Optimized Galaxy Bayesian Surface Photometry on a Many-Core Platform - Kolesnikov, Igor; Mendes, Celso; de Carvalho, Reinaldo; Rosa, Reinaldo (Anais do Simpósio em Sistemas Computacionais de Alto Desempenho (SSCAD); 2020: Anais do XXI Simpósio em Sistemas Computacionais de Alto DesempenhoDO - 10.5753/wscad.2020.14081, 2020)
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.