  We study the benefits and limits of parallelised Markov chain Monte Carlo
(MCMC) sampling in cosmology. MCMC methods are widely used for the estimation
of cosmological parameters from a given set of observations and are typically
based on the Metropolis-Hastings algorithm. Some of the required calculations
can however be computationally intensive, meaning that a single long chain can
take several hours or days to calculate. In practice, this can be limiting,
since the MCMC process needs to be performed many times to test the impact of
possible systematics and to understand the robustness of the measurements being
made. To achieve greater speed through parallelisation, MCMC algorithms need to
have short auto-correlation times and minimal overheads caused by tuning and
burn-in. The resulting scalability is hence influenced by two factors, the MCMC
overheads and the parallelisation costs. In order to efficiently distribute the
MCMC sampling over thousands of cores on modern cloud computing infrastructure,
we developed a Python framework called CosmoHammer which embeds emcee, an
implementation by Foreman-Mackey et al. (2012) of the affine invariant ensemble
sampler by Goodman and Weare (2010). We test the performance of CosmoHammer for
cosmological parameter estimation from cosmic microwave background data. While
Metropolis-Hastings is dominated by overheads, CosmoHammer is able to
accelerate the sampling process from a wall time of 30 hours on a dual core
notebook to 16 minutes by scaling out to 2048 cores. Such short wall times for
complex data sets opens possibilities for extensive model testing and control
of systematics.
