Rank-based p-value on large dataset

I have a fairly simple problem--I have about 80,000 values (call them
y) that I am using as an empirical distribution and I want to find
the p-value (never mind the multiple testing issues here, for the
time being) of 130,000 points (call them x) from the empirical
distribution. I typically do that (for one-sided test) something like

loop over i in x
p.val[i] = sum(y>x[i])/length(y)

and repeat for all i.  However, length(x) is large here as is
length(y), so this process takes quite a long time.  Any suggestions?

x <- rnorm(130000)
y <- rnorm(80000)
system.time(p.val <- 1 - ecdf(y)(x))