Reputation: 14309
I have a strong use case for parallelizing a flavor of the SGD algorithm. In such use-case I need to update the matrices P and Q with the delta gradient update and for a random batch of samples. Each process will update mutually exclusive indices on both matrices.
A simple illustration of what I intend to do would be something like this:
# create "big" matrix
A <- matrix(rnorm(10000), 100, 100)
system.time(
# update each row vector independently using all my cores
r <- mclapply(1:100, mc.cores = 6, function(i) {
# updating ...
A[i,] <- A[i,] - 0.01
# return something, i.e. here I'd return the RMSE of this batch instead
sqrt(sum(A[i,]^2))
})
)
Are there any drawbacks on using this approach? are there more R-idiomatic alternatives?
For example, to be clean (i.e. no side effects, immutable computation) returning the update A[i,] - 0.01
instead of the RMSE
would be more complex to program and peak on memory usage or even run out of memory.
Upvotes: 0
Views: 301
Reputation: 11728
Reimplementing your code, by block, using shared data with package {bigstatsr}:
N <- 10e3
A <- matrix(rnorm(N * N), N)
library(bigstatsr)
bigA <- as_FBM(A)
library(doParallel)
registerDoParallel(cl <- makeCluster(4))
system.time(
r <- foreach(i = seq_len(N), .combine = 'c') %dopar% {
# updating ...
A[i,] <- A[i,] - 0.01
# return something, i.e. here I'd return the RMSE of this batch instead
sqrt(sum(A[i,]^2))
}
) # 11 sec
stopCluster(cl)
registerDoParallel(cl <- makeCluster(4))
system.time(
r2 <- big_apply(bigA, function(X, ind) {
# updating ...
tmp <- bigA[ind, ] <- bigA[ind, ] - 0.01
# return something, i.e. here I'd return the RMSE of this batch instead
sqrt(rowSums(tmp^2))
}, a.combine = 'c')
) # 1 sec
stopCluster(cl)
all.equal(r, r2) # TRUE
Again, it would be better to update columns instead of rows.
Upvotes: 1