Speaker
Description
The ATLAS High Level Trigger Farm consists of around 30,000 CPU cores which filter events at up to 100 kHz input rate.
A costing framework is built into the high level trigger, this enables detailed monitoring of the system and allows for data-driven predictions to be made
utilising specialist datasets. This talk will present an overview of how ATLAS collects in-situ monitoring data on both CPU usage and dataflow
over the data-acquisition network during the trigger execution, and how these data are processed to yield both low level monitoring of individual
selection-algorithms and high level data on the overall performance of the farm. For development and prediction purposes, ATLAS uses a special
`Enhanced Bias' event selection. This mechanism will be explained along with how is used to profile expected resource usage and output event-rate of
new physics selections, before they are executed on the actual high level trigger farm.
| Primary Keyword (Mandatory) | Trigger |
|---|---|
| Secondary Keyword (Optional) | DAQ |
| Tertiary Keyword (Optional) | Data processing workflows and frameworks/pipelines |
