# SpecialFUnctions and CUDA

**URL:** <https://discourse.julialang.org/t/specialfunctions-and-cuda/116569>\
**Category:** General Usage\
**Created:** [July 3, 2024, 8:06am UTC](https://discourse.julialang.org/t/specialfunctions-and-cuda/116569 "2024-07-03T08:06:36Z")\
**Posts on this page:** 4\
**Page:** 1

<div class="post-metadata">

**Author:** ![Ferran\_Mazzanti](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/ferran_mazzanti/32/7458_2.png) [@Ferran\_Mazzanti](https://discourse.julialang.org/u/Ferran_Mazzanti)\
**Post date:** [July 3, 2024, 8:06am UTC](https://discourse.julialang.org/t/specialfunctions-and-cuda/116569/1 "2024-07-03T08:06:36Z")

</div>

Hi,  
I thought SpecialFunctions.jl worked flowlesly with CUDA, but I get this

```julia
using CUDA

N = 7000
M = CUDA.rand(N,N)
besselh.(0,M)

ERROR: InvalidIRError: compiling MethodInstance for (::GPUArrays.var"#35#37")(::CUDA.CuKernelContext, ::CuDeviceMatrix{ComplexF64, 1}, ::Base.Broadcast.Broadcasted{CUDA.CuArrayStyle{…}, Tuple{…}, typeof(besselh), Tuple{…}}, ::Int64) resulted in invalid LLVM IR
Reason: unsupported call to an unknown function (call to ijl_lazy_load_and_lookup)
Stacktrace:
 [1] _besselh
   @ ~/.julia/packages/SpecialFunctions/npKKV/src/bessel.jl:214
 [2] multiple call sites
   @ unknown:0
Hint: catch this exception as `err` and call `code_typed(err; interactive = true)` to introspect the erronous code with Cthulhu.jl
Stacktrace:
  [1] check_ir(job::GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget, CUDA.CUDACompilerParams}, args::LLVM.Module)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/nWT2N/src/validation.jl:147
  [2] macro expansion
    @ ~/.julia/packages/GPUCompiler/nWT2N/src/driver.jl:460 [inlined]
  [3] macro expansion
    @ ~/.julia/packages/TimerOutputs/Lw5SP/src/TimerOutput.jl:253 [inlined]
  [4] macro expansion
    @ ~/.julia/packages/GPUCompiler/nWT2N/src/driver.jl:459 [inlined]
  [5] emit_llvm(job::GPUCompiler.CompilerJob; libraries::Bool, toplevel::Bool, optimize::Bool, cleanup::Bool, only_entry::Bool, validate::Bool)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/nWT2N/src/utils.jl:103
  [6] emit_llvm
    @ ~/.julia/packages/GPUCompiler/nWT2N/src/utils.jl:97 [inlined]
  [7] codegen(output::Symbol, job::GPUCompiler.CompilerJob; libraries::Bool, toplevel::Bool, optimize::Bool, cleanup::Bool, strip::Bool, validate::Bool, only_entry::Bool, parent_job::Nothing)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/nWT2N/src/driver.jl:136
  [8] codegen
    @ ~/.julia/packages/GPUCompiler/nWT2N/src/driver.jl:115 [inlined]
  [9] compile(target::Symbol, job::GPUCompiler.CompilerJob; libraries::Bool, toplevel::Bool, optimize::Bool, cleanup::Bool, strip::Bool, validate::Bool, only_entry::Bool)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/nWT2N/src/driver.jl:111
 [10] compile
    @ ~/.julia/packages/GPUCompiler/nWT2N/src/driver.jl:103 [inlined]
 [11] #1145
    @ ~/.julia/packages/CUDA/75aiI/src/compiler/compilation.jl:254 [inlined]
 [12] JuliaContext(f::CUDA.var"#1145#1148"{GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget, CUDA.CUDACompilerParams}}; kwargs::@Kwargs{})
    @ GPUCompiler ~/.julia/packages/GPUCompiler/nWT2N/src/driver.jl:52
 [13] JuliaContext(f::Function)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/nWT2N/src/driver.jl:42
 [14] compile(job::GPUCompiler.CompilerJob)
    @ CUDA ~/.julia/packages/CUDA/75aiI/src/compiler/compilation.jl:253
 [15] actual_compilation(cache::Dict{…}, src::Core.MethodInstance, world::UInt64, cfg::GPUCompiler.CompilerConfig{…}, compiler::typeof(CUDA.compile), linker::typeof(CUDA.link))
    @ GPUCompiler ~/.julia/packages/GPUCompiler/nWT2N/src/execution.jl:128
 [16] cached_compilation(cache::Dict{…}, src::Core.MethodInstance, cfg::GPUCompiler.CompilerConfig{…}, compiler::Function, linker::Function)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/nWT2N/src/execution.jl:103
 [17] macro expansion
    @ ~/.julia/packages/CUDA/75aiI/src/compiler/execution.jl:369 [inlined]
 [18] macro expansion
    @ ./lock.jl:267 [inlined]
 [19] cufunction(f::GPUArrays.var"#35#37", tt::Type{Tuple{CUDA.CuKernelContext, CuDeviceMatrix{…}, Base.Broadcast.Broadcasted{…}, Int64}}; kwargs::@Kwargs{})
    @ CUDA ~/.julia/packages/CUDA/75aiI/src/compiler/execution.jl:364
 [20] cufunction
    @ ~/.julia/packages/CUDA/75aiI/src/compiler/execution.jl:361 [inlined]
 [21] macro expansion
    @ ~/.julia/packages/CUDA/75aiI/src/compiler/execution.jl:112 [inlined]
 [22] #launch_heuristic#1204
    @ ~/.julia/packages/CUDA/75aiI/src/gpuarrays.jl:17 [inlined]
 [23] launch_heuristic
    @ ~/.julia/packages/CUDA/75aiI/src/gpuarrays.jl:15 [inlined]
 [24] _copyto!
    @ ~/.julia/packages/GPUArrays/WuHrl/src/host/broadcast.jl:78 [inlined]
 [25] copyto!
    @ ~/.julia/packages/GPUArrays/WuHrl/src/host/broadcast.jl:44 [inlined]
 [26] copy
    @ ~/.julia/packages/GPUArrays/WuHrl/src/host/broadcast.jl:29 [inlined]
 [27] materialize(bc::Base.Broadcast.Broadcasted{CUDA.CuArrayStyle{2, CUDA.DeviceMemory}, Nothing, typeof(besselh), Tuple{Int64, CuArray{Float32, 2, CUDA.DeviceMemory}}})
    @ Base.Broadcast ./broadcast.jl:903
 [28] top-level scope
    @ REPL[40]:1
Some type information was truncated. Use `show(err)` to see complete types.

This is ubuntu 20.04 on an Intel I9 and a nVidia 3060 (12Gb RAM) working well with other CUDA things (including programming in julia). I run Julia 1.10.3

Best,

Ferran.
```

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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:** [July 3, 2024, 10:11am UTC](https://discourse.julialang.org/t/specialfunctions-and-cuda/116569/2 "2024-07-03T10:11:02Z")

</div>

SpecialFunctions.jl uses a library called openspecfun.

Nvidia does seem to provide at least some of them [CUDA Math API Reference Manual](https://docs.nvidia.com/cuda/cuda-math-api/index.html)

and CUDA.jl provides device replacements for SpecialFunctions.jl

But `besselh` is not one of them, I think `besslh` can be written in terms of `besselj` and `bessely` so you could open an issue or PR to CUDA.jl

---

<div class="post-metadata">

**Author:** ![Oscar\_Smith](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/oscar_smith/32/25343_2.png) [@Oscar\_Smith](https://discourse.julialang.org/u/Oscar_Smith)\
**Post date:** [July 3, 2024, 2:40pm UTC](https://discourse.julialang.org/t/specialfunctions-and-cuda/116569/3 "2024-07-03T14:40:42Z")

</div>

that said, for Bessel functions, you likely want to use Bessels.jl which in the CPU at least is often ~10 faster. not sure if the code will be GPU compatible by default.

---

<div class="post-metadata">

**Author:** ![Oscar\_Smith](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/oscar_smith/32/25343_2.png) [@Oscar\_Smith](https://discourse.julialang.org/u/Oscar_Smith)\
**Post date:** [April 24, 2025, 3:14am UTC](https://discourse.julialang.org/t/specialfunctions-and-cuda/116569/4 "2025-04-24T03:14:39Z")

</div>

~9 months later, I’ve actually tested this, and Bessels.jl is theoretically CUDA compatible since it’s just Julia code (although until [make float32 bessels type stable by oscardssmith · Pull Request #120 · JuliaMath/Bessels.jl · GitHub](https://github.com/JuliaMath/Bessels.jl/pull/120) is merged, a number of the functions are type unstable for Float32 inputs)