Evolutionary algorithms are often used for hard optimization problems. Solving time of this problems is long, so we want effective parallelization for this algorithms. Unfortunately, classical methods of parallelization do not work very well in cases where the individual evaluations of problems take significantly different times. In this project, we will try to extend the evolu- tionary algorithm with interleaving generations, which offers a better use of computational resources than classical parallel evolutionary algorithms, by speculative evaluation. Speculative evaluation means the estimation of an in- dividual's fitness function and the prediction of the following steps, which we will use later in the case of a correct estimate. We compare the algorithm with speculative evaluation with the original version in a series of experi- ments and we look at the effect of accuracy in the speculative step on the performance of the algorithm. 1

Arimaa is a strategic board game for two players. It was designed with the aim that it will be hard to create a computer program that could defeat the best human players. In this thesis, we focus on the design of the static evaluation function for Arimaa. The purpose of a static evaluation function is to determine which player is leading in a given position and how significant the lead is. We have divided the problem into a few parts, which were solved separately. We paid most attention to the efficient recognition of important patterns on the board, such as goal threats. The basic element of the proposed evaluation function is mobility. For each piece, the number of steps that the piece would need to get to other places on the board is estimated. We also examined machine learning. We developed a new algorithm for learning a static evaluation function from expert games. An implementation of an Arimaa playing program, which demonstrates the proposed methods, is part of the thesis. Powered by TCPDF (www.tcpdf.org)

This thesis will address the problem of the parallel evaluation of algorithmic trading models based on multiple kernel support vector regression. Various approaches to parallelization of the evaluation of these models will be proposed and their suitability for highly parallel architectures, namely the Intel Xeon Phi coprocessor, will be analysed considering specifics of this coprocessor and also specifics of its programming. Based on this analysis a prototype will be implemented, and its performance will be compared to a serial and multi-core baseline pursuant to executed experiments. Powered by TCPDF (www.tcpdf.org)

Cílem této práce je vylepšit implementaci modulu pro mobilní aplikace pro výuku výslovnosti, najít místa vhodná pro optimalizaci a provést optimalizaci s cílem zvýšit přesnost, snížit čas zpracování a snížit paměťovou náročnost zpracování.

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.

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 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.

Ray tracing is widely used technique for realistic rendering of computer scenes. Its major drawback is time needed to compute the image, therefore it's usually parallelized. This thesis describes parallelization and ray tracing in general. It explains the possibility of how can be ray tracing parallelized as well as it defines the problems which may occur during the process. The result is parallel rendering application which uses selected ray tracing software and measurement of how successful this application is.

This thesis deals with different parallelizations of training procedure for artificial neural networks. The networks are trained as phoneme-state acoustic descriptors for speech recognition. Two effective parallelization strategies were implemented and compared. The first strategy is data parallelization, where the training is split into several POSIX threads. The second strategy is node parallelization, which uses CUDA framework for general purpose computing on modern graphic cards. The first strategy showed a 4x speed-up, while using the second strategy we observed nearly 10x speed-up. The Stochastic Gradient Descent algorithm with error backpropagation was used for the training. After a short introduction, the second chapter of this thesis shows the motivation and introduces the neural networks into the context of speech recognition. The third chapter is theoretical, the anatomy of a neural network and the used training method are discussed. The following chapters are focused on the design and implementation of the project, while the phases of the iterative development are described. The last extensive chapter describes the setup of the testing system and reports the experimental results. Finally, the obtained results are concluded and the possible extensions of the project are proposed.