Speaker
Le Goff Fabrice
(Rutherford Appleton Laboratory)
Description
The ATLAS experiment collects proton-proton collision events delivered
by the LHC accelerator at CERN. The ATLAS Trigger and Data Acquisition
(TDAQ) system selects, transports and eventually records event data from
the detector at several gigabytes per second. The data are recorded on
transient storage before being delivered to permanent storage. The
transient storage consists of high-performance direct-attached storage
servers accounting for about 500 hard drives. The transient storage
operates dedicated software in the form of a distributed multi-threaded
application. The workload includes both CPU-demanding and IO-oriented tasks.
This paper presents the original application threading model for this
particular workload, discussing the load-sharing strategy among the
available CPU cores. The limitations of this strategy were reached in
2016 due to changes in the trigger configuration involving a new data
distribution pattern. We then describe a novel data-driven load-sharing
strategy, designed to automatically adapt to evolving operational
conditions, as driven by the detector configuration or the physics
research goals. The improved efficiency and adaptability of the solution
were measured with dedicated studies on both test and
production systems. This paper reports on the results of those tests
which demonstrate the capability of operating in a large variety of
conditions with minimal user intervention.
Summary
See attached file
Primary author
Le Goff Fabrice
(Rutherford Appleton Laboratory)
