  Neutrinos are particles that interact rarely, so identifying them requires
large detectors which produce lots of data. Processing this data with the
computing power available is becoming more difficult as the detectors increase
in size to reach their physics goals. In liquid argon time projection chambers
(TPCs) the charged particles from neutrino interactions produce ionization
electrons which drift in an electric field towards a series of collection
wires, and the signal on the wires is used to reconstruct the interaction. The
MicroBooNE detector currently collecting data at Fermilab has 8000 wires, and
planned future experiments like DUNE will have 100 times more, which means that
the time required to reconstruct an event will scale accordingly. Modernization
of liquid argon TPC reconstruction code, including vectorization,
parallelization and code portability to GPUs, will help to mitigate these
challenges. The liquid argon TPC hit finding algorithm within the
\texttt{LArSoft}\xspace framework used across multiple experiments has been
vectorized and parallelized. This increases the speed of the algorithm on the
order of ten times within a standalone version on Intel architectures. This new
version has been incorporated back into \texttt{LArSoft}\xspace so that it can
be generally used. These methods will also be applied to other low-level
reconstruction algorithms of the wire signals such as the deconvolution. The
applications and performance of this modernized liquid argon TPC wire
reconstruction will be presented.
