Computing effective properties of random heterogeneous materials on heterogeneous parallel processors |
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| Authors: | Tiziano Leidi Giulio Scocchi Loris Grossi Simone Pusterla Claudio D’Angelo Jean-Philippe Thiran Alberto Ortona |
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| Affiliation: | 1. Institute of Computer Integrated Manufacturing for Sustainable Innovation (iCIMSI), University of Applied Sciences and Arts of Southern Switzerland, 6928 Manno, Switzerland;2. Institute of Electrical Engineering, École polytechnique fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland;1. National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China;2. NEC Laboratory, Innovation Plaza, Tsinghua Science Park 1 Zhongguancun East Road, Beijing 100084, China;1. Philips Research, 22335 Hamburg, Germany;2. Karlsruher Institut für Technologie, 76131 Karlsruhe, Germany;1. School of Mathematical Sciences and Fujian Provincial Key Laboratory on Mathematical Modeling & High Performance Scientific Computing, Xiamen University, Fujian 361005, China;2. Beijing Computational Science Research Center, Beijing 10084, China;3. Department of Mathematics, Wayne State University, Detroit, MI 48202, United States;1. Department of Mathematics, Xidian University, Xi’an, PR China;2. School of Information and Computation Science, The North University for Ethnics, Yinchuan, PR China;1. Monell Chemical Senses Center, 3500 Market Street, Philadelphia, PA 19104, USA;2. Institut de Mathématiques et de Sciences Physiques, B.P. 613, Porto-Novo, Benin;3. Nonlinear Dynamics, Chaos and Complex Systems Group, Departamento de Física, Universidad Rey Juan Carlos, Tulipán s/n, 28933 Móstoles, Madrid, Spain |
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| Abstract: | In recent decades, finite element (FE) techniques have been extensively used for predicting effective properties of random heterogeneous materials. In the case of very complex microstructures, the choice of numerical methods for the solution of this problem can offer some advantages over classical analytical approaches, and it allows the use of digital images obtained from real material samples (e.g., using computed tomography). On the other hand, having a large number of elements is often necessary for properly describing complex microstructures, ultimately leading to extremely time-consuming computations and high memory requirements. With the final objective of reducing these limitations, we improved an existing freely available FE code for the computation of effective conductivity (electrical and thermal) of microstructure digital models. To allow execution on hardware combining multi-core CPUs and a GPU, we first translated the original algorithm from Fortran to C, and we subdivided it into software components. Then, we enhanced the C version of the algorithm for parallel processing with heterogeneous processors. With the goal of maximizing the obtained performances and limiting resource consumption, we utilized a software architecture based on stream processing, event-driven scheduling, and dynamic load balancing. The parallel processing version of the algorithm has been validated using a simple microstructure consisting of a single sphere located at the centre of a cubic box, yielding consistent results. Finally, the code was used for the calculation of the effective thermal conductivity of a digital model of a real sample (a ceramic foam obtained using X-ray computed tomography). On a computer equipped with dual hexa-core Intel Xeon X5670 processors and an NVIDIA Tesla C2050, the parallel application version features near to linear speed-up progression when using only the CPU cores. It executes more than 20 times faster when additionally using the GPU. |
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