# Parallel is very slow

**URL:** <https://discourse.julialang.org/t/parallel-is-very-slow/9443>\
**Category:** General Usage\
**Tags:** parallel\
**Created:** [March 2, 2018, 8:45am UTC](https://discourse.julialang.org/t/parallel-is-very-slow/9443 "2018-03-02T08:45:59Z")\
**Posts on this page:** 1\
**Showing post:** 5

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**Author:** ![tkoolen](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/tkoolen/32/1603_2.png) [@tkoolen](https://discourse.julialang.org/u/tkoolen)\
**Post date:** [March 7, 2018, 1:46pm UTC](https://discourse.julialang.org/t/parallel-is-very-slow/9443/5 "2018-03-07T13:46:32Z")

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That doesn’t seem to match what the documentation says. See [https://docs.julialang.org/en/stable/manual/parallel-computing/#Parallel-Map-and-Loops-1](https://docs.julialang.org/en/stable/manual/parallel-computing/#Parallel-Map-and-Loops-1), last paragraph:

> `@parallel for` can handle situations where each iteration is tiny, perhaps merely summing two numbers.

When I was trying out Julia’s parallel computing capabilities a few weeks ago, I was also surprised by a lack of performance, both with `@parallel` and with `@threads`. For `@threads`, it turned out to be completely due to the closure performance issue (see [Parallelizing for loop in the computation of a gradient - #7 by tkoolen](https://discourse.julialang.org/t/parallelizing-for-loop-in-the-computation-of-a-gradient/9154/7)). I didn’t look into the details of `@parallel`, but does it also generate a closure?

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