Rmpi: mpi.iapply – R documentation

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Rmpi

mpi.iapply

(Load balancing) parallel apply with nonblocking features

Description

(Load balancing) parallellapply and related functions.

Usage

mpi.iapplyLB(X, FUN, ..., apply.seq=NULL, comm=1, sleep=0.01)
mpi.iparApply(X, MARGIN, FUN, ..., job.num = mpi.comm.size(comm)-1,
                    apply.seq=NULL, comm=1, sleep=0.01)
mpi.iparLapply(X, FUN, ..., job.num=mpi.comm.size(comm)-1, apply.seq=NULL, 
		    comm=1,sleep=0.01)  
mpi.iparSapply(X, FUN, ..., job.num=mpi.comm.size(comm)-1, apply.seq=NULL, 
		simplify=TRUE, USE.NAMES = TRUE, comm=1, sleep=0.01)  
mpi.iparRapply(X, FUN, ..., job.num=mpi.comm.size(comm)-1, apply.seq=NULL, 
		comm=1, sleep=0.01)  
mpi.iparCapply(X, FUN, ..., job.num=mpi.comm.size(comm)-1, apply.seq=NULL, 
		comm=1,sleep=0.01)  
mpi.iparReplicate(n, expr, job.num=mpi.comm.size(comm)-1, apply.seq=NULL, 
		simplify = TRUE, comm=1,sleep=0.01)
mpi.iparMM(A, B, comm=1, sleep=0.01)

Arguments

`X`	an array or matrix.
`MARGIN`	vector specifying the dimensions to use.
`FUN`	a function.
`simplify`	logical; should the result be simplified to a vector or matrix if possible?
`USE.NAMES`	logical; if `TRUE` and if `X` is character, use `X` as `names` for the result unless it had names already.
`n`	number of replications.
`A`	a matrix
`B`	a matrix
`expr`	expression to evaluate repeatedly.
`job.num`	Total job numbers. If job numbers is bigger than total slave numbers (default value), a load balancing approach is used.
`apply.seq`	if reproducing the same computation (simulation) is desirable, set it to the integer vector .mpi.applyLB generated in previous computation (simulation).
`...`	optional arguments to `Fun`
`comm`	a communicator number
`sleep`	a sleep interval on master node (in sec)

Details

mpi.iparApply, mpi.iparLapply, mpi.iparSapply, mpi.iparRapply, mpi.iparCapply, mpi.iparSapply, mi.iparReplicate, and mpi.iparMM are nonblocking versions of mpi.parApply, mpi.parLapply, mpi.parSapply, mpi.parRapply, mpi.parCapply, mpi.parSapply, mpi.parReplicate, and mpi.parMM respectively. The main difference is that mpi.iprobe and Sys.sleep are used so that master node consumes almost no CPU cycles while waiting for slaves results. However, due to frequent wake/sleep cycles on master, those functions are not suitable for running small jobs on slave nodes. If anticipated computing time for each job is relatively long, e.g., minutes or hours, setting sleep to be 1 second or longer will further reduce load on master (only slightly).