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The High-Luminosity Large Hadron Collider at CERN will be characterized bygreater pileup of events and higher occupancy, making the track reconstructioneven more computationally demanding. Existing algorithms at the LHC are basedon Kalman filter techniques with proven excellent physics performance under avariety of conditions. Starting in 2014, we have been developingKalman-filter-based methods for track finding and fitting adapted for many-coreSIMD processors that are becoming dominant in high-performance systems.  This paper summarizes the latest extensions to our software that allow it torun on the realistic CMS-2017 tracker geometry using CMSSW-generated events,including pileup. The reconstructed tracks can be validated against either theCMSSW simulation that generated the hits, or the CMSSW reconstruction of thetracks. In general, the code's computational performance has continued toimprove while the above capabilities were being added. We demonstrate that thepresent Kalman filter implementation is able to reconstruct events withcomparable physics performance to CMSSW, while providing generally bettercomputational performance. Further plans for advancing the software arediscussed.

Parallelized and Vectorized Tracking Using Kalman Filters with CMS Detector Geometry and Events