# CUDA.math functions in Julia

**URL:** <https://discourse.julialang.org/t/cuda-math-functions-in-julia/62285>\
**Category:** New to Julia\
**Tags:** cuda\
**Created:** [June 2, 2021, 3:59pm UTC](https://discourse.julialang.org/t/cuda-math-functions-in-julia/62285 "2021-06-02T15:59:03Z")\
**Posts on this page:** 5\
**Page:** 1

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**Author:** ![fci](https://avatars.discourse-cdn.com/v4/letter/f/ce73a5/32.png) [@fci](https://discourse.julialang.org/u/fci)\
**Post date:** [June 2, 2021, 3:59pm UTC](https://discourse.julialang.org/t/cuda-math-functions-in-julia/62285/1 "2021-06-02T15:59:03Z")

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Hi,

I’m new to CUDA in Julia. Is there any way to use the CUDA math functions in Julia?

Frank

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<div class="post-metadata">

**Author:** ![maxfreu](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/maxfreu/32/17468_2.png) [@maxfreu](https://discourse.julialang.org/u/maxfreu)\
**Post date:** [June 4, 2021, 8:48am UTC](https://discourse.julialang.org/t/cuda-math-functions-in-julia/62285/2 "2021-06-04T08:48:16Z")

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I think all fundamental mathematical functions are readily defined for CuArrays (arrays stored on the GPU). The same should hold for their use in kernels you write. Just try it out and see 🙂

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<div class="post-metadata">

**Author:** ![fci](https://avatars.discourse-cdn.com/v4/letter/f/ce73a5/32.png) [@fci](https://discourse.julialang.org/u/fci)\
**Post date:** [June 4, 2021, 8:59am UTC](https://discourse.julialang.org/t/cuda-math-functions-in-julia/62285/3 "2021-06-04T08:59:34Z")

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Thanks! I do actually look for special functions like the Bessel function, which is defined in CUDA.math.

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<div class="post-metadata">

**Author:** ![maxfreu](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/maxfreu/32/17468_2.png) [@maxfreu](https://discourse.julialang.org/u/maxfreu)\
**Post date:** [June 4, 2021, 9:48am UTC](https://discourse.julialang.org/t/cuda-math-functions-in-julia/62285/4 "2021-06-04T09:48:19Z")

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There is a SpecialFunctions package which provides [bessel functions](https://specialfunctions.juliamath.org/stable/functions_list/#SpecialFunctions.besselj) etc. CUDA.jl overrides the definitions [here](https://github.com/JuliaGPU/CUDA.jl/blob/5d6127dbbef495c94d3dd8de98162188062e11b1/src/device/intrinsics/math.jl#L288-L306). So I think it should work by just importing SpecialFunctions and CUDA.

Ok, I’ve just tried

```julia
julia> using SpecialFunctions
julia> using CUDA
julia> besselj0.(cu(rand(Float32,3)))

```

and it failed with an InvalidIRError, unsupported call to literal pointer.

> **Stack trace**
>
> ```julia
> ERROR: InvalidIRError: compiling kernel broadcast_kernel(CUDA.CuKernelContext, CuDeviceVector{Float32, 1}, Base.Broadcast.Broadcasted{Nothing, Tuple{Base.OneTo{Int64}}, typeof(besselj0), Tuple{Base.Broadcast.Extruded{CuDeviceVector{Float32, 1}, Tuple{Bool}, Tuple{Int64}}}}, Int64) resulted in invalid LLVM IR
> Reason: unsupported call through a literal pointer (call to j0f)
> Stacktrace:
> [1] besselj0
> @ ~/.julia/packages/SpecialFunctions/L13jJ/src/bessel.jl:199
> [2] _broadcast_getindex_evalf
> @ broadcast.jl:648
> [3] _broadcast_getindex
> @ broadcast.jl:621
> [4] getindex
> @ broadcast.jl:575
> [5] broadcast_kernel
> @ ~/.julia/packages/GPUArrays/WV76E/src/host/broadcast.jl:62
> Stacktrace:
> [1] check_ir(job::GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget, CUDA.CUDACompilerParams, GPUCompiler.FunctionSpec{GPUArrays.var"#broadcast_kernel#12", Tuple{CUDA.CuKernelContext, CuDeviceVector{Float32, 1}, Base.Broadcast.Broadcasted{Nothing, Tuple{Base.OneTo{Int64}}, typeof(besselj0), Tuple{Base.Broadcast.Extruded{CuDeviceVector{Float32, 1}, Tuple{Bool}, Tuple{Int64}}}}, Int64}}}, args::LLVM.Module)
> @ GPUCompiler ~/.julia/packages/GPUCompiler/XwWPj/src/validation.jl:123
> [2] macro expansion
> @ ~/.julia/packages/GPUCompiler/XwWPj/src/driver.jl:288 [inlined]
> [3] macro expansion
> @ ~/.julia/packages/TimerOutputs/ZmKD7/src/TimerOutput.jl:206 [inlined]
> [4] macro expansion
> @ ~/.julia/packages/GPUCompiler/XwWPj/src/driver.jl:286 [inlined]
> [5] emit_asm(job::GPUCompiler.CompilerJob, ir::LLVM.Module, kernel::LLVM.Function; strip::Bool, validate::Bool, format::LLVM.API.LLVMCodeGenFileType)
> @ GPUCompiler ~/.julia/packages/GPUCompiler/XwWPj/src/utils.jl:62
> [6] cufunction_compile(job::GPUCompiler.CompilerJob)
> @ CUDA ~/.julia/packages/CUDA/M4jkK/src/compiler/execution.jl:306
> [7] check_cache
> @ ~/.julia/packages/GPUCompiler/XwWPj/src/cache.jl:44 [inlined]
> [8] cached_compilation
> @ ~/.julia/packages/GPUArrays/WV76E/src/host/broadcast.jl:60 [inlined]
> [9] cached_compilation(cache::Dict{UInt64, Any}, job::GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget, CUDA.CUDACompilerParams, GPUCompiler.FunctionSpec{GPUArrays.var"#broadcast_kernel#12", Tuple{CUDA.CuKernelContext, CuDeviceVector{Float32, 1}, Base.Broadcast.Broadcasted{Nothing, Tuple{Base.OneTo{Int64}}, typeof(besselj0), Tuple{Base.Broadcast.Extruded{CuDeviceVector{Float32, 1}, Tuple{Bool}, Tuple{Int64}}}}, Int64}}}, compiler::typeof(CUDA.cufunction_compile), linker::typeof(CUDA.cufunction_link))
> @ GPUCompiler ~/.julia/packages/GPUCompiler/XwWPj/src/cache.jl:0
> [10] cufunction(f::GPUArrays.var"#broadcast_kernel#12", tt::Type{Tuple{CUDA.CuKernelContext, CuDeviceVector{Float32, 1}, Base.Broadcast.Broadcasted{Nothing, Tuple{Base.OneTo{Int64}}, typeof(besselj0), Tuple{Base.Broadcast.Extruded{CuDeviceVector{Float32, 1}, Tuple{Bool}, Tuple{Int64}}}}, Int64}}; name::Nothing, kwargs::Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
> @ CUDA ~/.julia/packages/CUDA/M4jkK/src/compiler/execution.jl:294
> [11] cufunction
> @ ~/.julia/packages/CUDA/M4jkK/src/compiler/execution.jl:288 [inlined]
> [12] macro expansion
> @ ~/.julia/packages/CUDA/M4jkK/src/compiler/execution.jl:102 [inlined]
> [13] #launch_heuristic#280
> @ ~/.julia/packages/CUDA/M4jkK/src/gpuarrays.jl:17 [inlined]
> [14] launch_heuristic
> @ ~/.julia/packages/CUDA/M4jkK/src/gpuarrays.jl:17 [inlined]
> [15] copyto!
> @ ~/.julia/packages/GPUArrays/WV76E/src/host/broadcast.jl:66 [inlined]
> [16] copyto!(dest::CuArray{Float32, 1}, bc::Base.Broadcast.Broadcasted{CUDA.CuArrayStyle{1}, Tuple{Base.OneTo{Int64}}, typeof(besselj0), Tuple{CuArray{Float32, 1}}})
> @ Base.Broadcast ./broadcast.jl:935
> [17] copy(bc::Base.Broadcast.Broadcasted{CUDA.CuArrayStyle{1}, Tuple{Base.OneTo{Int64}}, typeof(besselj0), Tuple{CuArray{Float32, 1}}})
> @ Base.Broadcast ./broadcast.jl:907
> [18] materialize(bc::Base.Broadcast.Broadcasted{CUDA.CuArrayStyle{1}, Nothing, typeof(besselj0), Tuple{CuArray{Float32, 1}}})
> @ Base.Broadcast ./broadcast.jl:882
> [19] top-level scope
> @ REPL[4]:1
> 
> # on some nightly version
> Julia Version 1.7.0-DEV.252
> Commit 33237ef7ad (2021-01-11 09:49 UTC)
> Platform Info:
> OS: Linux (x86_64-pc-linux-gnu)
> CPU: Intel(R) Xeon(R) Gold 6136 CPU @ 3.00GHz
> WORD_SIZE: 64
> LIBM: libopenlibm
> LLVM: libLLVM-11.0.0 (ORCJIT, skylake-avx512)
> 
> ```

But maybe I’m doing sth wrong.

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**Author:** ![skleinbo](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/skleinbo/32/36080_2.png) [@skleinbo](https://discourse.julialang.org/u/skleinbo)\
**Post date:** [June 4, 2021, 11:32am UTC](https://discourse.julialang.org/t/cuda-math-functions-in-julia/62285/5 "2021-06-04T11:32:23Z")

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`CUDA.jl<3` calls them `j0` and so on, instead of `besselj0`.