The function `mapreduce(f, op, X; dims = dim)`

can be used, but it seems that the parallel analog `pmapreduce`

from the package `ParallelUtilities`

does not support the `dims `

argument.

How would one use simple parallel functions to perform the same operation and still get a big speed up?

Especially when `X`

is of dims `(very very big, big, very big)`

(say `(10^5, 10^2, 10^3)`

) and the reduction happens on the first (largest) dimension. `f`

are about `400ns`

and `op`

are typically `+`

.

Example of what I am doing

```
N = 10^5
n = 100
M = 1000
X = fill(rand(), N, M, n)
x = mapreduce(r -> sin(r, a, b), +, X, dims = 1)
# * What I want * #
using ParallelUtilities
x = pmapreduce(r -> sin(r, a, b), +, X, dims = 1)
```