A cloud computing based framework for general 2D and 3D cellular automata simulation

Marques, Rodrigo Fernando Costa; Feijó, Bruno; Breitman, Karin; Gomes, Thiéberson da Silva; Ferracioli, Laércio; Lopes, Hélio

doi:10.1016/j.advengsoft.2013.05.014

Cited by 6 publications

(5 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, besides CPU and GPU simulations, recent solutions based on heterogeneous clusters and cloud computing should be mentioned as they gave significant possibilities for the simulation of complex problems with the CA method. [163] As presented earlier, the conventional approach to the CA method is based on a regular shape of the CA space, which potentially provides enormous possibilities for efficient parallelization. Because a CA space consists of a finite number of cells, it is straightforward to change the way of computations from a sequential to a parallel mode.…”

Section: High-performance Ca Models Of Static Recrystallizationmentioning

confidence: 99%

See 1 more Smart Citation

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

Madej

Sitko

2022

steel research int.

View full text Add to dashboard Cite

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.

show abstract

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.

View full text Add to dashboard Cite

show abstract

“…For example, Radenski [51] tested such an algorithm on Amazon's Elastic MR Cloud with a maximum 1.6 × 10 8 cells in a 2D situation, where they employed 1 master node and 16 core nodes in this simulation. Marques and colleagues [52] developed a new computational framework based on MapReduce to implement a 1 trillion cell 2D CA simulation on the Microsoft Azure cloud platform. Those large CA approaches show the applicability of CA to big data/big model computing, but also leave room for other investigators to achieve improved solutions with simpler modeling, faster computing, and easier data access.…”

Section: Fine-tuned Ca Simulation On the Cloudmentioning

confidence: 99%

Data Gap Filling Using Cloud-Based Distributed Markov Chain Cellular Automata Framework for Land Use and Land Cover Change Analysis: Inner Mongolia as a Case Study

et al. 2022

View full text Add to dashboard Cite

With advances in remote sensing, massive amounts of remotely sensed data can be harnessed to support land use/land cover (LULC) change studies over larger scales and longer terms. However, a big challenge is missing data as a result of poor weather conditions and possible sensor malfunctions during image data collection. In this study, cloud-based and open source distributed frameworks that used Apache Spark and Apache Giraph were used to build an integrated infrastructure to fill data gaps within a large-area LULC dataset. Data mining techniques (k-medoids clustering and quadratic discriminant analysis) were applied to facilitate sub-space analyses. Ancillary environmental and socioeconomic conditions were integrated to support localized model training. Multi-temporal transition probability matrices were deployed in a graph-based Markov–cellular automata simulator to fill in missing data. A comprehensive dataset for Inner Mongolia, China, from 2000 to 2016 was used to assess the feasibility, accuracy, and performance of this gap-filling approach. The result is a cloud-based distributed Markov–cellular automata framework that exploits the scalability and high performance of cloud computing while also achieving high accuracy when filling data gaps common in longer-term LULC studies.

show abstract

“…As the utilization of smart phones quickly spread, mobile cloud computing appeared and made a number of applications and services available on mobile devices [5]- [9], providing improved infrastructure and service for big data collection and research [10] [11]. Driven by its scalability, reduced costs, and easy access, cloud computing is also popular in geoscience research and applications [12]- [14]. In short, cloud computing is popular in various fields of research due to its great potential in sharing resources between local computers with other computing devices over the Internet.…”

Section: Introductionmentioning

confidence: 99%

Technical Possibilities of Cloud-Based Virtual Reality Implementing Software as a Service for Online Collaboration in Urban Planning

Shen¹,

Ma²,

Sugihara³

et al. 2014

IJCNS

View full text Add to dashboard Cite

In this paper we discuss the technical possibilities of cloud-based virtual reality (cloud-based VR) computing tools for online collaboration in urban planning and design. We first create a digital asset representing our design proposal of a pedestrian bridge in Shibuya, Tokyo. A platform for cloud-based VR technology, i.e., a VR-Cloud server, is used to open the VR dataset to public collaboration over the Internet. The digital asset representing the design scheme of our pedestrian bridge includes buildings, roads, trees and street furniture for the entire urban area. The vehicles and people are designed and inputted into the virtual world of the urban area, in which they run and walk with predefined behaviour scenarios. Users share the VR world by accessing the VR-Cloud servers from remote clients, using cloud communication software to review vehicle and pedestrian crowd simulations and discuss the design concepts. Meanwhile, we compare the advantages and disadvantages of three cloud-based VR tools on their technical support for net collaboration: 1) VR-Cloud; 2) Google Earth; and 3) 3DVIA.

show abstract

A cloud computing based framework for general 2D and 3D cellular automata simulation

Cited by 6 publications

References 20 publications

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

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

Data Gap Filling Using Cloud-Based Distributed Markov Chain Cellular Automata Framework for Land Use and Land Cover Change Analysis: Inner Mongolia as a Case Study

Technical Possibilities of Cloud-Based Virtual Reality Implementing Software as a Service for Online Collaboration in Urban Planning

Contact Info

Product

Resources

About