The thesis deals with the acceleration of backpropagation neural networks using graphics chips. To solve this problem it was used the OpenCL technology that allows work with graphics chips from different manufacturers. The main goal was to accelerate the time-consuming learning process and classification process. The acceleration was achieved by training a large amount of neural networks simultaneously. The speed gain was used to find the best settings and topology of neural network for a given task using genetic algorithm.

This work describes a relatively new technique designed to write highly parallel programs, that name is OpenCL. It is intended for both GPU and CPU and other parallel processors. Libraries used by the processor architecture, which includes a large number of small cores. These cores are not as comprehensive as conventional processors and is therefore suitable for calculations, which are many and they are simple. It is this property could, under certain conditions, accelerate the calculation of the hologram, namely the calculation of the optical field. While the calculation itself is simple, but the amount of processed data is large and therefore slow. The work also contain the basic concepts of explanation of optical and digital holography.

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 deals with the task of image scaling using GPU paralelization. Portion of text is devoted to signal processing and his affection of whole result including measuring it's quality. Describtion of the most important methods including super-resolution is given further in the text. An important part of this thesis is library implementing choosen methods with usage of paralelization on graphic chip. Achieved results of paralelization are demonstrated on set of speed tests.

This thesis deals with a population based stochastic optimization technique PSO (Particle Swarm Optimization) and its acceleration. This simple, but very effective technique is designed for solving difficult multidimensional problems in a wide range of applications. The aim of this work is to develop a parallel implementation of this algorithm with an emphasis on acceleration of finding a solution. For this purpose, a graphics card (GPU) providing massive performance was chosen. To evaluate the benefits of the proposed implementation, a CPU and GPU implementation were created for solving a problem derived from the known NP-hard Knapsack problem. The GPU application shows 5 times average and almost 10 times the maximum speedup of computation compared to an optimized CPU application, which it is based on.

This thesis provides an analysis of the application for evolutionary scheduling of collective communications. It proposes possible ways to accelerate the application using general purpose computing on graphics processing units (GPU). This work offers a theoretical overview of systems on a chip, collective communications scheduling and more detailed description of evolutionary algorithms. Further, the work provides a description of the GPU architecture and its memory hierarchy using the OpenCL memory model. Based on the profiling, the work defines a concept for parallel execution of the fitness function. Furthermore, an estimation of the possible level of acceleration is presented. The process of implementation is described with a closer insight into the optimization process. Another important point consists in comparison of the original CPU-based solution and the massively parallel GPU version. As the final point, the thesis proposes distribution of the computation among different devices supported by OpenCL standard. In the conclusion are discussed further advantages, constraints and possibilities of acceleration using distribution on heterogenous computing systems.

This thesis addresses the issue of rigid body simulation and possibilities of paralellization using GPU. It describes the basics necessary for implementation of basic physics engine for blocks and technologies which can be used for acceleration. In my thesis, I describe approach which allowed me to gradually accellerate physics simulation using OpenCL. Each significant change is described in its own section and includes measurement results with short summary.

This term project addresses the construction of kD tree acceleration structures and parallelization of this construction using GPU. At the beginning, there is an introduction of the reader into CUDA platform for parallel programming. There is a decription of generic principles as well as specific features that will be used in this thesis. Following that the reader is put into the issue of acceleration structures for Ray tracing. These structures are described and the kD tree acceleration structure and its variants are portrayed in detail. After that the analysis of chosen kD tree variant is broken down and the problems and issuse of its parallel implementation are adressed. As a part of implementation discription, there is a short descripton of CPU variant and detailed specifications of the CUDA kernels. The testing section brings the results of implementation in form of CPU vs GPU comparison, as well as evaluation of how much the metric set in design was fulfilled. In the end there is a summary of achieved goals and results followed by possible future improvements for the implementation.

This work is part of the k-Wave project, which is a toolbox designed for time ultrasound simulations in complex and heterogeneous media. The simulation functions are based on the k-space pseudospectral method. The goal of this work is to compute these simulations on graphics cards using local domain decompostion. Thanks to decomposition we could compute these simulations faster, and on larger data grids. The main goal of this work is to achieve efficiency and scalability.

Technical problems make impossible steadily increase processor's clock frequency. Their power are currently growing due to increasing number of cores. It brings need for new approaches in programming such parallel systems. This thesis shows how to use paralelism in digital signal processing. As an example, it will be presented here implementation of the Geortzel's algorithm using the processing power of the graphics chip.