  Building particle tracks is the most computationally intense step of event
reconstruction at the LHC. With the increased instantaneous luminosity and
associated increase in pileup expected from the High-Luminosity LHC, the
computational challenge of track finding and fitting requires novel solutions.
The current track reconstruction algorithms used at the LHC are based on Kalman
filter methods that achieve good physics performance. By adapting the Kalman
filter techniques for use on many-core SIMD architectures such as the Intel
Xeon and Intel Xeon Phi and (to a limited degree) NVIDIA GPUs, we are able to
obtain significant speedups and comparable physics performance. New
optimizations, including a dedicated post-processing step to remove duplicate
tracks, have improved the algorithm's performance even further. Here we report
on the current structure and performance of the code and future plans for the
algorithm.
