Nothing
library(pbdDMAT, quiet = TRUE)
###################SETTINGS######################
init.grid()
comm.set.seed(1234, diff = TRUE)
# size
N <- 15000
p <- 500
# blocking
bldim <- 4
# normal family
mean <- 100
sd <- 1000
# replications
reps <- 10
#################################################
# benchmark
datatimes <- system.time({
dx <- ddmatrix("rnorm", nrow=N, ncol=p, bldim=bldim, mean=mean, sd=sd, ICTXT=0)
dy <- ddmatrix("rnorm", nrow=N, ncol=1, bldim=bldim, mean=mean, sd=sd, ICTXT=0)
})[3]
datatimes <- allreduce(datatimes, op='max')
size <- N*p*8/1024
unit <- "kb"
if (log10(size) > 3){
size <- size/1024
unit <- "mb"
}
if (log10(size) > 3){
size <- size/1024
unit <- "gb"
}
comm.cat(sprintf("\n%.2f %s of data generated in %.3f seconds\n\n", size, unit, datatimes), quiet=T)
times <- sapply(1:reps, function(.) system.time(lm.fit(x=dx, y=dy))[3])
total <- allreduce(sum(times), op='max')
avg <- total/reps
bench <- data.frame(operation="lm.fit(dx, dy)", mean.runtime=avg, total.runtime=total)
row.names(bench) <- ""
comm.print(bench, quiet=T)
finalize()
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