reading and processing files in parallel in R

Question

I am using the parallel library in R to process a large data set on which I am applying complex operations.

For the sake of providing a reproducible code, you can find below a simpler example:

#data generation
dir <- "C:/Users/things_to_process/"

setwd(dir)
for(i in 1:800)
{
    my.matrix <- matrix(runif(100),ncol=10,nrow=10)

    saveRDS(my.matrix,file=paste0(dir,"/matrix",i))
}

#worker function
worker.function <- function(files)
{
    files.length <- length(files)
    partial.results <- vector('list',files.length)

    for(i in 1:files.length)
    {
        matrix <- readRDS(files[i])
        partial.results[[i]] <- sum(diag(matrix))
    }

    Reduce('+',partial.results) 
}


#master part
cl <- makeCluster(detectCores(), type = "PSOCK")

file_list <- list.files(path=dir,recursive=FALSE,full.names=TRUE)

part <- clusterSplit(cl,seq_along(file_list))
files.partitioned <- lapply(part,function(p) file_list[p])

results <- clusterApply(cl,files.partitioned,worker.function)

result <- Reduce('+',results)

Essentially, I am wondering if trying to read files in parallel would be done in an interleaved fashion instead. And if, as a result, this bottleneck would cut down on the expected performance of running tasks in parallel?

Would it be better if I first read all matrices at once in a list then sent chunks of this list to each core for it to be processed? what if these matrices were much larger, would I be able to load all of them in a list at once ?

Answer 1

Instead of saving each matrix in a separate RDS file, have you tried saving a list of N matrices in each file, where N is the number that is going to be processed by a single worker?

Then the worker.function looks like:

worker.function <- function(file) {
    matrix_list <- readRDS(file)
    partial_results <- lapply(matrix_list, function(mat) sum(diag(mat)))
    Reduce('+',partial.results)
}

You should save some time on I/O and maybe even on computation by replacing a for with a lapply.