mpi.iapply: (Load balancing) parallel apply with nonblocking features

In Rmpi: Interface (Wrapper) to MPI (Message-Passing Interface)

(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).

See Also

mpi.iapply

Rmpi documentation built on Sept. 23, 2024, 5:09 p.m.