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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 Accelerated Optimisation of the Water Management Systems
Marek, Jan ; Petrlík, Jiří (referee) ; Jaroš, Jiří (advisor)
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.
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Deep Neural Networks Approximation
Stodůlka, Martin ; Mrázek, Vojtěch (referee) ; Vaverka, Filip (advisor)
The goal of this work is to find out the impact of approximated computing on accuracy of deep neural network, specifically neural networks for image classification. A version of framework Caffe called Ristretto-caffe was chosen for neural network implementation, which was extended for the use of approximated operations. Approximated computing was used for multiplication in forward pass for convolution. Approximated components from Evoapproxlib were chosen for this work.
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Methods for Network Traffic Classification
Jacko, Michal ; Ovšonka, Daniel (referee) ; Barabas, Maroš (advisor)
This paper deals with a problem of detection of network traffic anomaly and classification of network flows. Based on existing methods, paper describes proposal and implementaion of a tool, which can automatically classify network flows. The tool uses CUDA platform for network data processing and computation of network flow metrics using graphics processing unit. Processed flows are subsequently classified by proposed methods for network anomaly detection.
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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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Interactive Cloth Simulation Accelerated by GPU
Melichar, Vojtěch ; Klepárník, Petr (referee) ; Jaroš, Jiří (advisor)
This master thesis deals with interactive cloth simulation accelerated by GPU. In the first part there is a description of all technologies used during implementation of a program. The second part discusses various simulation methods. It is mainly focused on particle systems as a most used method. These parts are followed by a design of the program, which is implemented as a part of this thesis. The program was implemented in four variants. The first variant is CPU implementation, which was then optimalized with OpenMP. CUDA implementation is based on these implementations. Last variant implemented in this thesis is optimized CUDA implementation. All these implementations are evaluated from compute complexity point of view and suitability for real time graphics.
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Acceleration of Ultrasound Simulations on Multi-GPU Systems
Stodůlka, Martin ; Vaverka, Filip (referee) ; Jaroš, Jiří (advisor)
The main focus of this project is usage of multi - GPU systems and usage of CUDA unified memory . Its goal is to accelerate computation of 2D and 3D FFT, which is the main part of simulations in k- Wave library .K- Wave is a C++/ Matlab library used for simulations of propagation of ultrasonic waves in 1D , 2D or 3D space . Acceleration of these functions is necessary , because the simulations are computationally intensive .
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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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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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Recurrent Neural Network for Text Classification
Myška, Vojtěch ; Kolařík, Martin (referee) ; Povoda, Lukáš (advisor)
Thesis deals with the proposal of the neural networks for classification of positive and negative texts. Development took place in the Python programming language. Design of deep neural network models was performed using the Keras high-level API and the TensorFlow numerical computation library. The computations were performed using GPU with support of the CUDA architecture. The final outcome of the thesis is linguistically independent neural network model for classifying texts at character level reaching up to 93,64% accuracy. Training and testing data were provided by multilingual and Yelp databases. The simulations were performed on 1200000 English, 12000 Czech, German and Spanish texts.
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