  Understanding the properties of transient gravitational waves and their
sources is of broad interest in physics and astronomy. Bayesian inference is
the standard framework for astro-physical measurement in transient
gravitational-wave astronomy. Usually, stochastic sampling algorithms are used
to estimate posterior probability distributions over the parameter spaces of
models describing experimental data. The most physically accurate models
typically come with a large computational overhead which can render data
analysis extremely time consuming, or possibly even prohibitive. In some cases
highly specialized optimizations can mitigate these issues, though they can be
difficult to implement, as well as to generalize to arbitrary models of the
data. Here, we propose an accurate, flexible and scalable method for
astro-physical inference: parallelized nested sampling. The reduction in the
wall-time of inference scales almost linearly with the number of parallel
processes running on a high-performance computing cluster. By utilizing a pool
of several hundreds or thousands of CPUs in a high-performance cluster, the
large wall times of many astrophysical inferences can be alleviated while
simultaneously ensuring that any gravitational-wave signal model can be used
"out of the box", i.e., without additional optimization or approximation. Our
method will be useful to both the LIGO-Virgo-KAGRA collaborations and the wider
scientific community performing astrophysical analyses on gravitational waves.
An implementation is available in the open source gravitational-wave inference
library $\texttt{pBilby}$ (parallel $\texttt{bilby}$).
