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