R bigmemory always use backing file? -


we trying use bigmemory library foreach parallel our analysis. however, as.big.matrix function seems use backingfile. our workstations have enough memory, there way use bigmemory without backing file?

this code x.big.desc <-describe(as.big.matrix(x)) pretty slow write data c:\programdata\boost_interprocess\. somehow slower save x directly, as.big.matrix have slower i/o?

this code x.big.desc <-describe(as.big.matrix(x, backingfile = "")) pretty fast, however, save copy of data %tmp% directory. think reason fast, because r kick off background writing process, instead of writing data. (we can see writing thread in taskmanager after r prompt returns).

is there way use bigmemory ram only, each worker in foreach loop can access data via ram?

thanks help.

so, if have enough ram, use standard r matrices. pass part of each matrix each cluster, use rdsfiles.

one example computing colsums 3 cores:

# functions splitting cutbysize <- function(m, nb) {   int <- m / nb    upper <- round(1:nb * int)   lower <- c(1, upper[-nb] + 1)   size <- c(upper[1], diff(upper))    cbind(lower, upper, size) } seq2 <- function(lims) seq(lims[1], lims[2])  # matrix bm <- matrix(1, 10e3, 1e3) ncores <- 3 intervals <- cutbysize(ncol(bm), ncores) # save each part in different file tmpfile <- tempfile() (ic in seq_len(ncores)) {   saverds(bm[, seq2(intervals[ic, ])],            paste0(tmpfile, ic, ".rds")) } # parallel computation reading 1 part @ beginning cl <- parallel::makecluster(ncores) doparallel::registerdoparallel(cl) library(foreach) colsums <- foreach(ic = seq_len(ncores), .combine = 'c') %dopar% {   bm.part <- readrds(paste0(tmpfile, ic, ".rds"))   colsums(bm.part) } parallel::stopcluster(cl) # checking results all.equal(colsums, colsums(bm))

you use rm(bm); gc() after writing parts disk.


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