Job monitoring and steering in D-Grid's High Energy Physics Community Grid

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

  • D. Lorenz - , Universität Siegen (Autor:in)
  • S. Borovac - , University of Wuppertal (Autor:in)
  • P. Buchholz - , Universität Siegen (Autor:in)
  • H. Eichenhardt - , Technische Universität Dresden (Autor:in)
  • T. Harenberg - , University of Wuppertal (Autor:in)
  • P. Mättig - , University of Wuppertal (Autor:in)
  • M. Mechtel - , University of Wuppertal (Autor:in)
  • R. Müller-Pfefferkorn - , Technische Universität Dresden (Autor:in)
  • R. Neumann - , Technische Universität Dresden (Autor:in)
  • K. Reeves - , University of Wuppertal (Autor:in)
  • Ch Uebing - , Universität Siegen (Autor:in)
  • W. Walkowiak - , Universität Siegen (Autor:in)
  • Th William - , Zentrum für Informationsdienste und Hochleistungsrechnen (ZIH) (Autor:in)
  • R. Wismüller - , Universität Siegen (Autor:in)

Abstract

In the High Energy Physics Comunity Grid (HEPCG) of Germany’s D-Grid initiative, a suite of tools supporting the user in monitoring his jobs was developed. In the HEP community many users submit large numbers of jobs. A considerable fraction of these jobs fail for various reasons. Until now, it has been hard or even impossible for the user to find the reason for the job failure. The AMon tool supports the user with a graphical web-based overview on status and resource usage of his jobs. The script wrapper JEM (Job Execution Monitor) monitors a job’s environment giving detailed information about the job execution. Finally, once the job itself is running, a computational steering tool allows the user to interact with his job at runtime, to visualize intermediate results, and to modify job parameters.

Details

OriginalspracheEnglisch
Seiten (von - bis)308-314
Seitenumfang7
FachzeitschriftFuture Generation Computer Systems
Jahrgang25
Ausgabenummer3
PublikationsstatusVeröffentlicht - März 2009
Peer-Review-StatusJa

Externe IDs

Scopus 56149119030

Schlagworte

Forschungsprofillinien der TU Dresden

Schlagwörter

  • Computational steering, Grid computing, High Energy Physics Community Grid, Job monitoring