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The LHC (Large Hadron Collider) experiments at CERN collect enormous amounts of data, which need to be pre-processed, treated and then analysed to extract the scientific information which physicists look for. This makes the codes developed for LHC paramount examples of HPDA applications.


In DEEP-EST, CERN will investigate a new model for deploying improvements for the analysis of data created by the CMS instrument. This focuses on the instrument code (which specifies how the instrument “sees” events) and its calibration. Currently, new code must be available before starting the actual data processing. CERN will explore the dynamic reprocessing of objects when the instrument code or calibration changes. To test this new concept a large high performance processing centre with excellent integrated storage is required. The MSA could be an ideal platform.


At least three of the DEEP‑EST modules are planned to be used: the storage module will contain the input data, the Cluster Module (CM) will be used for data refresh, and the Data Analytics Modulre (DAM) for Data reduction. The CM and DAM modules are likely to be stressed by this application, which will define its requirements within the co‑design phase of the project. At a later stage of the project, porting to the ESB will be investigated. Derived data will reside in a large object store (SPARK/HDFS or CEPH). As selections are made, the object provenance is checked and an update of the object will be triggered when needed. The concept will be demonstrated on a flagship analysis that is sensitive to changes in calibrations: The Higgs decay to two gamma photons is an obvious choice. The degree of thread parallelism in the reconstruction algorithms determines their efficiency on an HPC platform and effort will be devoted to algorithm modernisation.