  One of the most computationally challenging problems expected for the
High-Luminosity Large Hadron Collider (HL-LHC) is finding and fitting particle
tracks during event reconstruction. Algorithms used at the LHC today rely on
Kalman filtering, which builds physical trajectories incrementally while
incorporating material effects and error estimation. Recognizing the need for
faster computational throughput, we have adapted Kalman-filter-based methods
for highly parallel, many-core SIMD and SIMT architectures that are now
prevalent in high-performance hardware. Previously we observed significant
parallel speedups, with physics performance comparable to CMS standard
tracking, on Intel Xeon, Intel Xeon Phi, and (to a limited extent) NVIDIA GPUs.
While early tests were based on artificial events occurring inside an idealized
barrel detector, we showed subsequently that our mkFit software builds tracks
successfully from complex simulated events (including detector pileup)
occurring inside a geometrically accurate representation of the CMS-2017
tracker. Here, we report on advances in both the computational and physics
performance of mkFit, as well as progress toward integration with CMS
production software. Recently we have improved the overall efficiency of the
algorithm by preserving short track candidates at a relatively early stage
rather than attempting to extend them over many layers. Moreover, mkFit
formerly produced an excess of duplicate tracks; these are now explicitly
removed in an additional processing step. We demonstrate that with these
enhancements, mkFit becomes a suitable choice for the first iteration of CMS
tracking, and eventually for later iterations as well. We plan to test this
capability in the CMS High Level Trigger during Run 3 of the LHC, with an
ultimate goal of using it in both the CMS HLT and offline reconstruction for
the HL-LHC CMS tracker.
