Sirvi Autor "Martens, Ott-Kaarel" järgi

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Causally Consistent Reversible Debugger for MPI Applications
(Tartu Ülikool, 2022) Martens, Ott-Kaarel; Vainikko, Eero, juhendaja; Kuhn, Stefan Hermann, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Writing programs for parallel computation is a process significantly more difficult than programming for sequential execution. Debugger tools are of use in multiple stages of software development including implementation, analysis and maintenance. Some sophisticated debuggers offer – in complement to generic debugging commands – reversible debugging commands, providing the ability to progress backwards in the program execution in some form. MPI (Message Passing Interface) is a widely used standard for developing parallel programs. In this thesis, the implementation of a causally debugger for MPI applications offering reversible debugging commands while being capable of maintaining causal consistency is presented. The debugger utilises a distributed independent checkpointing mechanism to record the execution of the MPI application and coordinated restore mechanism to support reversible debugging of the MPI application. To the best of the author’s knowledge, this is the first debugger for MPI implementing this kind of checkpointing mechanism to enable reversible debugging. The produced tool demonstrates the viability of this checkpoint-restore mechanism to enable reversible debugging for parallel computation.
Evolution of Topics in the Psychology Domain
(Tartu Ülikool, 2020) Martens, Ott-Kaarel; Barbu, Eduard, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Topic modeling is a set of statistical methods for modeling collections of discrete data such as text corpora. It is used as a text-mining tool to discover the hidden semantic structures in a text body. Latent Dirichlet Allocation, a particular method for topic modeling is a generative probabilistic model that models texts as a mixture of underlying topics. In this thesis, Latent Dirichlet Allocation is used on a large corpus of texts from the domain of psychology. A model with 100 topics is generated, and the resulting topics are labeled. The occurrence of the topics is analysed over a time span of 40 years. The