This thesis investigates violent air-water flows in two and three dimensions using a smoothed particle hydrodynamics (SPH) model accelerated using the parallel architecture of graphics processing units (GPUs). SPH is a meshless Lagrangian technique for CFD simulations, whose major advantage for multi-phase flows is that the highly nonlinear behaviour of the motion of the interface can be implicitly captured with a sharp interface. However, prior to this thesis performing multi-phase simulations of large scale air-water flows has been prohibitive due to the inherent high computational cost. The open source code DualSPHysics, a hybrid central processing unit (CPU) and GPU code, is heavily modified in order to be able to handle flows with multiple fluids by implementing a weakly compressible multi-phase model that is simple to implement on GPUs. The computational runtime shows a clear improvement over a conventional serial code for both two- and three dimensional cases enabling simulations with millions of particles. An investigation into different GPU algorithms focuses on optimising the multi-phase SPH implementation for the first time, leading to speedups of up to two orders of magnitude compared to a CPU-only simulation. Detailed comparison of different GPU algorithms reveals a further 12% improvement on the computational runtime. Enabling the modelling of cases with millions of fluid particles demonstrates some previously unreported problems regarding the simulation of the air phase. A new particle shifting algorithm has been proposed for multi-phase flows enabling the air, initially simulated as a highly compressible liquid, to expand rapidly as a gas and prevent the formation of unphysical voids. The new shifting algorithm is validated using dam break flows over a dry bed where good agreement is obtained with experimental data and reference solutions published in the literature. An improvement over a corresponding single-phase SPH simulation is also shown. Results for dam break flows over a wet bed are shown for different resolutions performing simulations that were unfeasible prior to the GPU multi-phase SPH code. Good agreement with the experimental results and a clear improvement over the single-phase model are obtained with the higher resolution showing closer agreement with the experimental results. Sloshing inside a rolling tank was also examined and was found to be heavily dependent on the viscosity model and the speed of sound of the phases. A sensitivity analysis was performed for a range of different values comparing the results to experimental data with the emphasis on the pressure impact on the wall. Finally, a 3-D gravity-driven flow where water is impacting an obstacle was studied comparing results with published experimental data. The height of the water at different points in the domain and the pressure on the side of the obstacle are compared to a state-of-the-art single-phase GPU SPH simulation. The results obtained were generally in good agreement with the experiment with closer results obtained for higher resolutions and showing an improvement on the single-phase model.

It is estimated that mind-boggling 14.1 million new cases of cancer occurred worldwide in 2012 alone. This number is alarming. Although healthy lifestyle may reduce a risk of developing cancer, there is always some probability that cancer would develop even in an absolutely fit individual. There are two main conditions for successful treatment of cancer. Firstly, early diagnostic is absolutely crucial. Secondly, there is a need for suitable surgical methods for affected tissue removal. Ultrasound has a great potential to be used for both purposes as a non-invasive method. Photoacoustic spectroscopy is imaging method for tumor detection of great properties making the use of ultrasound while High-Intensity Focused Ultrasound (HIFU) is non-invasive surgical method. These methods would be impossible without precise ultrasound propagation simulations. The k-Wave is an open source MATLAB toolbox implementing such simulations. So, why are not these methods already deployed in treatment? Unfortunately, the simulation of ultrasound propagation is a very time consuming task, which makes it ineffective for medical purposes. However, there are a few options how to accelerate these simulations. The use of GPU is a very promising way to accelerate simulation.   The main topic of this thesis is the acceleration of the simulation of soundwaves propagation in bones and hard tissue. The implementation developed as a part of this thesis was benchmarked on various supercomputers including Anselm in Ostrava and Piz Daint in Lugano. The implemented solution provides remarkable acceleration compared to the original MATLAB prototype. It was able to accelerate the simulation around 160 times in the best case. It means that the simulation, which would otherwise last for 6.5 days, can be now computed in one hour. This acceleration was achieved using an NVIDIA Tesla P100 to run the simulation with the domain size of 416x416x416 grid points. The thesis includes performance benchmarks on different GPUs to provide complex image acceleration capabilities of developed implementation and provides discussion about memory usage and numerical accuracy. Thanks to the implemented solution harnessing the power of modern GPUs, doctors and researchers all around the world have a powerful tool in hands.

This paper deals with a problem of detection of network traffic anomaly and classification of network flows. Based on existing methods, paper describes proposal and implementaion of a tool, which can automatically classify network flows. The tool uses CUDA platform for network data processing and computation of network flow metrics using graphics processing unit. Processed flows are subsequently classified by proposed methods for network anomaly detection.

The main objective of this research is to propose optimization of the defect detection algorithm in the production of nonwoven textile. The algorithm was developed by CAMEA spol. s.r.o. As a consequence of upgrading the current camera system to a more powerful one, it will be necessary to optimize the current algorithm and choose the hardware with the appropriate architecture on which the calculations will be performed. This work will describe a usefull programming techniques of CUDA software architecture and OpenCL framework in details. Using these tools, we proposed to implement a parallel equivalent of the current algorithm, describe various optimization methods, and we designed a GUI to test these methods.

Diploma thesis is focused on real-time 4K video encoding using NVENC technology. First chapter describes the most used video codecs H.264 and HEVC. There is an explanation of the principle of graphic cards and their programmable units. Analysis of the solution of open source Video Codec SDK is also part of the thesis. The main focus of the thesis is an implementation of an application which can handle 4K video encoding from multiple cameras in real time. Performance and qualitative tests were performed for application. Results of these tests were analyzed and discussed.

This thesis explores potential use of GPGPU libraries to accelerate k-Wave toolkit's acoustic wave propagation simulation. Firstly, the thesis researches and assesses available high level GPGPU libraries. Afterwards, an insight into k-Wave toolkit's current state of simulation acceleration is provided. Based on that, an approach to enhance currently available code for processors into a heterogeneous application, that is capable of being run on graphics card, is proposed. The outcome of this thesis is an application that can utilize graphics card. If graphics card is unavailable, a fallback into thread and SIMD based acceleration for processor is executed. The product of this thesis is then evaluated based on its performance, maintenance difficulty and usability.

This work deals with parallel computing on modern processors - multi-core CPU and GPU. The goal is to learn about computing on this devices suitable for parallelization, define their advantages and disadvantages, test their properties in examples and select appropriate tools to implement a library for parallel image processing. This library is going to be used for the vanishing point estimation in the path finding mobile robot.

This work deals with the PSO technique (Particle Swarm Optimization), which is capable to solve complex problems. This technique can be used for solving complex combinatorial problems (the traveling salesman problem, the tasks of knapsack), design of integrated circuits and antennas, in fields such as biomedicine, robotics, artificial intelligence or finance. Although the PSO algorithm is very efficient, the time required to seek out appropriate solutions for real problems often makes the task intractable. The goal of this work is to accelerate the execution time of this algorithm by the usage of Graphics processors (GPU), which offers higher computing potential while preserving the favorable price and size. The boolean satisfiability problem (SAT) was chosen to verify and benchmark the implementation. As the SAT problem belongs to the class of the NP-complete problems, any reduction of the solution time may broaden the class of tractable problems and bring us new interesting knowledge.

Subject of this thesis is optimalization of storage function of water management system. The work is based on dissertation thesis of Ing. Pavel Menšík Ph.D. Automatization of   storage function of water management system. As optimalization method was chosen diferential evolution. Sequential version of the method will be implemented as a first step, followed by CPU accelerated and   GPU accelerated versions.

This work contains a few basic terms of analytical geometry. We mention some of ray-triangle intersection computation algorithms and present some use-case examples. We discuss capabilities of CUDA, optimization techniques of this architecture and implementation with focus on given issues. Algorithms of ray-triangle intersection are tested and results are discussed.