ASINGLE SMC SAMPLER ON MPI THAT OUTPERFORMS A SINGLE MCMC SAMPLER Alessandro Varsi Lykourgos Kekempanos Department of Electrical Engineering and Electronics Department of Electrical Engineering and Electronics University of Liverpool University of Liverpool Liverpool, L69 3GJ, UK Liverpool, L69 3GJ, UK
[email protected] [email protected] Jeyarajan Thiyagalingam Simon Maskell Scientific Computing Department Department of Electrical Engineering and Electronics Rutherford Appleton Laboratory, STFC University of Liverpool Didcot, Oxfordshire, OX11 0QX, UK Liverpool, L69 3GJ, UK
[email protected] [email protected] ABSTRACT 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. Se- quential 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 im- plementation 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.