MCPar is a parallel implementation of the Metropolis-Hastings Monte Carlo algorithm (Metropolis, et al. 1953, Hastings 1970). The algorithm samples the distribution using multiple concurrent Markov chains, correcting for the quasi-ergodicity problem, using a variant of the algorithm proposed by Murray (2010). (That paper also explains the quasi-ergodicity problem, for readers unfamiliar with it.) The code is able to parallelize over multiple processors using MPI and within a single processor using vectorization.
The code is designed so that users can plug in their own likelihood
functions. Likelihood functions are implemented by writing subclasses
of the VLFunc interface class. The version of the code in this
repository has a couple of simple examples using sums of Gaussians and
Rosenbrock functions (Rosenbrock, 1960). There is also a wrapper for
a likelihood function written in R, if that is the sort of thing you
are into.
The code is set up to link to a set of MPI libraries. It has been tested with both OpenMPI and Intel MPI. It also uses the Intel Math Kernel Library (MKL). You will need to have both of these libraries in order to build and run the code.
If you want to write your likelihood function in R, you will also need to have R installed, along with the Rcpp and RInside packages. This functionality is optional and is not built by default. If you want to use it, set and export the environment variable USE_RFUNC before you build.
Hastings, W. K. (1970), "Monte Carlo Sampling Methods Using Markov Chains and Their Applications", Biometrika 57: 97--109
Metropolis, N., et al. (1953), "Equations of state calculations by fast computing machines", J. Chem. Phys. 21: 1087--92.
Murray, L. (2010), "Distributed Markov Chain Monte Carlo", Proceedings of Neural Information Processing Systems workshop on learning on cores, clusters, and clouds 11.
Rosenbrock, H. H. (1960), "An automatic method for finding the greatest or least value of a function", The Computer Journal 3: 175--184.