# KernelError: recursion is currently not supported
**URL:** https://discourse.julialang.org/t/kernelerror-recursion-is-currently-not-supported/33233
**Category:** GPU
**Created:** [January 11, 2020, 7:42am UTC](https://discourse.julialang.org/t/kernelerror-recursion-is-currently-not-supported/33233 "2020-01-11T07:42:31Z")
**Posts on this page:** 5
**Page:** 1
### Author:  [@FujiwaraTakumiEH](https://discourse.julialang.org/u/FujiwaraTakumiEH)
#### Post date: [January 11, 2020, 7:42am UTC](https://discourse.julialang.org/t/kernelerror-recursion-is-currently-not-supported/33233/1 "2020-01-11T07:42:32Z")
### Background:
> I changed my task to a simple example for description:
I will take every element in array x, make it into a third-order matrix and calculate its determinant, and then assign the value of the determinant to array y.
\begin{equation} x=\left[\begin{array}{c} {x\_{1}} \\ {x\_{2}} \\ {\vdots} \\ {x\_{i}} \end{array}\right], \operatorname{mat}[i]=\left[\begin{array}{ccc} {x[i]+1} & {x[i]+2} & {x[i]+5} \\ {x[i]+1} & {x[i]+0} & {x[i]+4} \\ {x[i]+2} & {x[i]+3} & {x[i]+2} \end{array}\right], y=\left[\begin{array}{c} {\operatorname{mat}[1]} \\ {\operatorname{mat}[2]} \\ {\vdots} \\ {\operatorname{mat}[i]} \end{array}\right] \end{equation}
### My code:
```julia
using CuArrays
using CUDAnative
using LinearAlgebra
function test(x)
mat = [x+1 x+2 x+5
x+1 x+0 x+4
x+2 x+3 x+2]
c = det(mat)
return c
end
function kernel!(x,y)
index = (blockIdx().x - 1) * blockDim().x + threadIdx().x
stride = blockDim().x * gridDim().x
for i = index:stride:size(x,1)
y[i] = test(x[i])
end
return nothing
end
x = rand(10000)
y = zeros(10000)
d_x = cu(x)
d_y = cu(y)
numblocks = ceil(Int, size(x, 1)/256)
@cuda threads = 256 blocks = numblocks kernel!(d_x,d_y)
```
### Result:
```julia
GPU compilation of kernel!(CuDeviceArray{Float32,1,CUDAnative.AS.Global}, CuDeviceArray{Float32,1,CUDAnative.AS.Global}) failed
KernelError: recursion is currently not supported
Try inspecting the generated code with any of the @device_code_... macros.
Stacktrace:
[1] mapreduce_impl at reduce.jl:148 (repeats 2 times)
[2] det at D:\buildbot\worker\package_win64\build\usr\share\julia\stdlib\v1.3\LinearAlgebra\src\triangular.jl:2525
[3] det at D:\buildbot\worker\package_win64\build\usr\share\julia\stdlib\v1.3\LinearAlgebra\src\generic.jl:1421
[4] kernel! at In[3]:2
Stacktrace:
[1] (::CUDAnative.var"#hook_emit_function#100"{CUDAnative.CompilerJob,Array{Core.MethodInstance,1}})(::Core.MethodInstance, ::Core.CodeInfo, ::UInt64) at C:\Users\zenan\.julia\packages\CUDAnative\Phjco\src\compiler\irgen.jl:102
[2] compile_method_instance(::CUDAnative.CompilerJob, ::Core.MethodInstance, ::UInt64) at C:\Users\zenan\.julia\packages\CUDAnative\Phjco\src\compiler\irgen.jl:149
[3] macro expansion at C:\Users\zenan\.julia\packages\TimerOutputs\7Id5J\src\TimerOutput.jl:228 [inlined]
[4] irgen(::CUDAnative.CompilerJob, ::Core.MethodInstance, ::UInt64) at C:\Users\zenan\.julia\packages\CUDAnative\Phjco\src\compiler\irgen.jl:163
[5] macro expansion at C:\Users\zenan\.julia\packages\TimerOutputs\7Id5J\src\TimerOutput.jl:228 [inlined]
[6] macro expansion at C:\Users\zenan\.julia\packages\CUDAnative\Phjco\src\compiler\driver.jl:99 [inlined]
[7] macro expansion at C:\Users\zenan\.julia\packages\TimerOutputs\7Id5J\src\TimerOutput.jl:228 [inlined]
[8] #codegen#156(::Bool, ::Bool, ::Bool, ::Bool, ::Bool, ::typeof(CUDAnative.codegen), ::Symbol, ::CUDAnative.CompilerJob) at C:\Users\zenan\.julia\packages\CUDAnative\Phjco\src\compiler\driver.jl:98
[9] #codegen at .\none:0 [inlined]
[10] #compile#155(::Bool, ::Bool, ::Bool, ::Bool, ::Bool, ::typeof(CUDAnative.compile), ::Symbol, ::CUDAnative.CompilerJob) at C:\Users\zenan\.julia\packages\CUDAnative\Phjco\src\compiler\driver.jl:47
[11] #compile#154 at .\none:0 [inlined]
[12] #compile at .\none:0 [inlined] (repeats 2 times)
[13] macro expansion at C:\Users\zenan\.julia\packages\CUDAnative\Phjco\src\execution.jl:392 [inlined]
[14] #cufunction#200(::Nothing, ::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}, ::typeof(cufunction), ::typeof(kernel!), ::Type{Tuple{CuDeviceArray{Float32,1,CUDAnative.AS.Global},CuDeviceArray{Float32,1,CUDAnative.AS.Global}}}) at C:\Users\zenan\.julia\packages\CUDAnative\Phjco\src\execution.jl:359
[15] cufunction(::Function, ::Type) at C:\Users\zenan\.julia\packages\CUDAnative\Phjco\src\execution.jl:359
[16] top-level scope at C:\Users\zenan\.julia\packages\CUDAnative\Phjco\src\execution.jl:176
[17] top-level scope at gcutils.jl:91
[18] top-level scope at C:\Users\zenan\.julia\packages\CUDAnative\Phjco\src\execution.jl:173
[19] top-level scope at In[5]:2
```
### Question:
The above example is very similar to the task I actually want to complete. Because I am new in using GPU, I cannot understand the error reporting. Excuse me, how should this mistake be solved? ๐
---
### Author:  [@maleadt](https://discourse.julialang.org/u/maleadt)
#### Post date: [January 11, 2020, 4:06pm UTC](https://discourse.julialang.org/t/kernelerror-recursion-is-currently-not-supported/33233/2 "2020-01-11T16:06:48Z")
You ar calling `det` on the GPU, which in turn calls `mapreduce`. That kind of functionality is not available within a kernel, where you can only do relatively simple computations. The array allocation in `test` is also not possible in a kernel. Just hard-code the expression to calculate the determinant of your 3x3 matrix. Or you could try using StaticArrays, which you can allocate in a kernel (since itโs stack based), and it looks like they provide a `det` method.
---
### Author:  [@FujiwaraTakumiEH](https://discourse.julialang.org/u/FujiwaraTakumiEH)
#### Post date: [January 13, 2020, 10:06am UTC](https://discourse.julialang.org/t/kernelerror-recursion-is-currently-not-supported/33233/3 "2020-01-13T10:06:14Z")
Thank you for your reply! ๐
1. I have understood the problem of solving determinants.
2.
> [@maleadt](#):
>
> The array allocation in `test` is also not possible in a kernel.
I found this problem. What should I do if I need to create a variable `mat` in `test(x)`, which will be a matrix of parameter `x`?
---
### Author:  [@maleadt](https://discourse.julialang.org/u/maleadt)
#### Post date: [January 13, 2020, 10:12am UTC](https://discourse.julialang.org/t/kernelerror-recursion-is-currently-not-supported/33233/4 "2020-01-13T10:12:59Z")
> [@FujiwaraTakumiEH](#):
>
> What should I do if I need to create a variable `mat` in `test(x)` , which will be a matrix of parameter `x` ?
This is not about creating a variable, but about allocating memory, which you (generally) cannot do in a kernel. So either pre-allocate and pass to your kernel, or use StaticArrays to have stack-allocated memory. You can also use CUDA shared memory, but these have specific semantics that you likely donโt want (values shared across threads in a block).
---
### Author:  [@FujiwaraTakumiEH](https://discourse.julialang.org/u/FujiwaraTakumiEH)
#### Post date: [January 13, 2020, 10:40am UTC](https://discourse.julialang.org/t/kernelerror-recursion-is-currently-not-supported/33233/5 "2020-01-13T10:40:07Z")
IIโve got it. Thanks again! ๐