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.
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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 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: | https://discovery.ucl.ac.uk/id/eprint/10060284 |
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