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Automata Applied in Visual Arts
Albrecht, Kryštof ; Havel, Martin (referee) ; Meduna, Alexandr (advisor)
Tato práce představuje nový programovací jazyk, určený ke kompozici dvourozměrných vizuálních efektů. Jazyk je založen na upravené verzi celulárních automatů navržené pro kompozici. Hlavní platformou, kde efekty mají běžet, je herní engine Godot, kde jsou efekty realizovány pomocí fragment shaderů.
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Heat Diffusion Simulation on GPU
Hradecký, Michal ; Vašíček, Zdeněk (referee) ; Jaroš, Jiří (advisor)
This thesis deals with the simulation of heat diffusion in human tissues. The proposed algorithm uses a finite-difference time-domain method, which is applied on the governing equation describing the system. A modern graphics card is used to accelerate the simulation. The performance achieved on the GPU card is compared with the implementation exploiting a modern multicore CPU. The output of this thesis is a set of differently optimized algorithms targeted on NVIDIA graphics cards. The experimental results reveal that the use of shared memory is contraproductive and the best performance is achieved by a register based implementation. The overall speedup of 18.5 was reached when comparing a NVIDIA GeForce GTX 580 with a quad-core Intel Core i7 920 CPU. This nicely corresponds with the theoretical capabilities of both architectures.
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The Three-Dimensional Digital Imaging Methods for X-ray Computed Tomography and Digital Holographic Microscopy
Kvasnica, Lukáš ; Číp, Ondřej (referee) ; Štarha, Pavel (referee) ; Chmelík, Radim (advisor)
This dissertation thesis deals with the methods for processing image data in X-ray microtomography and digital holographic microscopy. The work aims to achieve significant acceleration of algorithms for tomographic reconstruction and image reconstruction in holographic microscopy by means of optimization and the use of massively parallel GPU. In the field of microtomography, the new GPU (graphic processing unit) accelerated implementations of filtered back projection and back projection filtration of derived data are presented. Another presented algorithm is the orientation normalization technique and evaluation of 3D tomographic data. In the part related to holographic microscopy, the individual steps of the complete image processing procedure are described. This part introduces the new orignal technique of phase unwrapping and correction of image phase damaged by the occurrence of optical vortices in the wrapped image phase. The implementation of the methods for the compensation of the phase deformation and for tracking of cells is then described. In conclusion, there is briefly introduced the Q-PHASE software, which is the complete bundle of all the algorithms necessary for the holographic microscope control, and holographic image processing.
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Extreme learning machines for time series prediction
Zmeškal, Jiří ; Rajnoha, Martin (referee) ; Burget, Radim (advisor)
Thesis is aimed at the possibility of utilization of extreme learning machines and echo state networks for time series forecasting with possibility of utilizing GPU acceleration. Such predictions are part of nearly everyone’s daily lives through utilization in weather forecasting, prediction of regular and stock market, power consumption predictions and many more. Thesis is meant to familiarize reader firstly with theoretical basis of extreme learning machines and echo state networks, taking advantage of randomly generating majority of neural networks parameters and avoiding iterative processes. Secondly thesis demonstrates use of programing tools, such as ND4J and CUDA toolkit, to create very own programs. Finally, prediction capability and convenience of GPU acceleration is tested.
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Acceleration of Python Applications on GPU
Turcel, Matej ; Jaroš, Jiří (referee) ; Jaroš, Marta (advisor)
Compiled languages, such as C++, are conventionally used in the field of high performance computing (HPC). However, scripting languages like Python are more convenient and application development is quicker and simpler in these languages. This work compares C++ and Python in terms of the possibilities of computation acceleration on graphics card. Its aim is to show that scripting languages are also suitable for the implementation of HPC applications, and point out their advantages and disadvantages compared to compiled languages. To this purpose, a number of programs have been implemented. Several smaller programs for testing purposes and a larger one, implementing a computationally intensive problem. The implementations of these programs in C++ and Python are compared in terms of performance, as well as difficulty of implementation.
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Visualization of Marked Cells of a Model Organism
Kubíček, Radek ; Kršek, Přemysl (referee) ; Herout, Adam (advisor)
This master thesis is focused on volumetric data rendering and on highlighting and visualization of the selected cells of the model organisms. These data are captured by a confocal deconvolution microscope. Input data form one large volumetric block containing separate slices. This data block is rendered by an applicable method and then are identified and visualized the cells marked by the GFP (Green Fluorescent Protein) process or by chlorophyle fluorescency. The principal aim of this work is to find out the preferably optimal effective method enabling this highlighting, most preferably working without a manual check. Due to the data structure, this ambition seems hardly realizable, so it suffices to find out a manual working method. The last step is to embed the results of this work into FluorCam application, the confocal deconvolution microscope data visualizer.
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Extreme learning machines for time series prediction
Zmeškal, Jiří ; Rajnoha, Martin (referee) ; Burget, Radim (advisor)
Thesis is aimed at the possibility of utilization of extreme learning machines and echo state networks for time series forecasting with possibility of utilizing GPU acceleration. Such predictions are part of nearly everyone’s daily lives through utilization in weather forecasting, prediction of regular and stock market, power consumption predictions and many more. Thesis is meant to familiarize reader firstly with theoretical basis of extreme learning machines and echo state networks, taking advantage of randomly generating majority of neural networks parameters and avoiding iterative processes. Secondly thesis demonstrates use of programing tools, such as ND4J and CUDA toolkit, to create very own programs. Finally, prediction capability and convenience of GPU acceleration is tested.
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Acceleration of Python Applications on GPU
Turcel, Matej ; Jaroš, Jiří (referee) ; Jaroš, Marta (advisor)
Compiled languages, such as C++, are conventionally used in the field of high performance computing (HPC). However, scripting languages like Python are more convenient and application development is quicker and simpler in these languages. This work compares C++ and Python in terms of the possibilities of computation acceleration on graphics card. Its aim is to show that scripting languages are also suitable for the implementation of HPC applications, and point out their advantages and disadvantages compared to compiled languages. To this purpose, a number of programs have been implemented. Several smaller programs for testing purposes and a larger one, implementing a computationally intensive problem. The implementations of these programs in C++ and Python are compared in terms of performance, as well as difficulty of implementation.
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Heat Diffusion Simulation on GPU
Hradecký, Michal ; Vašíček, Zdeněk (referee) ; Jaroš, Jiří (advisor)
This thesis deals with the simulation of heat diffusion in human tissues. The proposed algorithm uses a finite-difference time-domain method, which is applied on the governing equation describing the system. A modern graphics card is used to accelerate the simulation. The performance achieved on the GPU card is compared with the implementation exploiting a modern multicore CPU. The output of this thesis is a set of differently optimized algorithms targeted on NVIDIA graphics cards. The experimental results reveal that the use of shared memory is contraproductive and the best performance is achieved by a register based implementation. The overall speedup of 18.5 was reached when comparing a NVIDIA GeForce GTX 580 with a quad-core Intel Core i7 920 CPU. This nicely corresponds with the theoretical capabilities of both architectures.
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Visualization of Marked Cells of a Model Organism
Kubíček, Radek ; Kršek, Přemysl (referee) ; Herout, Adam (advisor)
This master thesis is focused on volumetric data rendering and on highlighting and visualization of the selected cells of the model organisms. These data are captured by a confocal deconvolution microscope. Input data form one large volumetric block containing separate slices. This data block is rendered by an applicable method and then are identified and visualized the cells marked by the GFP (Green Fluorescent Protein) process or by chlorophyle fluorescency. The principal aim of this work is to find out the preferably optimal effective method enabling this highlighting, most preferably working without a manual check. Due to the data structure, this ambition seems hardly realizable, so it suffices to find out a manual working method. The last step is to embed the results of this work into FluorCam application, the confocal deconvolution microscope data visualizer.
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