I am recently running a Bayesian model based on DRAM (Delayed Rejection Adaptive Metropolis) sampling on R with FME package. As the analysis is consuming considerable time, I am planning to move it to a multicore computer. However, as what I understood, we are not able to parallelize MCMC code to speed up the computing process, or it will break its serial nature. The best we can do is to run multiple chains on multiple processors and summarize them afterwards.
I was therefore wondering:
- Does it mean that we can speed up the computing process by reducing iterations to a low number for each chain and increasing the number of chains we run in total?
- I looked up the High-Performance and Parallel Computing with R page and found that there are only packages for distributed computing of multiple MCMC chains using BUGS and JAGS. I wonder does it mean that it will be rather difficult to parallelly process the analysis with other Bayesian packages like FME?
As I haven't found much example code on this topic, I would also appreciate if anyone could direct me to the related discussions. Thank you very much in advance.