A parallel version of the cellular automata static recrystallization model dedicated for high performance computing platforms – Development and verification

2020 ◽  
Vol 172 ◽  
pp. 109283 ◽  
Author(s):  
Mateusz Sitko ◽  
Qi Chao ◽  
Jiangting Wang ◽  
Konrad Perzynski ◽  
Krzysztof Muszka ◽  
...  
Keyword(s):  
Cellular Automata ◽  
High Performance Computing ◽  
High Performance ◽  
Static Recrystallization ◽  
Parallel Version ◽  
Computing Platforms ◽  
Performance Computing

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

Materials ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4082
Author(s):  
Mateusz Sitko ◽  
Krzysztof Banaś ◽  
Lukasz Madej
Keyword(s):  
Cellular Automata ◽  
High Performance Computing ◽  
Microstructure Evolution ◽  
High Performance ◽  
Static Recrystallization ◽  
Full Field ◽  
Practical Applications ◽  
Grain Boundary Mobility ◽  
Computation Scheme ◽  
Performance Computing

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.

scholarly journals Investigating methods of supporting dynamically linked executables on high performance computing platforms.

2009 ◽  
Author(s):  
Suzanne Marie Kelly ◽  
James H., III Laros ◽  
Kevin Thomas Tauke Pedretti ◽  
Michael J. Levenhagen
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Computing Platforms ◽  
Performance Computing

scholarly journals IDEA OF ARCHITECTURE OF A MULTIPROCESSOR COMPUTER M-10 AND THEIR IMPLEMENTATION IN MODERN COMPUTING PLATFORMS (IN SMALL FORMS)

2019 ◽  
pp. 28-31
Author(s):  
E. V. Glivenko ◽  
S. А. Sorokin ◽  
G. N. Petrovа
Keyword(s):  
Parallel Processing ◽  
High Performance Computing ◽  
High Performance ◽  
Multiprocessor Computer ◽  
M 10 ◽  
Operator Type ◽  
Computing Platforms ◽  
Main Ideas ◽  
New Generation ◽  
Performance Computing

The article is devoted to the design of high‑performance computing devices for parallel processing of information. The problem of  increasing the productivity of computing facilities by one or several orders of magnitude is considered on the example of the high‑ performance electronic computer M‑10, which was created in the 1970s at the NIIVK. If in a conventional computer, the method  of processing numbers is given by commands, then in M‑10, the methods for processing a function were specified by operators  taken from functional analysis. At the same time, the possibility of parallel processing of an entire information line appeared. Such  systems began to be called «functional operator type machines». The main ideas presented in the article may be of interest to  developers of specialized machines of the new generation, as well as engineers involved in the creation of high‑performance  computing devices using technologies of computing platforms.

On Construction of Cluster and Grid Computing Platforms for Parallel Bioinformatics Applications

2012 ◽  
pp. 841-861
Author(s):  
Chao-Tung Yang ◽  
Wen-Chung Shih
Keyword(s):  
Grid Computing ◽  
High Performance Computing ◽  
High Speed ◽  
High Performance ◽  
Cluster Computing ◽  
Structural Features ◽  
Performance Technology ◽  
Data Intensive ◽  
Computing Platforms ◽  
Performance Computing

Biology databases are diverse and massive. As a result, researchers must compare each sequence with vast numbers of other sequences. Comparison, whether of structural features or protein sequences, is vital in bioinformatics. These activities require high-speed, high-performance computing power to search through and analyze large amounts of data and industrial-strength databases to perform a range of data-intensive computing functions. Grid computing and Cluster computing meet these requirements. Biological data exist in various web services that help biologists search for and extract useful information. The data formats produced are heterogeneous and powerful tools are needed to handle the complex and difficult task of integrating the data. This paper presents a review of the technologies and an approach to solve this problem using cluster and grid computing technologies. The authors implement an experimental distributed computing application for bioinformatics, consisting of basic high-performance computing environments (Grid and PC Cluster systems), multiple interfaces at user portals that provide useful graphical interfaces to enable biologists to benefit directly from the use of high-performance technology, and a translation tool for converting biology data into XML format.

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