  This paper presents the SPIIR pipeline used for public alerts during the
third advanced LIGO and Virgo observation run (O3 run). The SPIIR pipeline uses
infinite impulse response (IIR) filters to perform extremely low-latency
matched filtering and this process is further accelerated with graphics
processing units (GPUs). It is the first online pipeline to select candidates
from multiple detectors using a coherent statistic based on the maximum network
likelihood ratio statistic principle. Here we simplify the derivation of this
statistic using the singular-value-decomposition (SVD) technique and show that
single-detector signal-to-noise ratios from matched filtering can be directly
used to construct the statistic for each sky direction. Coherent searches are
in general more computationally challenging than coincidence searches due to
extra search over sky direction parameters. The search over sky directions
follows an embarrassing parallelization paradigm and has been accelerated using
GPUs. The detection performance is reported using a segment of public data from
LIGO-Virgo's second observation run. We demonstrate that the median latency of
the SPIIR pipeline is less than 9 seconds, and present an achievable roadmap to
reduce the latency to less than 5 seconds. During the O3 online run, SPIIR
registered triggers associated with 38 of the 56 non-retracted public alerts.
The extreme low-latency nature makes it a competitive choice for joint
time-domain observations, and offers the tantalizing possibility of making
public alerts prior to the merger phase of binary coalescence systems involving
at least one neutron star.
