  The discovery of gravitational waves, first observed in September 2015
following the merger of a binary black hole system, has already revolutionised
our understanding of the Universe. This was further enhanced in August 2017,
when the coalescence of a binary neutron star system was observed both with
gravitational waves and a variety of electromagnetic counterparts; this joint
observation marked the beginning of gravitational multimessenger astronomy. The
Einstein Telescope, a proposed next-generation ground-based gravitational-wave
observatory, will dramatically increase the sensitivity to sources: the number
of observations of gravitational waves is expected to increase from roughly 100
per year to roughly 100'000 per year, and signals may be visible for hours at a
time, given the low frequency cutoff of the planned instrument. This increase
in the number of observed events, and the duration with which they are
observed, is hugely beneficial to the scientific goals of the community but
poses a number of significant computing challenges. Moreover, the currently
used computing algorithms do not scale to this new environment, both in terms
of the amount of resources required and the speed with which each signal must
be characterised. This contribution will discuss the Einstein Telescope's
computing challenges, and the activities that are underway to prepare for them.
Available computing resources and technologies will greatly evolve in the years
ahead, and those working to develop the Einstein Telescope data analysis
algorithms will need to take this into account. It will also be important to
factor into the initial development of the experiment's computing model the
availability of huge parallel HPC systems and ubiquitous Cloud computing; the
design of the model will also, for the first time, include the environmental
impact as one of the optimisation metrics.
