  Markov Chain Monte Carlo (MCMC) is a well-established family of algorithms
which are primarily used in Bayesian statistics to sample from a target
distribution when direct sampling is challenging. Single instances of MCMC
methods are widely considered hard to parallelise in a problem-agnostic fashion
and hence, unsuitable to meet both constraints of high accuracy and high
throughput. Sequential Monte Carlo (SMC) Samplers can address the same problem,
but are parallelisable: they share with Particle Filters the same key tasks and
bottleneck. Although a rich literature already exists on MCMC methods, SMC
Samplers are relatively underexplored, such that no parallel implementation is
currently available. In this paper, we first propose a parallel MPI version of
the SMC Sampler, including an optimised implementation of the bottleneck, and
then compare it with single-core Metropolis-Hastings. The goal is to show that
SMC Samplers may be a promising alternative to MCMC methods with high potential
for future improvements. We demonstrate that a basic SMC Sampler with 512 cores
is up to 85 times faster or up to 8 times more accurate than
Metropolis-Hastings.
