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Visualization of Large Volumetric Data on CPU
Dlabaja, Drahomír ; Milet, Tomáš (referee) ; Španěl, Michal (advisor)
This thesis deals with the problem of displaying volumetric data that exceeds the operating memory capacity of the machine. The work describes the design of a visualization pipeline, which consists of a data structure for large volumetric data and an algorithm that visualizes such data. The proposed hierarchical data structure accelerates sampling and allows the reduction of the total amount of data that needs to be loaded into physical memory during visualization. Visualization of processed data is achieved by the ray casting method with existing optimization techniques, such as empty space skipping and early ray termination. The data structure allows up to 12x faster sampling compared to the sampling of raw large volumetric data serialized by rows. Up to 150x faster visualization of large volumetric data in near-lossless mode has been achieved compared to the fully lossless mode by utilizing the data hierarchy. The display scheme is implemented in the form of a library in C++20 language. The implementation uses acceleration by vectorization and allows easy parallelization by the user. The library provides tools for processing and visualization of large volumetric data on the CPU.
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CPU Rendering of Large Volumetric Data
Svoboda, Jan ; Vlnas, Michal (referee) ; Španěl, Michal (advisor)
This thesis deals with design and implementation of a system that allows displaying large volumetric data in real time on the CPU of a conventional computer. The thesis aims to solve two biggest problems. Firstly, it aims to solve the problem with rendering itself, where this amount of data often cannot be placed into the main memory of a target computer. Secondly, it aims to solve the problem of storing of this data, where, in the case of large datasets, storing them in the storage of a target computer may not be desirable. The proposed solution contains two applications -- the server one and the client one. The server part is used as a remote storage of volumetric data that is provided to the client application in small blocks and in different qualities. The client application renders this data by the ray casting method and, according to the created strategies, performs loading and storing of required blocks in the local memory. In order to achieve high performance, the client application was implemented with an emphasis on parallelization of the main processes. The resulting system allows a user to display large datasets stored on a server's storage and to manage the datasets using a simple graphical user interface.
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Visualization of Large Volumetric Data on CPU
Dlabaja, Drahomír ; Milet, Tomáš (referee) ; Španěl, Michal (advisor)
This thesis deals with the problem of displaying volumetric data that exceeds the operating memory capacity of the machine. The work describes the design of a visualization pipeline, which consists of a data structure for large volumetric data and an algorithm that visualizes such data. The proposed hierarchical data structure accelerates sampling and allows the reduction of the total amount of data that needs to be loaded into physical memory during visualization. Visualization of processed data is achieved by the ray casting method with existing optimization techniques, such as empty space skipping and early ray termination. The data structure allows up to 12x faster sampling compared to the sampling of raw large volumetric data serialized by rows. Up to 150x faster visualization of large volumetric data in near-lossless mode has been achieved compared to the fully lossless mode by utilizing the data hierarchy. The display scheme is implemented in the form of a library in C++20 language. The implementation uses acceleration by vectorization and allows easy parallelization by the user. The library provides tools for processing and visualization of large volumetric data on the CPU.
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Mathematical analysis of equations describing the flow of compressible heat conducting fluids
Axmann, Šimon ; Pokorný, Milan (advisor) ; Feireisl, Eduard (referee) ; Novotný, Antonín (referee)
Title: Mathematical analysis of equations describing the flow of compressible heat conducting fluids Author: Šimon Axmann Department: Mathematical Institute of Charles University Supervisor: doc. Mgr. Milan Pokorný, Ph.D., Mathematical Institute of Charles University Abstract: The present thesis is devoted to the mathematical analysis of equa- tions describing the flow of viscous compressible newtonian fluid in various time regimes. In particular, we present existence results for three problems arising as special cases of a general model derived in the introductory part. The first chap- ter deals with time-periodic solutions to the full Navier-Stokes-Fourier system for heat-conducting fluid. The second chapter contains the proof of existence of steady solutions to a system arising from phase field model for two-phase com- pressible fluid. Finally, in the last section we study steady strong solutions to the Navier-Stokes equations under the additional assumption that the fluid is suffi- ciently dense. For each problem a different concept of the solution is considered, on the other hand in all cases an essential role is played by the crucial quantity effective viscous flux. Keywords: compressible Navier-Stokes system; weak solution; entropy variational solution; large data
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Framework for information extraction from the large language data sets
Kuboň, David ; Križ, Vincent (advisor) ; Bednárek, David (referee)
This thesis describes the FAFEFI program that focuses on n-gram and skip-gram extraction from large data sets. The thesis presents two different approaches to passing input data to the program. It also describes the design of data structures for n-gram and skip-gram representation within computer memory, the algorithm of n-gram and skip-gram extraction, memory-friendly options of saving extracted data and their final composition into output feature vectors. It also offers a variety of extra functions such as line filter and line modifier and a great deal of configurable parameters ranging from in-file separators to formatting the names of output files. Moreover, the program provides a differentiation in its activity by enabling saving data just after extraction from the train set and brings tools for cluster parallelization. Powered by TCPDF (www.tcpdf.org)
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