  After a highly successful first data taking period at the LHC, the LHCb
experiment developed a new trigger strategy with a real-time reconstruction,
alignment and calibration for Run II. This strategy relies on offline-like
track reconstruction in the high level trigger, making a separate offline event
reconstruction unnecessary. To enable such reconstruction, and additionally
keeping up with a higher event rate due to the accelerator upgrade, the time
used by the track reconstruction had to be decreased. Timing improvements have
in parts been achieved by utilizing parallel computing techniques that will be
described in this document by considering two example applications. Despite
decreasing computing time, the reconstruction quality in terms of
reconstruction efficiency and fake rate could be improved at several places.
Two applications of fast machine learning techniques are highlighted, refining
track candidate selection at the early stages of the reconstruction.
