TY - THES A1 - Wende, Florian T1 - Dynamic Load Balancing on Massively Parallel Computer Architectures N2 - This thesis reports on using dynamic load balancing methods on massively parallel computers in the context of multithreaded computations. In particular we investigate the applicability of a randomized work stealing algorithm to ray tracing and breadth-first search as representatives of real-world applications with dynamic work creation. For our considerations we made use of current massively parallel hardware accelerators: Nvidia Tesla M2090, and Intel Xeon Phi. For both of the two we demonstrate the suitability of the work stealing scheme for the said real-world applications. Also the necessity of dynamic load balancing for irregular computations on such hardware is illustrated. N2 - Vorliegende Bachelorarbeit befasst sich mit Methoden der dynamischen Lastbalancierung auf massiv parallelen Computern im Rahmen von mehrprozess gestützten Ausführungen von Programmen. Im einzelnen wird die Eignung eines randomisierten Work-Stealing Algorithmus für die Ausführung realer Anwendungen mit dynamischer Arbeitserzeugung, wie Ray-Tracing und Breitensuche, untersucht. Für die entsprechenden Betrachtungen wer den aktuelle massiv parallele Hardwarebeschleuniger vom Typ Nvidia Tesla M2090 und Intel Xeon Phi verwendet. Für beide Beschleunigertypen konnte die Tauglichkeit des Work-Stealing Schemas für die genannten Anwendungen gezeigt werden. Ebenfalls wird die Notwendigkeit der Verwendung dynamischer Lastausgleichsmethoden für irreguläre Berechnungen auf der genannten Hardware verdeutlicht. Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-42166 ER - TY - THES A1 - Meitzner, Felix T1 - Influence of Ordinary Differential Equations on Neural Networks N2 - In this thesis, I want to analyze how neural networks trained to resemble a given function based on particular values react to additional knowledge about the function in terms of a differential equation that the function satisfies. In the first chapter, I will recall the definition of initial value problems and discuss the existence and uniqueness of their solutions, as well as touch on a method to numerically approximate them. Furthermore, I will give a mathematical introduction to neural networks. Then, I will consider a system of two first-order differential equations and test how their solution will be approximated by a neural net. In the next step, I want to see how added noise to the training data and the influence of the differential equation on the loss functional of the neural net will affect the error of the predicted solution against the exact data. Furthermore, I will test if this influence can help reduce the amount of necessary data points in order to reach similar degrees of accuracy. Y1 - 2019 ER - TY - THES A1 - Rabben, Robert Julian T1 - Bestimmung invarianter Unterräume des Koopman-Operators einer Overdamped Langevin-Dynamik mithilfe eines Muli-Layer Neural Networks Y1 - 2019 ER - TY - THES A1 - Wachsmann, Ole T1 - Computational Models for the prediction of drug-induced Nephrotoxicity Y1 - 2019 ER - TY - THES A1 - Boz, Seyma T1 - Classification of the toxicity profile of the withdrawn drugs using computational prediction platform ProTox-II Y1 - 2019 ER - TY - THES A1 - Basaleh, Rascha T1 - Prediction and Analysis of Drugs Inducing Liver Injury (DILI) using ProTox-II Computational-Tool Y1 - 2019 ER - TY - THES A1 - Clausecker, Robert T1 - Notes on the Construction of Tablebases Y1 - 2017 ER - TY - THES A1 - Wyczisk, Christopher T1 - Portfoliooptimierung mittels mathematischer Optimierung Y1 - 2016 ER -