Verteidigung im Promotionsverfahren von Dipl.-Inform. Benjamin Schiller (Institut für Systemarchitektur, Datenschutz und Datensicherheit)
15.12.2016, 13:15 Uhr, APB 1004 (Ratssaal)
The analysis of dynamic systems provides insights into their time-dependent characteristics. This enables us to monitor, evaluate, and improve systems from various areas. They are often represented as graphs that model the system's components and their relations. The analysis of the resulting dynamic graphs yields great insights into the system's underlying structure, its characteristics, as well as properties of single components. The interpretation of these results can help us understand how a system works and how parameters influence its performance. This knowledge supports the design of new systems and the improvement of existing ones. The main issue in this scenario is the performance of analyzing the dynamic graph to obtain relevant properties. While various approaches have been developed to analyze dynamic graphs, it is not always clear which one performs best for the analysis of a specific graph. The runtime also depends on many other factors, including the size and topology of the graph, the frequency of changes, and the data structures used to represent the graph in memory. While the benefits and drawbacks of many data structures are well-known, their runtime is hard to predict when used for the representation of dynamic graphs. Hence, tools are required to benchmark and compare different algorithms for the computation of graph properties and data structures for the representation of dynamic graphs in memory. Based on deeper insights into their performance, new algorithms can be developed and efficient data structures can be selected. In this thesis, we present four contributions to tackle these problems: A benchmarking framework for dynamic graph analysis, novel algorithms for the efficient analysis of dynamic graphs, an approach for the parallelization of dynamic graph analysis, and a novel paradigm to select and adapt graph data structures. In addition, we present three use cases from the areas of social, computer, and biological networks to illustrate the great insights provided by their graph-based analysis. Our contributions provide novel approaches for the efficient analysis of dynamic graphs and tools to further investigate the trade-offs between different factors that influence the performance.
Wissenschaftlicher Vortrag (alle Studiengänge) von Prof. Sandip Kundu (University of Massachusetts, Amherst)
22.12.2016, 10:00 Uhr, Georg-Schumann-Str. 7A, 2. OG Raum 204
Asymmetric multi-core processors (AMPs) comprise of cores with different sizes of micro-architectural resources yielding very different performance and energy characteristics. Since the computational demands of workloads vary from one task to the other, AMPs often provide greater power efficiency than symmetric multicores. Furthermore, as the computational demands of a task change during its course of execution, reassigning the task from one core to another, where it can run more efficiently can further improve the overall energy efficiency. However, too frequent re-assignments of tasks to cores may result in high overhead. To greatly reduce this overhead we propose a morphable core architecture that dynamically adapts its resource sizes and operating frequency to assume one of four possible core configurations. Such a morphable architecture allows more frequent task to core configuration re-assignments for a better match between the current needs of the task and the available resources. To make the online morphing decisions we have developed a runtime analysis scheme using hardware performance counters. Our results indicate that the proposed morphable architecture controlled by the runtime management scheme can improve the performance/watt of applications by 43% over executing on a static AMP.
Diese Veranstaltung wird unterstützt von Professur Prozessordesign.