Performance analysis and comparison of cellular automata GPU implementations

Millán, Emmanuel N.; Wolovick, Nicolás; Piccoli, María Fabiana; Garino, Carlos García; Bringa, Eduardo M.

doi:10.1007/s10586-017-0850-3

Cited by 11 publications

(10 citation statements)

References 37 publications

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“…Several algorithms and issues that influence the CA performance in GPUs were studied in [16] using again GoL cellular automaton as an example. Issues such as the influence of shared memory on GPUs and the thread block size on Cuda were analysed.…”

Section: Related Workmentioning

confidence: 99%

Efficient simulation execution of cellular automata on GPU

Cagigas-Muñiz

Díaz-del-Río

Sevillano

et al. 2022

Simulation Modelling Practice and Theory

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Section: Related Workmentioning

confidence: 99%

Efficient simulation execution of cellular automata on GPU

Cagigas-Muñiz

Díaz-del-Río

Sevillano

et al. 2022

Simulation Modelling Practice and Theory

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“…Nevertheless, it utilizes the critical aspect of simulating cellular automata. Recently, primarily due to the increase of available computing power, including high-performance GPU computing [14], such a problem gains a significant interest. For this reason, its scientific impact was also classified as high.…”

Section: Second Prizementioning

confidence: 99%

Brilliant Challenges Optimization Problem Submission Contest Final Report

Badura,

Laskowski,

Antczak

et al. 2021

Preprint

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This paper concludes the Brilliant Challenges contest. Participants had to design interesting optimization problems and publish them using the Optil.io platform. It was the first widely-advertised contest in the area of operational research where the objective was to submit the problem definition instead of the algorithmic solutions. Thus, it is a crucial contribution to Open Science and the application of crowdsourcing methodology to solve discrete optimization problems. The paper briefly describes submitted problems, presents the winners, and discusses the contest's achievements and shortcomings. Finally, we define guidelines supporting the organization of contests of similar type in the future.

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“…The size of the simulations is not ideal for benchmarking, bigger simulations can produce a better performance speedup using GPUs. See [14] and [15] for more benchmarks.…”

Section: B Computational Benchmarksmentioning

confidence: 99%

Análisis de clústeres para simulaciones de mecánica granular mediante algoritmos de aprendizaje automático

Rim¹,

Millán²,

Planes³

et al. 2020

Entre cienc. ing.

Self Cite

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Las simulaciones de dinámica molecular (MD) en colisiones de granos permiten incorporar propiedades complejas de interacciones de polvo. Realizamos simulaciones de colisiones de granos porosos, cada uno con muchas partículas, utilizando el software LAMMPS de MD. Las simulaciones consistieron en un grano de proyectil que golpeó un grano objetivo inmóvil más grande, con diferentes velocidades de impacto. La desventaja de este método es el gran costo computacional debido a que se modela una gran cantidad de partículas. Machine Learning (ML) tiene el poder de manipular grandes datos y construir modelos predictivos que podrían reducir los tiempos de simulación MD. Usando algoritmos ML (Support Vector Machine y Random Forest) podemos predecir el resultado de las simulaciones MD con respecto a la formación de fragmentos, después de varios pasos más pequeños que en las simulaciones MD habituales. Logramos una reducción de tiempo de al menos un 46%, para una precisión del 90%. Estos resultados muestran que SVM y RF pueden ser herramientas poderosas pero simples para reducir el costo computacional en simulaciones de fragmentación de colisiones.

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Performance analysis and comparison of cellular automata GPU implementations

Cited by 11 publications

References 37 publications

Efficient simulation execution of cellular automata on GPU

Efficient simulation execution of cellular automata on GPU

Brilliant Challenges Optimization Problem Submission Contest Final Report

Análisis de clústeres para simulaciones de mecánica granular mediante algoritmos de aprendizaje automático

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