  In LHC Run 3, ALICE will increase the data taking rate significantly, from an
approximately 1 kHz trigger readout in minimum-bias Pb--Pb collisions to a 50
kHz continuous readout rate. The reconstruction strategy of the online-offline
computing upgrade foresees a synchronous online reconstruction stage during
data taking, which generates the detector calibration, and a posterior
calibrated asynchronous reconstruction stage. The huge amount of data requires
a significant compression in order to store all recorded events. The aim is a
factor 20 compression of the TPC data, which is one of the main challenges
during synchronous reconstruction. In addition, the reconstruction will run
online, processing 50 times more collisions than at present, yielding results
comparable to current offline reconstruction. These requirements pose new
challenges for the tracking, including the continuous TPC readout, more
overlapping collisions, no a priori knowledge of the primary vertex position
and of location-dependent calibration during the synchronous phase,
identification of low-momentum looping tracks, and a distorted refit to improve
track model entropy coding. At the 2018 workshop, the TPC tracking for Run 3
was presented, which matches the physics performance of the Run 2 offline
tracking. It leverages the potential of hardware accelerators via the OpenCL
and CUDA APIs in a shared source code for CPUs and GPUs for both reconstruction
stages. Porting more reconstruction steps like the remainder of the TPC
reconstruction and tracking for other detectors to GPU will shift the computing
balance from traditional processors towards GPUs. These proceedings focus on
the global tracking strategy, including the ITS and TRD detectors, offloading
more reconstruction steps onto GPU, and the approaches taken to achieve the
necessary data compression.
