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Web Interface for Task Management and Monitoring on Supercomputers
Dančák, Petr ; Jaroš, Jiří (referee) ; Jaroš, Marta (advisor)
Bachelor's thesis focuses on web development process to create interface for k-Dispatch system, with which we can monitor, create and edit existing tasks, users, machines, allocations and groups. Web design was created based on web content layout principle and object grouping based on similarities principle. Comparison of usable tools is in design procedure section. Implementation uses mainly written in Python3 language with usage of Flask framework, which is helpful with processing requests and user authentication. Furthermore in implementation are used languages as HTML, JavaScript, Jinja2 and other. For test phase were used unit tests together with Selenium interface and user testing.
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Web Interface for Task Management and Monitoring on a Supercomputer
Bukovinský, Denis ; Jaroš, Jiří (referee) ; Jaroš, Marta (advisor)
One of the goals of the project was to complete the web interface for applications that access the k-Dispatch database, which monitors both running and scheduled tasks on a supercomputer. Another goal was to create a web-based graphical interface for the system administrator using already implemented web interface. Using this graphical interface, the administrator can manage and supervise the system. After studying the web interface specification, I implemented the required functionality that I integrated into the k-Dispatch system. The admin interface will include functionality based on the use-case diagrams that I created from the Web interface specification. This interface uses the k-Dispatch web interface to access the database. The web interface was implemented in Python using microframeworks. The admin part of the application consists of dynamic web pages created using HTML, cascading styles and JavaScript. The server generates these dynamic pages using pre-created templates.
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Optimization of Run Configurations of k-Wave Jobs
Sasák, Tomáš ; Jaroš, Marta (referee) ; Jaroš, Jiří (advisor)
This thesis focuses on scheduling, i.e. correct approximation of configurations used to run k-Wave simulations on supercomputers from the IT4Innovations infrastructure. Especially, for clusters Salomon and Anselm. A single work is composed of a set which contains many simulations. Every simulation is executed by some code from the k-Wave toolbox. To calculate the simulation, it is necesarry to select a suitable configuration, which means the amount of supercomputer resources (number of nodes, i.e. cores), and the duration of the rental. Creation of an ideal configuration is complicated and is even harder for an inexperienced user. The approximation is made based on the empiric data, obtained from multiple executions of different sets of simulations on given clusters. This data is stored and used by a set of approximators, which performs the actual approximation by methods of interpolation and regression. The text describes the implementation of the final scheduler. By experimenting, the most efficient methods for this problem has found out to be Akima spline, PCHIP interpolation and cubic spline. The main contribution of this work is creation of a tool which can find suitable configuration for k-Wave simulation without knowing the code or having lots of experience with its usage.
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Acceleration of Applications on a Supercomputer Using Python
Čelka, Marek ; Jaroš, Jiří (referee) ; Jaroš, Marta (advisor)
Nowadays, all computers we use are capable of parallel processing that saves time in compute-intensive tasks such as scientific computations, various simulations or predictions. The theme of this thesis is acceleration of compute-intensive tasks on supercomputer. This is achieved by the parallelization of the problem. For better understanding the issue by scientists from diverse scientific fields, the python programming language was chosen. Python is very powerful and easy to use as well. The first part of the thesis deals with the parallel processing techniques. The set of microtests was designed and implemented for this purpose. Results are then discussed and used in the further work. The second part of the thesis deals with the problem of parallel image reconstruction. For a comparison, the sequential version of the problem was also implemented. Both versions, sequential and parallel, were tested on a set of images of a different size. Experiments focus on acceleration, spent time, memory bandwidth and latency. These outcomes are also presented and discussed.
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Acceleration of Python Applications on GPU
Turcel, Matej ; Jaroš, Jiří (referee) ; Jaroš, Marta (advisor)
Compiled languages, such as C++, are conventionally used in the field of high performance computing (HPC). However, scripting languages like Python are more convenient and application development is quicker and simpler in these languages. This work compares C++ and Python in terms of the possibilities of computation acceleration on graphics card. Its aim is to show that scripting languages are also suitable for the implementation of HPC applications, and point out their advantages and disadvantages compared to compiled languages. To this purpose, a number of programs have been implemented. Several smaller programs for testing purposes and a larger one, implementing a computationally intensive problem. The implementations of these programs in C++ and Python are compared in terms of performance, as well as difficulty of implementation.
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Acceleration of Algorithms for Clustering of Tunnels in Proteins
Jaroš, Marta ; Vašíček, Zdeněk (referee) ; Martínek, Tomáš (advisor)
This thesis deals with the clustering of tunnels in data obtained from the protein molecular dynamics simulation. This process is very computationaly intensive and it has been a challenge for scientific communities. The goal is to find such an algorithm with optimal time and space complexity ratio. The research of clustering algorithms, work with huge highdimensional datasets, visualisation and cluster-comparing methods are discussed. The thesis provides a proposal of the solution of this problem using the Twister Tries algorithm. The implementation details are analysed and the testing results of the solution quality and space complexity are provided. The goal of the thesis was to prove that we could achieve the same results with a stochastic algorithm - Twister Tries , as with an exact algorithm ( average-linkage ). This assumption was not confirmed confidently. Another finding of the hashing functions analysis shows that we could obtain the same results of hashing with a low dimensional hashing function but in much better computational time.
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Visualization of DNA Secondary Structures in R/Bioconductor
Jaroš, Marta ; Bendl, Jaroslav (referee) ; Martínek, Tomáš (advisor)
This bachelors thesis deals with the visualization techniques of the secondary structures of DNA. It summarizes and discusses some of the current methods of visualization. It explains the problem and its importance from the standpoint of molecular biology. The main aim of the work is the design of a generic algorithm for visualization of secondary structures of DNA, specifically palindromes and triplexes, and its implementation into the environment R/Bioconductor. It focuses in particular on the area of 2D graphical imaging of these secondary structures of DNA. The result of the work is 2D visualization support for R/Bioconductors software packages which provide searching of the characteristic sequence of palindromes and triplexes in DNA sequences.
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