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  • Homogenization methods are becoming the most popular approach to modelling of heterogeneous materials. The main principle is to represent the heterogeneous microstructure with an equivalent homogeneous material. When dealing with the complex random microstructures, the unit cell representing exactly periodic morphology needs to be replaced by a statistically equivalent periodic unit cell (SEPUC) preserving the important materiál properties in the statistical manner. One of the statistical descriptors suitable for SEPUC definition is the lineal path function. It is a low-order descriptor based on a more complex fundamental function able to capture certain information about the phase connectedness. Its main disadvantage is the computational cost. In this contribution, we present the reformulation of the sequential C code for evaluation of the lineal path function into the parallel C code with Compute Unified Device Architecture (CUDA) extensions enabling the usage of computational potential of the NVIDIA graphics processing unit (GPU).
  • Homogenization methods are becoming the most popular approach to modelling of heterogeneous materials. The main principle is to represent the heterogeneous microstructure with an equivalent homogeneous material. When dealing with the complex random microstructures, the unit cell representing exactly periodic morphology needs to be replaced by a statistically equivalent periodic unit cell (SEPUC) preserving the important materiál properties in the statistical manner. One of the statistical descriptors suitable for SEPUC definition is the lineal path function. It is a low-order descriptor based on a more complex fundamental function able to capture certain information about the phase connectedness. Its main disadvantage is the computational cost. In this contribution, we present the reformulation of the sequential C code for evaluation of the lineal path function into the parallel C code with Compute Unified Device Architecture (CUDA) extensions enabling the usage of computational potential of the NVIDIA graphics processing unit (GPU). (en)
Title
  • Computationally efficient algorithms for evaluation of statistical descriptors
  • Computationally efficient algorithms for evaluation of statistical descriptors (en)
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  • Computationally efficient algorithms for evaluation of statistical descriptors
  • Computationally efficient algorithms for evaluation of statistical descriptors (en)
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  • RIV/68407700:21110/12:00193935!RIV13-GA0-21110___
http://linked.open...avai/predkladatel
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  • P(GAP105/12/1146)
http://linked.open...vai/riv/dodaniDat
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  • 128300
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  • RIV/68407700:21110/12:00193935
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  • lineal path function; homogenization; statistically equivalent periodic unit cell; graphics processing unit. (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [4D81E95D837F]
http://linked.open...v/mistoKonaniAkce
  • Svratka
http://linked.open...i/riv/mistoVydani
  • Prague
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  • Engineering Mechanics 2012
http://linked.open...in/vavai/riv/obor
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http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
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  • Havelka, Jan
  • Kučerová, Anna
  • Sýkora, Jan
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
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  • Ústav teoretické a aplikované mechaniky AV ČR
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  • 978-80-86246-40-6
http://localhost/t...ganizacniJednotka
  • 21110
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