This diploma thesis is concerned with research on available methods of plagiarism detection and then with design and implementation of such detector. Primary aim is to detect plagiarism within academic works or theses issued at BUT. The detector uses sophisticated preprocessing algorithms to store documents in its own corpus (document database). Implemented comparison algorithms are designed for parallel execution on graphical processing units and they compare a single subject document against all other documents within the corpus in the shortest time possible, enabling near real-time detection while maintaining acceptable quality of output.

This master's thesis deals with acceleration of geometrical image transforms using the GPU and NVIDIA (R) CUDA TM architecture. Time critical parts of the code are moved on the GPU and executed in parallel. One of the results is a demonstrational application for performance comparison of both architectures: the CPU, and GPU in combination with the CPU. As a reference implementation, there are used highly optimized routines from the OpenCV library, made by the Intel company.

Cílem této práce je návrh nové techniky pro stabilizaci obrazu za pomoci hardwarové akcelerace prostřednictvím GPGPU. Využití této techniky umožnuje stabilizaci videosekvencí v reálném čase i pro video ve vysokém rozlišení. Toho je zapotřebí pro ulehčení dalšího zpracování v počítačovém vidění nebo v armádních aplikacích. Z důvodu existence vícerých programovacích modelů pro GPGPU je navrhnutý stabilizační algoritmus implementován ve třech nejpoužívanějších z nich. Jejich výkon a výsledky jsou následně porovnány a diskutovány.

This thesis describes selected methods of brain connectivity analysis. It was created an application, as a part of this thesis - plugin for evaluating relationships and dependencies between signals calculated as Pearson correlation coefficients. Computation of these coefficients is accelerated by GPU.

Title: Text mining in social network analysis Author: Bc. Michal Hušek Department: Department of Theoretical Computer Science and Mathematical Logic Supervisor: doc. RNDr. Iveta Mrázová, CSc., Department of Theoretical Computer Science and Mathematical Logic Abstract: Nowadays, social networks represent one of the most important sources of valuable information. This work focuses on mining the data provided by social networks. Multiple data mining techniques are discussed and analysed in this work, namely, clustering, neural networks, ranking algorithms and histogram statistics. Most of the mentioned algorithms have been implemented and tested on real-world social network data and the obtained results have been mutually compared against each other whenever it made sense. For computationally demanding tasks, graphic processing units have been used in order to speed up calculations for vast amounts of data, e.g., during clustering. The performed tests have confirmed lower time requirements. All the performed analyses are, however, independent of the actually involved type of social network. Keywords: data mining, social networks, clustering, neural networks, ranking algorithms, CUDA

In the past decade we have witnessed an abrupt shift to parallel computing subsequent to the increasing demand for performance and functionality that can no longer be satisfied by conventional paradigms. As a consequence, the abstraction gab between the applications and the underlying hardware increased, triggering both industry and academia in several research directions. This thesis project aims at analyzing some of these directions in order to offer a solution for bridging the abstraction gap between the description of a problem at a functional level and the implementation on a heterogeneous parallel platform using ForSyDe – a formal design methodology. This report treats applications employing data-parallel and time-parallel computation, regards nvidia CUDA-enabled GPGPUs as the main backend platform. The report proposes a heuristic transformation-and-refinement process based on analysis methods and design decisions to automate and aid in a correct-by-design backend code synthesis. Its purpose is to identify potential data parallelism and time parallelism in a high-level system. Furthermore, based on a basic platform model, the algorithm load-balances and maps the execution onto the best computation resources in an automated design flow. This design flow will be embedded into an already existing tool, f2cc (ForSyDe-to-CUDA C) and tested for correctness on an industrial-scale image processing application aimed at monitoring inkjet print-heads reliability.

Purpose – The purpose of this study is partly to illustrate how the AES encryption methods ECB and CTR affect the computational speed when using the GPGPU framework Cuda, but also to clarify the advantages and disadvantages between the different AES encryption modes. Method – A preliminary study was conducted to obtain empirical data on the AES encryption modes ECB and CTR. Data from the study has been analyzed and compared to determine the various aspects of the AES encryption modes and to create a basis for determining the advantages and disadvantages between them. The preliminary study has been carried out systematically by finding scientific works by searching databases within the subject. An experiment has been used as a method to be able to extract execution time data for the GPGPU framework Cuda when processing the AES encryption modes. Experiment were chosen as a method to gain control over the variables included in the study and to see how these variables change when they are consciously influenced. Findings – The findings of the preliminary study show that CTR is more secure than the ECB, but also considerably more complex, which can lead to integrity risks when implementation is done incorrectly. In the experiment, computational speeds are produced when the CPU memory sends to the GPU memory, the encryption on the GPU and how long it takes for the GPU memory to send to the CPU memory. This is done for both CTR and ECB in encryption and decryption. The result of the analysis shows that the ECB is faster than CTR in encryption and decryption. The calculation speed is higher with the ECB compared to the CTR. Implications – The experiment shows that CTR is slower than the ECB. But the most amount of time spent in encryption for both modes are the transfers between the CPU memory and the GPU memory. Limitations – The file sizes of the files tested only goes up to about 1 gigabyte which gave small computation times.

This work deals with diffrential equations, with the possibility     of using them for analysis of the line and the possibility     of accelerating the computations in GPU using nVidia CUDA.

This master's thesis deals with acceleration of pixel interpolation methods using the GPU and NVIDIA (R) CUDA TM architecture. Graphic output is represented by a demonstrational application for geometrical image transforms using chosen interpolation method. Time critical parts of the code are moved on the GPU and executed in parallel. There are used highly optimized routines from the OpenCV library, made by the Intel company for an image and video processing.

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