  We present and apply Gaussbock, a new embarrassingly parallel iterative
algorithm for cosmological parameter estimation designed for an era of cheap
parallel computing resources. Gaussbock uses Bayesian nonparametrics and
truncated importance sampling to accurately draw samples from posterior
distributions with an orders-of-magnitude speed-up in wall time over
alternative methods. Contemporary problems in this area often suffer from both
increased computational costs due to high-dimensional parameter spaces and
consequent excessive time requirements, as well as the need for fine tuning of
proposal distributions or sampling parameters. Gaussbock is designed
specifically with these issues in mind. We explore and validate the performance
and convergence of the algorithm on a fast approximation to the Dark Energy
Survey Year 1 (DES Y1) posterior, finding reasonable scaling behavior with the
number of parameters. We then test on the full DES Y1 posterior using
large-scale supercomputing facilities, and recover reasonable agreement with
previous chains, although the algorithm can underestimate the tails of
poorly-constrained parameters. Additionally, we discuss and demonstrate how
Gaussbock recovers complex posterior shapes very well at lower dimensions, but
faces challenges to perform well on such distributions in higher dimensions. In
addition, we provide the community with a user-friendly software tool for
accelerated cosmological parameter estimation based on the methodology
described in this paper.
