Abstract

For Run 3 of the Large Hadron Collider, the final stage of the LHCb experiment’s high-level trigger must process 100 GB/s of input data. This corresponds to an input rate of 1 MHz, and is an order of magnitude larger compared to Run 2. The trigger is responsible for selecting all physics signals that form part of the experiment’s broad research programme, and as such defines thousands of analysis-specific selections that together comprise tens of thousands of algorithm instances. The configuration of such a system needs to be extremely flexible to be able to handle the large number of different studies it must accommodate. However, it must also be robust and easy to understand, allowing analysts to implement and understand their own selections without the possibility of error. A Python-based system for configuring the data and control flow of the Gaudi-based trigger application is presented. It is designed to be user-friendly by using functions for modularity and removing indirection layers employed previously in Run 2. Robustness is achieved by reducing global state and instead building the data flow graph in a functional manner, whilst keeping configurability of the full call stack.

Highlights

  • The LHCb experiment [1] is undergoing a major upgrade for the third run of the CERN LHC in 2021 [2, 3]

  • Importing an object from the Algorithms module returns the usual configurable but wrapped in the Algorithm class. This wrapper is immutable after instantiation and distinguishes input and output (I/O) parameters from other parameters

  • Note that the data dependencies of the combiner algorithm do not need to be specified in the control flow

Read more

Summary

Introduction

The LHCb experiment [1] is undergoing a major upgrade for the third run of the CERN LHC in 2021 [2, 3] This involves a new tracking detector and the removal of the hardware trigger stage to gain versatility and efficiency for physics selections. Final high-level trigger stage, we plan to run O(104) unique selection and secondary vertexing algorithm instances to be able to efficiently identify the desired signatures for a large number of different physics analyses. The configuration of such a system needs to be extremely flexible due to the many different studies it must support.

Data and control flow in the trigger application
Algorithms in LHCb software
A new approach to data flow configuration
Control flow configuration
Conclusion
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.