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  • With the appearance of modern general purpose graphical processor units (GPU), a powerful and cheap architecture has entered the field of scientific computation. This highly parallel architecture, formerly designed for floating point graphical operation acceleration, is now being used for the acceleration of<br>various algorithms. <br><br>During the past few years, various papers dealing with the utilization of GPUs in general purpose computing have been published. Even evolutionary algorithms have been accelerated [1, 3], among them genetic programming and its variants. In order to achieve maximal performance of genome evaluation, various approaches of candidate solution evaluation have been proposed. The genome can be evaluated as a program which can be directly downloaded into the GPU [1] or interpreted by using an interpreter program running on the GPU [2]. Due to the architectural limitations, the second method appears to be more promising in comparison with the previous one.<br><br>The GPUs are
  • With the appearance of modern general purpose graphical processor units (GPU), a powerful and cheap architecture has entered the field of scientific computation. This highly parallel architecture, formerly designed for floating point graphical operation acceleration, is now being used for the acceleration of<br>various algorithms. <br><br>During the past few years, various papers dealing with the utilization of GPUs in general purpose computing have been published. Even evolutionary algorithms have been accelerated [1, 3], among them genetic programming and its variants. In order to achieve maximal performance of genome evaluation, various approaches of candidate solution evaluation have been proposed. The genome can be evaluated as a program which can be directly downloaded into the GPU [1] or interpreted by using an interpreter program running on the GPU [2]. Due to the architectural limitations, the second method appears to be more promising in comparison with the previous one.<br><br>The GPUs are (en)
Title
  • Can the performance of GPGPU really beat CPU in evolutionary design task?
  • Can the performance of GPGPU really beat CPU in evolutionary design task? (en)
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  • Can the performance of GPGPU really beat CPU in evolutionary design task?
  • Can the performance of GPGPU really beat CPU in evolutionary design task? (en)
skos:notation
  • RIV/00216305:26230/08:PU78069!RIV10-MSM-26230___
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  • 358771
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  • RIV/00216305:26230/08:PU78069
http://linked.open...riv/jazykVysledku
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  • GPU, CUDA, CGP, acceleration<br> (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [856E05F29E5B]
http://linked.open...v/mistoKonaniAkce
  • Znojmo
http://linked.open...i/riv/mistoVydani
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  • 4th Doctoral Workshop on Mathematical and Engineering Methods in Computer Science
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http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Vašíček, Zdeněk
  • Šimek, Václav
  • Slaný, Karel
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
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  • Masaryk University
https://schema.org/isbn
  • 978-80-7355-082-0
http://localhost/t...ganizacniJednotka
  • 26230
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