• Refine Query
  • Source
  • Publication year
  • to
  • Language
  • 2
  • 2
  • Tagged with
  • 4
  • 2
  • 2
  • 2
  • 2
  • 2
  • 2
  • 2
  • 2
  • 2
  • 2
  • 2
  • 2
  • 2
  • 2
  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Schnittstellen zum Trust in der Schweiz Die Aussonderung des Trustvermögens im Konkurs sowie die Tracing-Problematik des Trusts unter ausländischem Recht in der Schweiz /

Mayr, Claudia Sabine. January 2008 (has links) (PDF)
Master-Arbeit Univ. St. Gallen, 2008.
2

Testing, tracing und debugging bei embedded systems /

Langer, Josef. January 2008 (has links)
Zugl.: Linz, Universiẗat, Diss., 2008.
3

Scalable Tools for Non-Intrusive Performance Debugging of Parallel Linux Workloads

Schöne, Robert, Schuchart, Joseph, Ilsche, Thomas, Hackenberg, Daniel 26 January 2015 (has links) (PDF)
There is a variety of tools to measure the performance of Linux systems and the applications running on them. However, the resulting performance data is often presented in plain text format or only with a very basic user interface. For large systems with many cores and concurrent threads, it is increasingly difficult to present the data in a clear way for analysis. Moreover, certain performance analysis and debugging tasks require the use of a high-resolution time-line based approach, again entailing data visualization challenges. Tools in the area of High Performance Computing (HPC) have long been able to scale to hundreds or thousands of parallel threads and help finding performance anomalies. We therefore present a solution to gather performance data using Linux performance monitoring interfaces. A combination of sampling and careful instrumentation allows us to obtain detailed performance traces with manageable overhead. We then convert the resulting output to the Open Trace Format (OTF) to bridge the gap between the recording infrastructure and HPC analysis tools. We explore ways to visualize the data by using the graphical tool Vampir. The combination of established Linux and HPC tools allows us to create an interface for easy navigation through time-ordered performance data grouped by thread or CPU and to help users find opportunities for performance optimizations.
4

Scalable Tools for Non-Intrusive Performance Debugging of Parallel Linux Workloads

Schöne, Robert, Schuchart, Joseph, Ilsche, Thomas, Hackenberg, Daniel January 2014 (has links)
There is a variety of tools to measure the performance of Linux systems and the applications running on them. However, the resulting performance data is often presented in plain text format or only with a very basic user interface. For large systems with many cores and concurrent threads, it is increasingly difficult to present the data in a clear way for analysis. Moreover, certain performance analysis and debugging tasks require the use of a high-resolution time-line based approach, again entailing data visualization challenges. Tools in the area of High Performance Computing (HPC) have long been able to scale to hundreds or thousands of parallel threads and help finding performance anomalies. We therefore present a solution to gather performance data using Linux performance monitoring interfaces. A combination of sampling and careful instrumentation allows us to obtain detailed performance traces with manageable overhead. We then convert the resulting output to the Open Trace Format (OTF) to bridge the gap between the recording infrastructure and HPC analysis tools. We explore ways to visualize the data by using the graphical tool Vampir. The combination of established Linux and HPC tools allows us to create an interface for easy navigation through time-ordered performance data grouped by thread or CPU and to help users find opportunities for performance optimizations.

Page generated in 0.0554 seconds