Evolutionary Computation Conference (GECCO '17)
ISBN-13: 9781450349390
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3067695.3082524
Publisher version: https://doi.org/10.1145/3067695.3082524
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/10060284
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