# Writing fast stencil computation kernels that work on both CPUs and GPUs

**URL:** <https://discourse.julialang.org/t/writing-fast-stencil-computation-kernels-that-work-on-both-cpus-and-gpus/20200>\
**Category:** GPU\
**Created:** [January 28, 2019, 11:42pm UTC](https://discourse.julialang.org/t/writing-fast-stencil-computation-kernels-that-work-on-both-cpus-and-gpus/20200 "2019-01-28T23:42:23Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**Author:** ![PolarizedPoutine](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/polarizedpoutine/32/6067_2.png) [@PolarizedPoutine](https://discourse.julialang.org/u/PolarizedPoutine)\
**Post date:** [January 29, 2019, 12:40am UTC](https://discourse.julialang.org/t/writing-fast-stencil-computation-kernels-that-work-on-both-cpus-and-gpus/20200/2 "2019-01-29T00:40:11Z")

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I think I may have found an answer using `@views` after skimming through [Tim’s awesome presentation](https://discourse.julialang.org/t/presentation-on-effective-use-of-cudanative-cuarrays/18919)!

```julia
function δx!(f, δxf)
    @views @. δxf[2:end, :, :] = f[2:end, :, :] - f[1:end-1, :, :]
    @views @. δxf[end, :, :] = f[1, :, :] - f[end, :, :]
    nothing
end

```

which is almost as fast on the CPU and gives a decent ~10x speedup on a GPU

```julia
~ julia
julia> using BenchmarkTools
julia> Nx, Ny, Nz = 128, 128, 128;
julia> xc = rand(Nx, Ny, Nz); yc = zeros(Nx, Ny, Nz);
julia> @benchmark δx!($xc, $yc)
BenchmarkTools.Trial:
  memory estimate: 384 bytes
  allocs estimate: 6
  --------------
  minimum time: 1.645 ms (0.00% GC)
  median time: 1.662 ms (0.00% GC)
  mean time: 1.667 ms (0.00% GC)
  maximum time: 3.740 ms (0.00% GC)
  --------------
  samples: 2996
  evals/sample: 1

~ julia
julia> using CUDAnative, CUDAdrv, CuArrays, BenchmarkTools
julia> Nx, Ny, Nz = 128, 128, 128;
julia> xg = cu(rand(Nx, Ny, Nz)); yg = cu(zeros(Nx, Ny, Nz));
julia> @benchmark δx!($xg, $yg)
BenchmarkTools.Trial:
  memory estimate: 4.84 KiB
  allocs estimate: 70
  --------------
  minimum time: 9.721 μs (0.00% GC)
  median time: 178.504 μs (0.00% GC)
  mean time: 169.339 μs (0.27% GC)
  maximum time: 2.521 ms (99.15% GC)
  --------------
  samples: 10000
  evals/sample: 1

```

although I’m interested to look into why there’s such a big spread in run times. And apparently `@inbounds` slows things down on the CPU.

I know double posting is somewhat taboo but I figured this is closer to a solution than an edit.

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_[View the full topic](https://discourse.julialang.org/t/writing-fast-stencil-computation-kernels-that-work-on-both-cpus-and-gpus/20200)._