  In the High-Luminosity Large Hadron Collider (HL-LHC), one of the most
challenging computational problems is expected to be finding and fitting
charged-particle tracks during event reconstruction. The methods currently in
use at the LHC are based on the Kalman filter. Such methods have shown to be
robust and to provide good physics performance, both in the trigger and
offline. In order to improve computational performance, we explored
Kalman-filter-based methods for track finding and fitting, adapted for
many-core SIMD and SIMT architectures. Our adapted Kalman-filter-based software
has obtained significant parallel speedups using such processors, e.g., Intel
Xeon Phi, Intel Xeon SP (Scalable Processors) and (to a limited degree) NVIDIA
GPUs. Recently, an effort has started towards the integration of our software
into the CMS software framework, in view of its exploitation for the Run III of
the LHC. Prior reports have shown that our software allows in fact for some
significant improvements over the existing framework in terms of computational
performance with comparable physics performance, even when applied to realistic
detector configurations and event complexity. Here, we demonstrate that in such
conditions physics performance can be further improved with respect to our
prior reports, while retaining the improvements in computational performance,
by making use of the knowledge of the detector and its geometry.
