english Icono del idioma   español Icono del idioma  

Por favor, use este identificador para citar o enlazar este ítem: https://hdl.handle.net/20.500.12008/36664 Cómo citar
Título: Towards a massively-parallel version of the SimSEE
Autor: Marichal, Raúl
Vallejo, Damián
Dufrechou, Ernesto
Ezzatti, Pablo
Tipo: Preprint
Palabras clave: Coarse-grained parallelism, Electric energy generation, Stochastic dynamic programming
Fecha de publicación: 2021
Resumen: The SimSEE is a simulation software used/designed to aid the decision-making in the electric energy generation market. It is based on Stochastic DynamicProgramming technique and allows to simulate the contribution of several energy sources, such as hydro-electric, solar, thermal or wind energy, to a specific electrical network. Uruguay’s electric generation system has considerably grown and diversified in the past decades. This evolution implies potentially more complex scenarios and also motivates a more precise modeling of some electric sources. Therefore, the computational cost of the simulations is also expected to rise and the use of HPC techniques becomes mandatory. In this work we study the performance bottlenecks in the SimSEE tool. Additionally, and considering the previously mentioned results, we design a parallelization strategy that enables its acceleration using massively-parallel devices such as GPUs.
Descripción: 2021 IEEE URUCON, Montevideo, Uruguay, 2021, pp. 440-443.
Editorial: IEEE
Financiadores: Agencia Nacional de Investigación e Innovación. Proyecto ANII FSE_1_2018_1_153060
Citación: Marichal, R., Vallejo, D., Dufrechou, E. y otros. Towards a massively-parallel version of the SimSEE. [Preprint]. Publicado en: 2021 IEEE URUCON, Montevideo, Uruguay, 2021, pp. 440-443, DOI: 10.1109/URUCON53396.2021.9647142.
Licencia: Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
Aparece en las colecciones: Reportes Técnicos - Instituto de Computación

Ficheros en este ítem:
Fichero Descripción Tamaño Formato   
MVDE21.pdfPreprint459,68 kBAdobe PDFVisualizar/Abrir


Este ítem está sujeto a una licencia Creative Commons Licencia Creative Commons Creative Commons