Performance analysis of Cellular Automata HPC implementations

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

doi:10.1016/j.compeleceng.2015.09.015

Cited by 11 publications

(5 citation statements)

References 35 publications

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“…Most of them are focused on highlighting the benefits of computing CA on GPUs versus CPUs. Examples can be found in [11,12], and [13]. In these studies, there is little discussion about GPU optimization algorithms and/or techniques applicable to CA.…”

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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“…Unfortunately, all presented methods and techniques of CA models parallelization are widely used in computational science for the simulation of different problems but are usually not related to complex microstructure evolution phenomena. [149,[164][165][166] Kühbach [158] did pioneering research in the area of CA SRX parallel simulations. The authors performed simulations with a large 3D domain consisting of 2560 Â 640 Â 640 CA cells that were decomposed into a series of computing nodes.…”

Section: High-performance Ca Models Of Static Recrystallizationmentioning

confidence: 99%

Section: High‐performance Ca Models Of Static Recrystallizationmentioning

confidence: 99%

Computationally Efficient Cellular Automata‐Based Full‐Field Models of Static Recrystallization: A Perspective Review

Madej

Sitko

2022

steel research int.

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The evolution of metallic material microstructures under thermomechanical treatment is a critical element that influences the in‐use properties of the final product. In the last four decades, many researchers around the world focused on reliable numerical recreation of phenomena that occur during metal processing. Particular attention is put on the phase transformation, texture evolution, and recrystallization predictions to simulate material behavior at the subsequent processing stages. In recent years, approaches taking into account explicit representation of morphological elements during simulation are becoming more frequently used even at the industrial scale. One of those methods addressed within the current article in application to static recrystallization (SRX) modeling is the cellular automata (CA) approach. A review of the CA‐based full‐field SRX models highlights the progress in capabilities of developed approaches in consideration of various mechanisms controlling microstructure evolution like heterogeneous energy distribution, probabilistic or physics‐based nucleation, grain growth driven by different driving forces, or grain boundary migration is presented within the article. The issue of the computational time associated with 3D CA modeling is particularly addressed. Possible approaches to reducing simulation times based on efficient use of modern computer architecture are finally evaluated as guidelines for further model development.

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“…For this reason, advanced techniques of calculation parallelization are more often used. Some of the solutions are focused on parallelization for processors within a single computing unit [ 20 ], more advanced are those designed for massively parallel computing centers [ 21 ] or modern graphics cards [ 22 ]. In the former case, the number of working threads is limited to the number of CPU cores (independent processing units that read and execute instructions), which are available on a computing unit.…”

Section: Introductionmentioning

confidence: 99%

Scaling Scientific Cellular Automata Microstructure Evolution Model of Static Recrystallization toward Practical Industrial Calculations

2021

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An attempt to bridge the gap between capabilities offered by advanced full-field microstructure evolution models based on the cellular automata method and their practical applications to daily industrial technology design was the goal of the work. High-performance parallelization techniques applied to the cellular automata static recrystallization (CA-SRX) model were selected as a case study. Basic assumptions of the CA-SRX model and developed modifications allowing high-performance computing are presented within the paper. Particular attention is placed on the development of the parallel computation scheme allowing numerical simulations even for a large volume of material. The development of new approaches to handle communication within the distributed environment is also addressed in the paper as a means to obtain higher computational efficiency. Evaluation of model limits was based on the scalability analysis. The investigation was carried out for the 3D and 2D case studies. Therefore, the complex static recrystallization cellular automata simulation taking into account the influence of recovery, nucleation based on accumulated energy, and the progress of recrystallization as a function of stored energy and grain boundary mobility with high-performance computing capabilities is now possible. The research highlighted that parallelization is more effective with an increasing number of cellular automata cells processed during the entire simulation. It was also proven that the developed parallelization scheme and communication mechanism provides a possibility of obtaining scaled speedup over 700 times for 2D and over 800 times for 3D computational domains, which is crucial for future applications in industrial practice. Therefore, the presented approach’s main advantage is based on the possibility of running the calculation based on input data obtained directly from high-resolution 3D imaging of the microstructure. With that, the full immersion of the experimental results into the numerical model is possible. The second novelty aspect of this work is related to the identification of the quality of model predictions as a function of model size reductions.

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Performance analysis of Cellular Automata HPC implementations

Cited by 11 publications

References 35 publications

Efficient simulation execution of cellular automata on GPU

Efficient simulation execution of cellular automata on GPU

Computationally Efficient Cellular Automata‐Based Full‐Field Models of Static Recrystallization: A Perspective Review

Scaling Scientific Cellular Automata Microstructure Evolution Model of Static Recrystallization toward Practical Industrial Calculations

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