# Complex numbers on GPU

**URL:** <https://discourse.julialang.org/t/complex-numbers-on-gpu/12776>\
**Category:** New to Julia\
**Tags:** gpu, complex-numbers\
**Created:** [August 1, 2018, 4:04am UTC](https://discourse.julialang.org/t/complex-numbers-on-gpu/12776 "2018-08-01T04:04:36Z")\
**Posts on this page:** 4\
**Page:** 1

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**Author:** ![Malay\_Das](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/malay_das/32/9634_2.png) [@Malay\_Das](https://discourse.julialang.org/u/Malay_Das)\
**Post date:** [August 1, 2018, 4:04am UTC](https://discourse.julialang.org/t/complex-numbers-on-gpu/12776/1 "2018-08-01T04:04:36Z")

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Hello,

I am trying to work on complex numbers.

_a=[0 1;-1 0]_  
_@time f=eigfact(a) #takes 0.000092 seconds_  
_a=convert(Array{Complex64,2},a)_  
_@time f=eigfact(a) #takes 0.062753 seconds_

And then move the computation to GPU which does not seem to work.

_using CUDAnative_  
_using CuArrays_  
_b=cu(a)_  
_@time f=eigfact(b) #Throws following error message_

#=  
julia\> @time f=eigfact(b)  
ERROR: InexactError()  
Stacktrace:  
[1] trunc(::Type{Int64}, ::Float64) at ./float.jl:672  
[2] poolidx(::Int64) at /home/malay/.julia/v0.6/CuArrays/src/memory.jl:31  
[3] alloc(::Int64) at /home/malay/.julia/v0.6/CuArrays/src/memory.jl:203  
[4] Type at /home/malay/.julia/v0.6/CuArrays/src/array.jl:34 [inlined]  
[5] similar at /home/malay/.julia/v0.6/CuArrays/src/array.jl:44 [inlined]  
[6] similar at ./abstractarray.jl:524 [inlined]  
[7] geevx!(::Char, ::Char, ::Char, ::Char, ::CuArray{Complex{Float32},2}) at ./linalg/lapack.jl:2108  
[8] #eigfact!#38(::Bool, ::Bool, ::Function, ::CuArray{Complex{Float32},2}) at ./linalg/eigen.jl:65  
[9] (::Base.LinAlg.#kw##eigfact!)(::Array{Any,1}, ::Base.LinAlg.#eigfact!, ::CuArray{Complex{Float32},2}) at ./:0  
[10] #eigfact#39(::Bool, ::Bool, ::Function, ::CuArray{Complex{Float16},2}) at ./linalg/eigen.jl:103  
[11] eigfact(::CuArray{Complex{Float16},2}) at ./linalg/eigen.jl:102

> [@Computing eigenvalues/eigenvectors using GPU?](https://discourse.julialang.org/t/computing-eigenvalues-eigenvectors-using-gpu/12396/2):
>
> Short answer: AFAICT there isn’t even a not-simple solution. To use a GPU effectively for dense eigensystem computations is quite difficult. The functionality was only recently added to the CUDA libraries (useless to you) and is conspicuously absent from the prominent projects using OpenCL (e.g. ArrayFire). There was some work in the clMagma project, but that seems to have been abandoned well before recent upgrades to the AMD software stack. You say “decomposition”, so I assume you mean for d…

=#

I even tried a sine function which errored as well

_CUDAnative.sin(a)_

ERROR: MethodError: no method matching sin(::Array{Complex{Float32},2})  
You may have intended to import Base.sin  
Closest candidates are:  
sin(::Float32) at /home/malay/.julia/v0.6/CUDAnative/src/device/libdevice.jl:13  
sin(::Float64) at /home/malay/.julia/v0.6/CUDAnative/src/device/libdevice.jl:12  
sin(::ForwardDiff.Dual{T,V,N} where N where V\<:Real) where T at /home/malay/.julia/v0.6/ForwardDiff/src/dual.jl:166

I am not sure if my approach is incorrect or my installation is incorrect or the functionality is not implemented yet. Please advise.  
I am on Linux Mint, Cuda 9.1, Julia 6.4, gtx1060

My motive is to find the eigenvalues of complex matrixes and diagonalize them and then perform further calculations. I was thinking of moving the calculations to GPU to expedite them. I am not sure if GPU acceleration is possible for all complex number operations. I did see another thread [here](https://discourse.julialang.org/t/computing-eigenvalues-eigenvectors-using-gpu/12396/2) which states that eigen decomposition is not possible.

Futher, if someone has a nice documentation link for CuArrays and GPUArrays, it would be nice.  
Thanks in advance.

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<div class="post-metadata">

**Author:** ![rveltz](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/rveltz/32/2707_2.png) [@rveltz](https://discourse.julialang.org/u/rveltz)\
**Post date:** [August 1, 2018, 7:49am UTC](https://discourse.julialang.org/t/complex-numbers-on-gpu/12776/2 "2018-08-01T07:49:08Z")

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There is a documentation for `GPUArrays`. For `GPUArrays` and `CuArrays`, you sould look at their examples. In your case, `CUDAnative.sin(a)` should be replaced by `CuArrays.sin(a)`.

Finally, the function you are looking may exist in [cusolver](https://docs.nvidia.com/cuda/cusolver/index.html). It is inside `CuArrays`. Have a look at the tests and sources.

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**Author:** ![maleadt](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/maleadt/32/10097_2.png) [@maleadt](https://discourse.julialang.org/u/maleadt)\
**Post date:** [August 1, 2018, 8:22am UTC](https://discourse.julialang.org/t/complex-numbers-on-gpu/12776/3 "2018-08-01T08:22:53Z")

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> [@rveltz](#):
>
> In your case, `CUDAnative.sin(a)` should be replaced by `CuArrays.sin(a)`

That’s not accurate. `CUDAnative.sin` is meant to be used in device code, working with scalars. That’s why OP got the `MethodError` trying to apply it to an `Array`. `CuArrays.sin` does not exist, and just refers to `Base.sin`, which can be called on an Array but that’s deprecated. You should be broadcasting `sin`, in which case you _have_ to use `CUDAnative.sin` when dealing with a `CuArray` (because of [Rewrite calls to eg. `Base.sin` · Issue #112 · JuliaGPU/GPUCompiler.jl · GitHub](https://github.com/JuliaGPU/CUDAnative.jl/issues/27))

```julia
julia> CuArrays.sin === Base.sin
true

# not meant to be used on arrays
julia> CUDAnative.sin(a)
ERROR: MethodError: no method matching sin(::CuArray{Float32,1})

# deprecated, and calls the wrong sin function
julia> Base.sin(a)
WARNING: sin(x::AbstractArray{T}) where T <: Number is deprecated, use sin.(x) instead.
ERROR: CUDA error: an illegal memory access was encountered (code #700, ERROR_ILLEGAL_ADDRESS)

# correct invocation
julia> CUDAnative.sin.(a)
1-element CuArray{Float32,1}:
 0.841471

```

> [@Malay\_Das](#):
>
> ```julia
> ERROR: InexactError()
> Stacktrace:
> [1] trunc(::Type{Int64}, ::Float64) at ./float.jl:672
> [2] poolidx(::Int64) at /home/malay/.julia/v0.6/CuArrays/src/memory.jl:31
> [3] alloc(::Int64) at /home/malay/.julia/v0.6/CuArrays/src/memory.jl:203
> [4] Type at /home/malay/.julia/v0.6/CuArrays/src/array.jl:34 [inlined]
> [5] similar at /home/malay/.julia/v0.6/CuArrays/src/array.jl:44 [inlined]
> [6] similar at ./abstractarray.jl:524 [inlined]
> [7] geevx!(::Char, ::Char, ::Char, ::Char, ::CuArray{Complex{Float32},2}) at ./linalg/lapack.jl:2108
> [8] #eigfact!#38(::Bool, ::Bool, ::Function, ::CuArray{Complex{Float32},2}) at ./linalg/eigen.jl:65
> [9] (::Base.LinAlg.#kw##eigfact!)(::Array{Any,1}, ::Base.LinAlg.#eigfact!, ::CuArray{Complex{Float32},2}) at ./:0
> [10] #eigfact#39(::Bool, ::Bool, ::Function, ::CuArray{Complex{Float16},2}) at ./linalg/eigen.jl:103
> [11] eigfact(::CuArray{Complex{Float16},2}) at ./linalg/eigen.jl:102
> 
> ```

That’s a bug in CuArrays. I don’t have time to look into this, but I’ve filed an issue: [InexactError in pool allocator · Issue #101 · JuliaGPU/CuArrays.jl · GitHub](https://github.com/JuliaGPU/CuArrays.jl/issues/101)

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<div class="post-metadata">

**Author:** ![Malay\_Das](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/malay_das/32/9634_2.png) [@Malay\_Das](https://discourse.julialang.org/u/Malay_Das)\
**Post date:** [August 1, 2018, 11:36pm UTC](https://discourse.julialang.org/t/complex-numbers-on-gpu/12776/4 "2018-08-01T23:36:41Z")

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Thanks a lot for both of your response. I appreciate it. It helped me.  
I will track the issue on github.