  Bayesian inference is the workhorse of gravitational-wave astronomy, for
example, determining the mass and spins of merging black holes, revealing the
neutron star equation of state, and unveiling the population properties of
compact binaries. The science enabled by these inferences comes with a
computational cost that can limit the questions we are able to answer. This
cost is expected to grow. As detectors improve, the detection rate will go up,
allowing less time to analyze each event. Improvement in low-frequency
sensitivity will yield longer signals, increasing the number of computations
per event. The growing number of entries in the transient catalog will drive up
the cost of population studies. While Bayesian inference calculations are not
entirely parallelizable, key components are embarrassingly parallel:
calculating the gravitational waveform and evaluating the likelihood function.
Graphical processor units (GPUs) are adept at such parallel calculations. We
report on progress porting gravitational-wave inference calculations to GPUs.
Using a single code - which takes advantage of GPU architecture if it is
available - we compare computation times using modern GPUs (NVIDIA P100) and
CPUs (Intel Gold 6140). We demonstrate speed-ups of $\sim 50 \times$ for
compact binary coalescence gravitational waveform generation and likelihood
evaluation and more than $100\times$ for population inference within the
lifetime of current detectors. Further improvement is likely with continued
development. Our python-based code is publicly available and can be used
without familiarity with the parallel computing platform, CUDA.
