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Auflistung nach Autor:in "Becker, Thomas"

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  • Konferenzbeitrag
    Integrating Organic Computing Mechanisms into a Task-based Runtime System for Heterogeneous Systems
    (INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (Workshop-Beiträge), 2019) Becker, Thomas
    Modern computer architectures feature a high degree of parallelism and heterogeneity. They are deployed in different fields of application, which leads to different constraints and optimization goals. Additionally, the current and future system states have to be considered, making dynamic and proactive adaptations necessary. Organic Computing techniques offer a solution for this. A combination with runtime systems, which control execution and monitor the system, sensibly provides reduction in system complexity, efficient resource usage and the ability to dynamically adapt. To enable Organic Computing in runtime systems, we first study heterogeneous systems in different fields of application. As we identified dependability as a major concern, we study symptom-based fault detection, a light-weight technique to detect faults. We develop a mechanism based on rule-based machine learning to consider the identified requirements and constraints and dynamically balance contradicting optimization goals. Additionally, we present a scheduling mechanism to globally optimize several instances of a runtime system and show first results.
  • Zeitschriftenartikel
    Predicting Efficient Execution with Source Code Analysis in a Heterogeneous Environment
    (PARS-Mitteilungen: Vol. 34, Nr. 1, 2017) Hellwig, Markus; Becker, Thomas
    Finding a good schedule for the tasks of an application is a critical step for the efficient usage of heterogeneous systems. A good schedule can only be found with information about the tasks to be scheduled. In a dynamic system, this information is normally only available after each task is at least executed once, thereby creating an initial overhead until a good schedule can be created. Therefore, we introduce a method based on static code analysis and machine learning algorithms to predict the fastest processor of a given OpenCL task before runtime by classification which helps to reduce this initial overhead. We show how we used a static code analysis implementation based on Clang to generate training data on a set of 10 different heterogeneous processors including Intel, AMD and Nvidia GPUs, a Intel Xeon Phi and Intel CPUs. This training data was used to generate prediction models via several different machine learning algorithms including Random Forest and k-Nearest Neighbour and then evaluate the models by predicting the fastest processor out of two and more processors via classification.
  • Zeitschriftenartikel
    Symptom-based Fault Detection in Modern Computer Systems
    (PARS-Mitteilungen: Vol. 35, Nr. 1, 2020) Becker, Thomas; Rudolf, Nico; Yang, Dai; Karl, Wolfgang
    Miniaturization and the increasing number of components, which get steadily more complex, lead to a rising failure rate in modern computer systems. Especially soft hardware errors are a major problem because they are usually temporary and therefore hard to detect. As classical fault-tolerance methods are very costly and reduce system efficiency, light-weight methods are needed to increase system reliability. A method that copes with this requirement is symptom-based fault detection. In this work, we evaluate the ability to detect different faults with symptom-based fault detection by using hardware performance counters. As the knowledge of a fault occurrence is usually not enough, we also evaluate the possibility to make conclusions about which fault occurred. For the evaluation, we used the fault-injection library FINJ and manually manipulated loops. The results show that symptom-based fault detection enables the system to detect faulty application behavior, however fine-grained conclusions about the causing fault are hardly possible.