# GPU randn way slower than rand?

**URL:** https://discourse.julialang.org/t/gpu-randn-way-slower-than-rand/18236
**Category:** Performance
**Tags:** gpu, cuda
**Created:** [December 3, 2018, 5:16am UTC](https://discourse.julialang.org/t/gpu-randn-way-slower-than-rand/18236 "2018-12-03T05:16:36Z")
**Posts on this page:** 1
**Showing post:** 3

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### Author: ![Zhiye\_Xia](https://avatars.discourse-cdn.com/v4/letter/z/bcef8e/32.png) [@Zhiye\_Xia](https://discourse.julialang.org/u/Zhiye_Xia)
#### Post date: [December 3, 2018, 6:07am UTC](https://discourse.julialang.org/t/gpu-randn-way-slower-than-rand/18236/3 "2018-12-03T06:07:05Z")

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Hi Tim,

Thanks for the reply.  
I understand that testing once in global is not accurate, it’s just that the difference is too large to be normal.

Here the new result following your code

```julia
ac = Array{Float64}(undef, 2^20)
ag = cu(ac)
@btime randn!(ac)
@btime CuArrays.@sync randn!(ag)

```

6.788 ms (0 allocations: 0 bytes)  
7.626 s (5242883 allocations: 288.00 MiB)

So the allocations are clearly the problem, but I can’t really figure out the reason.

Even with rand there are some extra allocations

```julia
@btime rand!(ac)
@btime CuArrays.@sync rand!(ag)

```

957.006 μs (0 allocations: 0 bytes)  
5.205 μs (38 allocations: 1.48 KiB)

Do you have any idea where the problem might be?

I’m using a 840m on my laptop with 2GB of memory, however, I don’t think array size is the problem either.

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