snowfallcalculation  R Documentation 
Parallel calculation functions. Execution is distributed automatically
over the cluster.
Most of this functions are wrappers for snow functions, but all
can be used directly in sequential mode.
sfClusterApply( x, fun, ... )
sfClusterApplyLB( x, fun, ... )
sfClusterApplySR( x, fun, ..., name="default", perUpdate=NULL, restore=sfRestore() )
sfClusterMap( fun, ..., MoreArgs = NULL, RECYCLE = TRUE )
sfLapply( x, fun, ... )
sfSapply( x, fun, ..., simplify = TRUE, USE.NAMES = TRUE )
sfApply( x, margin, fun, ... )
sfRapply( x, fun, ... )
sfCapply( x, fun, ... )
sfMM( a, b )
sfRestore()
x 
vary depending on function. See function details below. 
fun 
function to call 
margin 
vector speficying the dimension to use 
... 
additional arguments to pass to standard function 
simplify 
logical; see 
USE.NAMES 
logical; see 
a 
matrix 
b 
matrix 
RECYCLE 
see snow documentation 
MoreArgs 
see snow documentation 
name 
a character string indicating the name of this parallel
execution. Naming is only needed if there are more than one call to

perUpdate 
a numerical value indicating the progress printing. Values range from 1 to 100 (no printing). Value means: any X percent of progress status is printed. Default (on given value ‘NULL’) is 5). 
restore 
logical indicating whether results from previous runs should be restored or not. Default is coming from sfCluster. If running without sfCluster, default is FALSE, if yes, it is set to the value coming from the external program. 
sfClusterApply
calls each index of a given list on a seperate
node, so length of given list must be smaller than nodes. Wrapper for
snow function clusterApply
.
sfClusterApplyLB
is a load balanced version of
sfClusterApply
. If a node finished it's list segment it
immidiately starts with the next segment. Use this function in
infrastructures with machines with different speed. Wrapper for
snow function clusterApplyLB
.
sfClusterApplySR
saves intermediate results and is able to
restore them on a restart. Use this function on very long calculations
or it is (however) foreseeable that cluster will not be able to finish
it's calculations (e.g. because of a shutdown of a node machine). If
your program use more than one parallised part, argument name
must be given with a unique name for each loop. Intermediate data is
saved depending on Rfilename, so restore of data must be explicit
given for not confusing changes on your Rfile (it is recommended to
only restore on fully tested programs). If restores,
sfClusterApplySR
continues calculation after the first nonnull
value in the saved list. If your parallized function can return null
values, you probably want to change this.
sfLapply
, sfSapply
and sfApply
are parallel
versions of lapply
, sapply
and apply
. The first
two use an list or vector as argument, the latter an array.
parMM
is a parallel matrix multiplication. Wrapper for
snow function parMM
.
sfRapply
and sfCapply
are not implemented atm.
See snow documentation for details on commands:
snowparallel
## Not run:
restoreResults < TRUE
sfInit(parallel=FALSE)
## Execute in cluster or sequential.
sfLapply(1:10, exp)
## Execute with intermediate result saving and restore on wish.
sfClusterApplySR(1:100, exp, name="CALC_EXP", restore=restoreResults)
sfClusterApplySR(1:100, sum, name="CALC_SUM", restore=restoreResults)
sfStop()
##
## Small bootstrap example.
##
sfInit(parallel=TRUE, cpus=2)
require(mvna)
data(sir.adm)
sfExport("sir.adm", local=FALSE)
sfLibrary(cmprsk)
wrapper < function(a) {
index < sample(1:nrow(sir.adm), replace=TRUE)
temp < sir.adm[index, ]
fit < crr(temp$time, temp$status, temp$pneu, failcode=1, cencode=0)
return(fit$coef)
}
result < sfLapply(1:100, wrapper)
mean( unlist( rbind( result ) ) )
sfStop()
## End(Not run)
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