  Charged particle reconstruction is one the most computationally heavy
components of the full event reconstruction of Large Hadron Collider (LHC)
experiments. Looking to the future, projections for the High Luminosity LHC
(HL-LHC) indicate a superlinear growth for required computing resources for
single-threaded CPU algorithms that surpass the computing resources that are
expected to be available. The combination of these facts creates the need for
efficient and computationally performant pattern recognition algorithms that
will be able to run in parallel and possibly on other hardware, such as GPUs,
given that these become more and more available in LHC experiments and
high-performance computing centres. Line Segment Tracking (LST) is a novel such
algorithm which has been developed to be fully parallelizable and hardware
agnostic. The latter is achieved through the usage of the Alpaka library. The
LST algorithm has been tested with the CMS central software as an external
package and has been used in the context of the CMS HL-LHC High Level Trigger
(HLT). When employing LST for pattern recognition in the HLT tracking, the
physics and timing performances are shown to improve with respect to the ones
utilizing the current pattern recognition algorithms. The latest results on the
usage of the LST algorithm within the CMS HL-LHC HLT are presented, along with
prospects for further improvements of the algorithm and its CMS central
software integration.
