# Pmapreduce with the keyword argument "dims"

**URL:** https://discourse.julialang.org/t/pmapreduce-with-the-keyword-argument-dims/76256
**Category:** General Usage
**Tags:** parallel, pmap
**Created:** [February 11, 2022, 3:19pm UTC](https://discourse.julialang.org/t/pmapreduce-with-the-keyword-argument-dims/76256 "2022-02-11T15:19:29Z")
**Posts on this page:** 1
**Page:** 1

<div class="post-metadata">

### Author: ![dmetivie](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/dmetivie/32/6926_2.png) [@dmetivie](https://discourse.julialang.org/u/dmetivie)
#### Post date: [February 11, 2022, 3:19pm UTC](https://discourse.julialang.org/t/pmapreduce-with-the-keyword-argument-dims/76256/1 "2022-02-11T15:19:29Z")

</div>

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

```julia
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)

```