000 02474naa a2200265 a 4500
003 AR-LpUFIB
005 20250311170510.0
008 230201s2020 xx o 000 0 eng d
024 8 _aDIF-M8313
_b8533
_zDIF007606
040 _aAR-LpUFIB
_bspa
_cAR-LpUFIB
100 1 _aMontes de Oca, Erica Soledad
245 1 0 _aGreen high performance simulation for AMB models of Aedes aegypti
300 _a1 archivo (1,0 MB)
500 _aFormato de archivo PDF. -- Este documento es producción intelectual de la Facultad de Informática - UNLP (Colección BIPA/Biblioteca)
520 _aThe increase in temperature caused by the climate change has resulted in the rapid dissemination of infectious diseases. Given the alert for the current situation, the World Health Organization (WHO) has declared a state of health emergency, highlighting the severity of the situation in some countries. For this reason, coming up with knowledge and tools that can help control and eradicate the vectors propagating these diseases is of the utmost importance. Highperformance modeling and simulation can be used to produce knowledge and strategies that allow predicting infections, guiding actions and/or training health/civil protection agents. The model developed as part of this research work is aimed at assisting the decision-making process for disease prevention and control, as well as evaluating the reproduction and predicting the evolution of the Aedes aegypti mosquito, which is the transmitting vector of the dengue, Zika and chikungunya diseases. Decisionmaking based on these models requires a large number of simulations to achieve results with statistical variability. The objective of this paper is to demonstrate that the GPU is a suitable platform from the point of view of the reduction of energy consumed for HPC simulations. It is also shown that it is possible to define energy prediction models that allow scientists to plan their experiments based on energy consumption and select those that are representative for decision making by reducing energy consumption in HPC simulations.
534 _aJournal of Computer Science & Technology, 20(1), pp. 15-22.
650 4 _aPROCESADORES GRÁFICOS (GPUs)
650 4 _aCOMPUTACIÓN DE ALTO RENDIMIENTO - HPC
653 _aAedes aegypti
700 1 _aSuppi, Remo
700 1 _aDe Giusti, Laura Cristina
700 1 _aNaiouf, Ricardo Marcelo
856 4 0 _uhttps://doi.org/10.24215/16666038.20.e02
942 _cCP
999 _c57379
_d57379