doCallParallel: Call a function with a vectorized input in parallel
In Smisc: Sego Miscellaneous

Description Usage Arguments Details Value Author(s) Examples

Call a function with a vectorized input in parallel, where the function is computationally intensive.

1 2	doCallParallel(fun, x, ..., njobs = parallel::detectCores() - 1, random.seed = NULL)

`fun`	A function, or a text string with the name of the function, whose first argument is a vector and returns a corresponding vector
`x`	A vector of values that is the first argument to `fun`
`...`	Additional named arguments for `fun`
`njobs`	The number of parallel jobs to spawn using `parLapplyW`.
`random.seed`	If a numeric value is provided, `x` is randomized to better distribute the work among the jobs if some values of `x` take longer to evaluate than others. The original ordering is restored before `fun(x, ...)` is returned. If `NULL`, no randomization is performed.

This function is a parallelized wrapper for do.call designed for the case where fun is computationally intensive. Each element of x is evaluated independently of the other elements of x. Thus, fun(c(x1,x2)) must be equivalent to c(fun(x1), fun(x2)) in order for doCallParallel to work properly.

The same result that would be had by calling fun(x, ...), except calculated in parallel

Landon Sego

# Get a vector of x's
x <- rnorm(18, mean = 2, sd = 2)

# 2 cores
y1 <- doCallParallel("pnorm", x, mean = 2, sd = 2, njobs = 2)

# 2 cores and randomization
y2 <- doCallParallel(pnorm, x, mean = 2, sd = 2, njobs = 2, random.seed = 1)

# Without using doCallParallel()
y3 <- pnorm(x, mean = 2, sd = 2)

# Comparisons
identical(y1, y2)
identical(y1, y3)