The main aim of this thesis is the exploration of the potential use of the parallelism of numerical computations in the field of nonlinear dynamics. In the last decade the dramatic onset of multicore and multi-processor systems in combination with the possibilities which now provide modern computer networks has risen. The complexity and size of the investigated models are constantly increasing due to the high computational complexity of computational tasks in dynamics and statics of structures, mainly because of the nonlinear character of the solved models. Any possibility to speed up such calculation procedures is more than desirable. This is a relatively new branch of science, therefore specific algorithms and parallel implementation are still in the stage of research and development which is attributed to the latest advances in computer hardware, which is growing rapidly. More questions are raised on how best to utilize the available computing power. The proposed parallel model is based on the explicit form of the finite element method, which naturaly provides the possibility of efficient parallelization. The possibilities of multicore processors, as well as parallel hybrid model combining both the possibilities of multicore processors, and the form of the parallelism on a computer network are investigated. The designed approaches are then examined in addressing of the numerical analysis regarding contact/impact phenomena of shell structures.

This thesis deals with model-based approach to 3D tracking from monocular video. The 3D model pose dynamically estimated through minimization of objective function by particle filter. Objective function is based on rendered scene to real video similarity.

This thesis represents master's thesis focused on acceleration of Genetic algorithms using GPU. First chapter deeply analyses Genetic algorithms and corresponding topics like population, chromosome, crossover, mutation and selection. Next part of the thesis shows GPU abilities for unified computing using both DirectX/OpenGL with Cg and specialized GPGPU libraries like CUDA. The fourth chapter focuses on design of GPU implementation using CUDA, coarse-grained and fine-grained GAs are discussed, and completed by sorting and random number generation task accelerated by GPU. Next chapter covers implementation details -- migration, crossover and selection schemes mapped on CUDA software model. All GA elements and quality of GPU results are described in the last chapter.