# ERROR: InvalidIRError: compiling reduce\_kernel with \`eigen\` command

**URL:** <https://discourse.julialang.org/t/error-invalidirerror-compiling-reduce-kernel-with-eigen-command/28307>\
**Category:** GPU\
**Tags:** cudanative, cuda\
**Created:** [September 3, 2019, 12:14am UTC](https://discourse.julialang.org/t/error-invalidirerror-compiling-reduce-kernel-with-eigen-command/28307 "2019-09-03T00:14:13Z")\
**Posts on this page:** 5\
**Page:** 1

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**Author:** ![sergevic](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/sergevic/32/9958_2.png) [@sergevic](https://discourse.julialang.org/u/sergevic)\
**Post date:** [September 3, 2019, 12:14am UTC](https://discourse.julialang.org/t/error-invalidirerror-compiling-reduce-kernel-with-eigen-command/28307/1 "2019-09-03T00:14:13Z")

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I’m trying to convert my Julia code with CUDA programming, because I get a `OutOfMemory()` error when I run the original code on Julia v.1.2. My converted code starts with the following lines:

> using CuArrays  
> using Distances  
> using LinearAlgebra  
> using Distributions  
> data=Float32.(rand(10000,15))  
> Eucldist=pairwise(Euclidean(),data,dims=1)  
> D=maximum(Eucldist.^2)  
> sigma2hat=mean(((Eucldist.^2)./D)[tril!(trues(size((Eucldist.^2)./D)),-1)])  
> L=exp.(-(Eucldist.^2/D)/(2\*sigma2hat))  
> L=cu(L)  
> K=eigen(L)

With the last command, I get the following error:

> ┌ Warning: Performing scalar operations on GPU arrays: This is very slow, consider disallowing these operations with `allowscalar(false)`  
> └ @ GPUArrays C:\Users\User.julia\packages\GPUArrays\J4c3Q\src\indexing.jl:16  
> ERROR: InvalidIRError: compiling reduce\_kernel(CuArrays.CuKernelState, typeof(==), typeof(&), Bool, CuDeviceArray{Float32,2,CUDAnative.AS.Global}, Val{256}, CuDeviceArray{Bool,1,CUDAnative.AS.Global}, Adjoint{Float32,CuDeviceArray{Float32,2,CUDAnative.AS.Global}}) resulted in invalid LLVM IR  
> Reason: unsupported dynamic function invocation (call to simple\_broadcast\_index)  
> Stacktrace:  
> [1] reduce\_kernel at C:\Users\User.julia\packages\GPUArrays\J4c3Q\src\mapreduce.jl:141  
> Stacktrace:  
> [1] check\_ir(::CUDAnative.CompilerJob, ::LLVM.Module) at C:\Users\User.julia\packages\CUDAnative\LkH1v\src\compiler\validation.jl:114  
> [2] macro expansion at C:\Users\User.julia\packages\CUDAnative\LkH1v\src\compiler\driver.jl:188 [inlined]  
> [3] macro expansion at C:\Users\User.julia\packages\TimerOutputs\7zSea\src\TimerOutput.jl:216 [inlined]  
> [4] #codegen#130(::Bool, ::Bool, ::Bool, ::Bool, ::Bool, ::typeof(CUDAnative.codegen), ::Symbol, ::CUDAnative.CompilerJob) at C:\Users\User.julia\packages\CUDAnative\LkH1v\src\compiler\driver.jl:186  
> [5] #codegen at .\none:0 [inlined]  
> [6] #compile#129(::Bool, ::Bool, ::Bool, ::Bool, ::Bool, ::typeof(CUDAnative.compile), ::Symbol, ::CUDAnative.CompilerJob) at C:\Users\User.julia\packages\CUDAnative\LkH1v\src\compiler\driver.jl:47  
> [7] #compile#128 at .\none:0 [inlined]  
> [8] #compile at .\none:0 [inlined] (repeats 2 times)  
> [9] macro expansion at C:\Users\User.julia\packages\CUDAnative\LkH1v\src\execution.jl:389 [inlined]  
> [10] #cufunction#170(::Nothing, ::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{,Tuple{}}}, ::typeof(cufunction), ::typeof(GPUArrays.reduce\_kernel), ::Type{Tuple{CuArrays.CuKernelState,typeof(==),typeof(&),Bool,CuDeviceArray{Float32,2,CUDAnative.AS.Global},Val{256},CuDeviceArray{Bool,1,CUDAnative.AS.Global},Adjoint{Float32,CuDeviceArray{Float32,2,CUDAnative.AS.Global}}}}) at C:\Users\User.julia\packages\CUDAnative\LkH1v\src\execution.jl:357  
> [11] cufunction(::Function, ::Type) at C:\Users\User.julia\packages\CUDAnative\LkH1v\src\execution.jl:357  
> [12] macro expansion at C:\Users\User.julia\packages\CUDAnative\LkH1v\src\execution.jl:174 [inlined]  
> [13] macro expansion at .\gcutils.jl:87 [inlined]  
> [14] macro expansion at C:\Users\User.julia\packages\CUDAnative\LkH1v\src\execution.jl:171 [inlined]  
> [15] \_gpu\_call(::CuArrays.CuArrayBackend, ::Function, ::CuArray{Bool,1}, ::Tuple{typeof(==),typeof(&),Bool,CuArray{Float32,2},Val{256},CuArray{Bool,1},Adjoint{Float32,CuArray{Float32,2}}}, ::Tuple{Tuple{Int64},Tuple{Int64}}) at C:\Users\User.julia\packages\CuArrays\wXQp8\src\gpuarray\_interface.jl:60  
> [16] gpu\_call(::Function, ::CuArray{Bool,1}, ::Tuple{typeof(==),typeof(&),Bool,CuArray{Float32,2},Val{256},CuArray{Bool,1},Adjoint{Float32,CuArray{Float32,2}}}, ::Tuple{Tuple{Int64},Tuple{Int64}}) at C:\Users\User.julia\packages\GPUArrays\J4c3Q\src\abstract\_gpu\_interface.jl:151  
> [17] acc\_mapreduce(::Function, ::Function, ::Bool, ::CuArray{Float32,2}, ::Tuple{Adjoint{Float32,CuArray{Float32,2}}}) at C:\Users\User.julia\packages\GPUArrays\J4c3Q\src\mapreduce.jl:186  
> [18] ishermitian at C:\Users\User.julia\packages\GPUArrays\J4c3Q\src\mapreduce.jl:15 [inlined]  
> [19] issymmetric at C:\cygwin\home\Administrator\buildbot\worker\package\_win64\build\usr\share\julia\stdlib\v1.2\LinearAlgebra\src\generic.jl:1009 [inlined]  
> [20] #eigen!#56(::Bool, ::Bool, ::typeof(LinearAlgebra.eigsortby), ::typeof(eigen!), ::CuArray{Float32,2}) at C:\cygwin\home\Administrator\buildbot\worker\package\_win64\build\usr\share\julia\stdlib\v1.2\LinearAlgebra\src\eigen.jl:53  
> [21] #eigen! at .\none:0 [inlined]  
> [22] #eigen#58(::Bool, ::Bool, ::typeof(LinearAlgebra.eigsortby), ::typeof(eigen), ::CuArray{Float32,2}) at C:\cygwin\home\Administrator\buildbot\worker\package\_win64\build\usr\share\julia\stdlib\v1.2\LinearAlgebra\src\eigen.jl:139  
> [23] eigen(::CuArray{Float32,2}) at C:\cygwin\home\Administrator\buildbot\worker\package\_win64\build\usr\share\julia\stdlib\v1.2\LinearAlgebra\src\eigen.jl:137  
> [24] top-level scope at none:0

According to [https://github.com/JuliaGPU/CuArrays.jl](https://github.com/JuliaGPU/CuArrays.jl) “because `CuArray` is an `AbstractArray` , it doesn’t have much of a learning curve; just use your favourite array ops as usual.” So what am I dong wrong if I cannot perform a simple eigendecomposition…? And that’s just the beginning of my code…

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<div class="post-metadata">

**Author:** ![vchuravy](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/vchuravy/32/8_2.png) [@vchuravy](https://discourse.julialang.org/u/vchuravy)\
**Post date:** [September 3, 2019, 12:25am UTC](https://discourse.julialang.org/t/error-invalidirerror-compiling-reduce-kernel-with-eigen-command/28307/2 "2019-09-03T00:25:12Z")

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CuArrays indeed aims to provide a relatively simple programming model for GPUs. We generally aim to have linear algebra and `broadcast`/`map` just work for users, but that doesn’t mean all packages just work. Examples are functions that are for loops over GPU memory and therefore do scalar indexing into GPU memory (that will be rather slow).

Can you post the full stacktrace? It sounds like somewhere something is calling `pointer` on CuArray and that is not possible, since most pieces of code that operate on `Ptr` expect it to be a CPU address.

I started answering your post before you updated it.

* * *

The updated snippet indeed seems like a bug in probably GPUArrays.jl

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<div class="post-metadata">

**Author:** ![sergevic](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/sergevic/32/9958_2.png) [@sergevic](https://discourse.julialang.org/u/sergevic)\
**Post date:** [September 3, 2019, 12:28am UTC](https://discourse.julialang.org/t/error-invalidirerror-compiling-reduce-kernel-with-eigen-command/28307/3 "2019-09-03T00:28:15Z")

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I put the whole stacktrace in my post 🙂 From `[1]` to `[24]`

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**Author:** ![vchuravy](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/vchuravy/32/8_2.png) [@vchuravy](https://discourse.julialang.org/u/vchuravy)\
**Post date:** [September 3, 2019, 12:28am UTC](https://discourse.julialang.org/t/error-invalidirerror-compiling-reduce-kernel-with-eigen-command/28307/4 "2019-09-03T00:28:48Z")

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Yeah that was a response to version 1 of your post 🙂

[https://github.com/JuliaGPU/GPUArrays.jl/issues/201](https://github.com/JuliaGPU/GPUArrays.jl/issues/201)

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<div class="post-metadata">

**Author:** ![sergevic](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/sergevic/32/9958_2.png) [@sergevic](https://discourse.julialang.org/u/sergevic)\
**Post date:** [September 3, 2019, 12:31am UTC](https://discourse.julialang.org/t/error-invalidirerror-compiling-reduce-kernel-with-eigen-command/28307/5 "2019-09-03T00:31:34Z")

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Yes 🙂 I tried to put more lines of my code 🙂