Título:
Refactoring an electric-market simulation software for massively parallel computations
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Autor:
Seveso, Franco
Marichal, Raúl
Dufrechou, Ernesto
Ezzatti, Pablo
Marichal, Raúl
Dufrechou, Ernesto
Ezzatti, Pablo
Tutor:
Tipo de documento:
Preprint
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Palabras clave:
Coarse-grained parallelism
Electric energy generation
Stochastic dynamic programming
Memory usage
Electric energy generation
Stochastic dynamic programming
Memory usage
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Año de publicación:
2022
Contenido:
Resumen:
In the last two decades, Uruguay has been immersed in the process of significantly changing its energy generation matrix, especially by the introduction of wind and solar sources. In this context, SimSEE, a
simulation and optimization software designed to help decision-making in generating and distributing electrical energy, is extensively used. The design of this tool is conceived for conventional CPUs and follows a sequential
execution paradigm. This paper focuses on a refactoring of SimSEE that enables leveraging massively-parallel hardware platforms, seeking to adapt the tool for the increasing size and complexity of Uruguay’s
electric market. We extend our previous ideas about reorganizing the software architecture to exploit the parallelism in each time-step of Sim-SEE’s simulation. In more detail, we present two variants following this
parallelism pattern, a straightforward parallel version that requires replicating the used memory and a variant that implies limited performance restrictions but requires a minimal memory overhead.
Descripción:
Latin America High Performance Computing Conference, CARLA 2022, Porto Alegre, Brazil.
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Financiadores:
Agencia Nacional de Investigación e Innovación. Proyecto ANII FSE_1_2018_1_153060 Aceleración del SimSEE utilizando GPUs (SimSEE-MP).
Citación:
Seveso, F., Marichal, R., Dufrechou, E. y otros. Refactoring an electric-market simulation software for massively parallel computations [Preprint] Publicado en: Latin America High Performance Computing Conference, CARLA 2022, Porto Alegre, Brazil, Sep. 26-30, 2022, pp.190-204.
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Licencia:
Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
Colecciones:
| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| SMDE22.pdf | Preprint | 536.09 KB | Adobe PDF |