如何在Linux Cluster上使用Rmpi来增加DEoptim可用的内核？

Question

I am using code developed in R to calibrate a hydrological model with 8 parameters using DEoptim (a function that aims to minimise an objective function). The DEoptim code uses the 'parallel' package to detect the number of cores available using 'DetectCores()'. On my PC I have 4 cores with 2 threads each so it detects 8 cores and then sends out the hydrological model to a core with different values of parameters and the results are returned to the centre. It does this hundreds or thousands of times and iterates the parameters to try and find an optimum set. Therefore the more cores available, the faster it will work.

I am at a university and have access to a Linux compute cluster. They have servers with up to 12 cores (ie not threads) and if I used this it would work two - three times faster than my PC. Great. However, ideally I would spread the code around other servers so I could have access to more cores and all the info sent back the master.

Therefore, my question is how could I include Rmpi in my code to effectively increase the cores available. As you can probably tell, I am quite new to using clusters.

Many thanks, Antony

Answer 1

If you want to execute DEoptim on multiple nodes of a Linux cluster, I believe you'll need to use foreach by specifying parallelType=2 in the control argument. You can use either the doMPI parallel backend or the doParallel backend with an MPI cluster object. For example:

library(doParallel)
library(Rmpi)
cl <- makeCluster(mpi.universe.size()-1, type='MPI')
registerDoParallel(cl)

# and eventually...
DEoptim(fn=Genrose, lower=rep(-25, n), upper=rep(25, n),
  control=list(NP=10*n, itermax=maxIt, parallelType=2))

You'll need to have the snow package installed in addition to the others. Also, make sure that you execute your script with mpirun using the -np 1 option. If you don't use mpirun, the workers will all be spawned on the local machine.