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Use of Heuristics for Password Recovery with GPU Acceleration
Gazdík, Peter ; Holkovič, Martin (referee) ; Hranický, Radek (advisor)
This thesis discusses various techniques to enhance the password recovery process with GPU acceleration. The first part introduces a Markov model and simple regular expressions. These techniques dramatically reduce the password space to be searched. This is based on observations of users and their use of letters in passwords. We propose the design of a parallel algorithm that combines both techniques. Last part of the thesis contains the results of experiments to prove benefits of Markov model.
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Acceleration of Data Compression with Parallel Architectures
Juránek, Luboš ; Tříska, Vít (referee) ; Šimek, Václav (advisor)
This bachelor thesis deals with the use of parallel architectures, in particular the GPU, for acceleration of selected lossless compression algorithms, based on a statistical method, and transformations, which change the entropy of the input data to achieve better compression ratio. In this work there are in theory summarized general information about parallel architectures and programming options for them, mainly using NVIDIA CUDA and OpenCL.
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The GPU Based Acceleration of Neural Networks
Šimíček, Ondřej ; Jaroš, Jiří (referee) ; Petrlík, Jiří (advisor)
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.
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High data rate image processing using CUDA/OpenCL
Sedláček, Filip ; Klečka, Jan (referee) ; Honec, Peter (advisor)
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.
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Parallel signal processing with help of GPU
Václavík, Jiří ; Frýza, Tomáš (referee) ; Mego, Roman (advisor)
In the introduction, the bachelor thesis outlines the origins of modern graphic processors. The theoretical part of the text describes the minimum of required information from parallel programming model essential to program simple DSP algorithms. The next part elaborate on three common DSP algorithms, finite impulse response filter, naive implementation of discrete cosine transform type II, and fast Fourier transform. To demonstrate parallel capability of GPU, algorithm for JPEG compression was chosen as JPEG compression is favorable because it illustrates both advantages and disadvantages of parallel data processing on GPU, and compromises needed to be considered.
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Face Detection in Video on GPU
Tesař, Martin ; Nečas, Ondřej (referee) ; Polok, Lukáš (advisor)
This work deals with task of face detection on graphic card. First part is the introduction to face detection methods focusing on detector proposed by Viola and Jones. Further, this work studies the possibilities of mapping detector's key parts on graphic card. Next part describes implementation details of designed application. The end of work include results and comparison with CPU approach. The last chapter summarizes the whole work and proposes future possibilities of development.
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Acceleration of Particle Swarm Optimization Using GPUs
Krézek, Vladimír ; Schwarz, Josef (referee) ; Jaroš, Jiří (advisor)
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.
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Hardware Acceleration Using Functional Languages
Hodaňová, Andrea ; Kadlček, Filip (referee) ; Fučík, Otto (advisor)
The aim of this thesis is to research how the functional paradigm can be used for hardware acceleration with an emphasis on data-parallel tasks. The level of abstraction of the traditional hardware description languages, such as VHDL or Verilog, is becoming to low. High-level languages from the domains of software development and modeling, such as C/C++, SystemC or MATLAB, are experiencing a boom for hardware description on the algorithmic or behavioral level. Functional Languages are not so commonly used, but they outperform imperative languages in verification, the ability to capture inherent paralellism and the compactness of code. Data-parallel task are often accelerated on FPGAs, GPUs and multicore processors. In this thesis, we use a library for general-purpose GPU programs called Accelerate and extend it to produce VHDL. Accelerate is a domain-specific language embedded into Haskell with a backend for the NVIDIA CUDA platform. We use the language and its frontend, and create a new backend for high-level synthesis of circuits in VHDL.
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GLSL Based Engine
Šlesár, Michal ; Karas, Matej (referee) ; Milet, Tomáš (advisor)
Creating a graphical application running on a GPU typically involves configuring the GPU, creating and configuring the required objects, and then implementing the application's behavior itself. The aim of this work is to create a tool that would automate this configuration using the OpenGL application interface. As a result, the user would not have to waste time configuring and could quickly create and prototype graphics applications. In addition, the created tool adds new functionality to the application that is not native or supported on the GPU, such as working with a mouse and keyboard.
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General-Purpose Computation Using Graphics Card
Boček, Michal ; Pospíchal, Petr (referee) ; Jaroš, Jiří (advisor)
This thesis describes the programming models OpenCL and CUDA for Parallel Programming adapters and in case of OpenCL even for other computing platforms. There was implemented the application which calculates the electric potential in the crystalline lattice. The algorithm was programmed using two technologies for the GPU - OpenCL and CUDA. Their computational time were compared together with computational time of the CPU.
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