Optimized Galaxy Bayesian Surface Photometry on a Many-Core Platform

  • Igor Kolesnikov INPE
  • Celso Mendes INPE
  • Reinaldo de Carvalho Universidade Cruzeiro do Sul
  • Reinaldo Rosa INPE

Resumo


Parametric computational modeling of galaxies is a process with a high computational cost. The statistical component of modeling, which may involve model refinements in relation to the source brightness distribution, achieves more satisfactory results when the Bayesian approach is employed. In our research, we use GALaxy PHotometric ATtributes (GALPHAT) as our primary tool for data processing. In the current scenario of cosmology, to be scientifically relevant, this type of modeling must be performed on thousands of galaxies. In this article, we present the study and optimization of solutions based on modern HPC platforms, including a many-core processor, that enable effective processing of that amount of galaxies obtained from Sloan Digital Sky Survey.

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Publicado
21/10/2020
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KOLESNIKOV, Igor; MENDES, Celso; DE CARVALHO, Reinaldo; ROSA, Reinaldo. Optimized Galaxy Bayesian Surface Photometry on a Many-Core Platform. In: SIMPÓSIO EM SISTEMAS COMPUTACIONAIS DE ALTO DESEMPENHO (WSCAD), 21. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 335-346. DOI: https://doi.org/10.5753/wscad.2020.14081.