A heterogeneous and vectorized sequence for the HL-LHC full tracking reconstruction of the CMS experiment

Abstract

This contribution presents the new baseline strategy for the Phase-2 tracking of the CMS experiment for online event reconstruction, and for the main iteration of offline tracking. This tracking sequence takes advantage of the combination of cutting-edge tracking algorithms that are either optimized for parallel execution on GPUs (Patatrack and LST), or vectorized for efficient CPU performance (mkFit). Such a combined approach offers an effective solution to deal with the unprecedented computational challenges caused by the large number of simultaneous collisions per bunch crossing expected at the High-Luminosity Large Hadron Collider (HL-LHC). The proposed combination not only reduces the computational resource requirements but also enhances the physics reach by incorporating displaced tracking and increasingly leveraging machine learning techniques.