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Scalable preprocessing of data using Hadoop tool
Marinič, Michal ; Šmirg, Ondřej (referee) ; Burget, Radim (advisor)
The thesis is concerned with scalable pre-processing of data using Hadoop tool which is used for processing of large volumes of data. In the first theoretical part it focuses on explaining of functioning and structure of the basic elements of Hadoop distributed file system and MapReduce methods for parallel processing. The latter practical part of the thesis describes the implementation of basic Hadoop cluster in pseudo-distributed mode for easy program-debugging, and also describes an implementation of Hadoop cluster in fully-distributed mode for simulation in practice.
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Optimization of the Distributed I/O Subsystem of the k-Wave Project
Vysocký, Ondřej ; Hrbáček, Radek (referee) ; Jaroš, Jiří (advisor)
This thesis deals with an effective solution of parallel writing of variable amounts of data on the Lustre file system. The work will be used by the k-Wave project designed for time domain acoustic and ultrasound simulations. Since the simulation is computationally and data intensive, the project requires to be implemented with libraries for parallel computig (Open MPI) and large data processing (HDF5) and it must run on a supercomputer. The application is implemented in C and uses previously mentioned libraries. The proper settings of the Lustre file system leads to the peak write bandwith of 2.5 GB/s that corresponds to a speedup factor of 5 compared to the reference settings. The data aggregation improved the write bandwidth by a factor of 3 compared to a naive version. Here, the achieved I/O bandwidth for certain block sizes hits the limits of the Anselm I/O subsytem (3GB/s).
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Parallelisation of Ultrasound Simulations Using Local Fourier Decomposition
Dohnal, Matěj ; Hrbáček, Radek (referee) ; Jaroš, Jiří (advisor)
This document introduces a brand new method of the 1D, 2D and 3D decomposition with the use of local Fourier basis, its implementation and comparison with the currently used global 1D domain decomposition. The new method was designed, implemented and tested primarily for future use in the simulation software called The k-Wave toolbox, but it can be applied in many other spectral methods. Compared to the global 1D domain decomposition, the Local Fourier decomposition is up to 3 times faster and more efficient thanks to lower inter-process communication, however it is a little inaccurate. The final part of the thesis discusses the limitations of the new method and also introduces best practices to use 3D Local Fourier decomposition to achieve both more speed and accuracy.
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Realization of supercomputer using graphic card
Jasovský, Filip ; Karásek, Jan (referee) ; Mašek, Jan (advisor)
This master´s thesis deals with realization of supercomputer using graphic card with CUDA technology. The theoretical part of this thesis describes the function and the possibility of graphic cards and desktop computers and processes taking place in the proces sof calculations on them. The practical part deals with creation system for calculations on the graphic card using the algorithm of artificial intelligence, more specifically artificial neural networks. Subsequently is the generated program used for data classification of large input data file. Finally the results are compared.
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