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2 results for “numerical variability model”

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zenodo44/100

Smart Analyser of Variability Requirements of Unknown Spaces (SAVRUS) Dataset of a study with 5 real-world large numerical variability models.

<p>The publications and research associated to cite is in:</p><p><a href="https://doi.org/10.1016/j.knosys.2023.110558">https://doi.org/10.1016/j.knosys.2023.110558</a></p><p>In that research we detail the Smart Analyser of Variability Requirements of Unknown Spaces (SAVRUS) approach, and provide a web-tool prototype in <a href="https://hadas.caosd.lcc.uma.es/savrus">https://hadas.caosd.lcc.uma.es/savrus</a></p><p>In the study, we model 5 different real-world software product lines to then analysed them with SAVRUS:</p><p>Detailed real-world variability models ordered by their search space size, of which GEC QA is incompletely measured NVM Description #Booleans #Numericals Space QA #Measurements&nbsp;</p><p>Dune1</p><p>&nbsp;</p><p>Multi-grid solver</p><p>&nbsp;</p><p>11</p><p>&nbsp;</p><p>3</p><p>&nbsp;</p><p>2,304</p><p>&nbsp;</p><p>Complex..</p><p>&nbsp;</p><p>2,304</p><p>&nbsp;</p><p>HSMGP1</p><p>&nbsp;</p><p>Stencil-grid solver</p><p>&nbsp;</p><p>14</p><p>&nbsp;</p><p>3</p><p>&nbsp;</p><p>3,456</p><p>&nbsp;</p><p>..equation..</p><p>&nbsp;</p><p>3,456</p><p>&nbsp;</p><p>HiPAcc1</p><p>&nbsp;</p><p>Image processing framework</p><p>&nbsp;</p><p>33</p><p>&nbsp;</p><p>2</p><p>&nbsp;</p><p>13,485</p><p>&nbsp;</p><p>..solving..</p><p>&nbsp;</p><p>13,485</p><p>&nbsp;</p><p>Trimesh2</p><p>&nbsp;</p><p>Triangle mesh library</p><p>&nbsp;</p><p>13</p><p>&nbsp;</p><p>4</p><p>&nbsp;</p><p>239,360</p><p>&nbsp;</p><p>..time</p><p>&nbsp;</p><p>239,360</p><p>&nbsp;</p><p>GEC</p><p>&nbsp;</p><p>Generic edge computing</p><p>&nbsp;</p><p>552</p><p>&nbsp;</p><p>2</p><p>&nbsp;</p><p>~5.3*108</p><p>&nbsp;</p><p>Energy Consumption</p><p>&nbsp;</p><p>132500</p><p>&nbsp;</p><p>The dataset zip file contains:</p><ul><li>5 numerical variability models in Clafer format (.txt) for each software product line.</li><li>5 CSV files with the respective quality attribute measurements</li><li>An .xlsx file containing SAVRUS scalability results divided in different tabs.</li></ul><p>References:</p><p>[1] N. Siegmund, A. Grebhahn, S. Apel, C. Kastner, Performance-influence models for highly configurable systems, in: Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, ESEC/FSE 2015, Association for Computing Machinery, New York, NY, USA, 2015, p.284–294. doi:10.1145/2786805.2786845.</p><p>[2] M. Bauer, A comparison of six constraint solvers for variability analysis, Tech. rep., University of Passau (2019).</p>

opencc-by-4.0Feb 2022View details →
zenodo36/100

The interpretation of temperature and salinity variables in numerical ocean model output and the calculation of heat fluxes and heat content - ACCESS-CM2 data and code

<p>This dataset contains post-processed ACCESS-CM2 PI control CMIP6 climate model&nbsp;output and code used to produce&nbsp;Figs. 5, 6 and 9 in the published article:</p> <p>McDougall, T., J., Barker, P.M.,&nbsp;Holmes, R.M., Pawlowicz, R., Griffies, S. and Durack, P. (2021): The interpretation of temperature and salinity variables in numerical ocean model output and the calculation of heat fluxes and heat content,&nbsp;<strong>Geoscientific Model Development</strong>,&nbsp;14, 1&ndash;21, <a href="https://doi.org/10.5194/gmd-2020-426">https://doi.org/10.5194/gmd-2020-426</a></p> <p>The processed ACCESS-CM2 data is included as .mat files and is accompanied by&nbsp;Matlab processing routines (including code from the TEOS-10 Gibbs SeaWater Oceanographic Toolbox, https://www.teos-10.org/software.htm#1) to produce the figures. A&nbsp;more detailed description of the data are included in README.md. The code and data is also available under version control at&nbsp;<a href="https://github.com/rmholmes/ACCESS_CM2_SpecificHeat/tree/GMD_Published">https://github.com/rmholmes/ACCESS_CM2_SpecificHeat/tree/GMD_Published</a>.</p>

opencc-by-4.0Oct 2021View details →

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International Brain Laboratory public data

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