Speaker
Karl Harrison
(High Energy Physics Group, Cavendish Laboratory)
Description
Ganga is a lightweight, end-user tool for job submission and monitoring and provides
an open framework for multiple applications and submission backends. It is developed
in a joint effort in LHCb and ATLAS. The main goal of Ganga is to effectively enable
large-scale distributed data analysis for physicists working in the LHC experiments.
Ganga offers simple, pleasant and consistent user experience in a variety of
heterogeneous environments: from local clusters to global GRID systems. Ganga helps
end-users organize the analysis activities on the GRID by providing automatic
persistency of the job's metadata. A user has full access to the jobs submitted in
the past including their configuration and input/output. Automatic status monitoring
and output retrieval simplify the usage of the tool. Job splitting allows a very
efficient handling of large numbers of similar jobs using different datasets. Job
templates provide a convenient mechanism to support repetitive tasks. Ganga is an
open development framework and has a clear internal architecture. Ganga Public
Interface (GPI) is a python-based, user-centric API that is a key component of the
system. GPI combines the consistency and flexibility of the programming interface
with intuitive and concise usage. GPI may be used for writing complex, user-specific
scripts or in the interactive python shell. A Qt-based graphical user interface is a
GPI overlay which integrates scripting and graphical capabilities into a single
environment. GPI may also be embedded as a library in a third-party framework and be
used as convenient abstraction layer for job submission and monitoring. Release 4 of
Ganga contains optimized handlers for ATLAS/Athena and LHCb/Gaudi applications which
are interfaced to a number of generic execution backends (LSF, LCG, gLite) as well as
experiment-specific workload management systems (LHCb's DIRAC and ATLAS production
system). Other applications, such as Geant4 simulation in medical physics, or BLAST
protein alignment algorithm in biotechnology have been successfully run with Ganga.
Ganga fully exploits the plug-in architecture that makes the integration of new
applications and backend very easy.
Primary author
Dr
Ulrik Egede
(IMPERIAL COLLEGE LONDON)
Co-authors
Dr
Alexander Soroko
(University of Oxford)
Dr
Andrew Maier
(CERN)
Dr
Chun Lik Tan
(University of Birmingham)
Dr
Dietrich Liko
(CERN)
Dr
Jakub Moscicki
(CERN)
Dr
Karl Harrison
(University of Cambridge)
