# CUDA Error in constant CuArray with conditional usage
**URL:** https://discourse.julialang.org/t/cuda-error-in-constant-cuarray-with-conditional-usage/48581
**Category:** GPU
**Tags:** question, gpu, cuda, cuarrays
**Created:** [October 18, 2020, 12:21pm UTC](https://discourse.julialang.org/t/cuda-error-in-constant-cuarray-with-conditional-usage/48581 "2020-10-18T12:21:41Z")
**Posts on this page:** 4
**Page:** 1
### Author:  [@ozmaden](https://discourse.julialang.org/u/ozmaden)
#### Post date: [October 18, 2020, 12:21pm UTC](https://discourse.julialang.org/t/cuda-error-in-constant-cuarray-with-conditional-usage/48581/1 "2020-10-18T12:21:41Z")
Hi everyone, I am confronted with an error concerning a constant CuArray. Any help would be greatly appreciated.
**Quick context:**
I’m one of the developers of the [GNSSSignals](https://github.com/JuliaGNSS/GNSSSignals.jl) package, adapting it to do signal processing on the GPU. Receving GPS signals requires keeping a matrix of binary codes in the memory _at all times_. Currently:
- Function [`read_in_codes`](https://github.com/JuliaGNSS/GNSSSignals.jl/blob/269bcb5eb0df8efe6a060d80c08e6adf73f0fc54/src/gps_l1.jl#L1) reads the GPS codes from a binary file in the repo
- Function [`extend_front_and_back`](https://github.com/JuliaGNSS/GNSSSignals.jl/blob/269bcb5eb0df8efe6a060d80c08e6adf73f0fc54/src/GNSSSignals.jl#L59) stretches the code for early and late correlators
- [`const GPS_CA_CODES`](https://github.com/JuliaGNSS/GNSSSignals.jl/blob/269bcb5eb0df8efe6a060d80c08e6adf73f0fc54/src/GNSSSignals.jl#L52) calls these functions and stores codes in the CPU
**MWE:**
Trying to implement conditional CUDA usage according to the [documentation](https://juliagpu.gitlab.io/CUDA.jl/installation/conditional/#Scenario-2:-GPU-is-optional-1).
```julia
using CUDA, GNSSSignals
const use_gpu = CUDA.functional() # true if CUDA available
function read_in_codes(filename, num_prns, code_length)
# reading the int8 encoded binary codes under filename as
# code_length x num_prns matrix
code_int8 = open(filename) do file_stream
read!(file_stream, Array{Int8}(undef, code_length, num_prns)
end
# return either an array or a CuArray according to the use_gpu flag
use_gpu ? CuArray{ComplexF32}(code_int8) : Int16.(code_int8)
end
# change nothing
function extend_front_and_back(codes)
[codes[end, :]'; codes; codes[1,:]'; codes[2,:]']
end
# change nothing
const GPS_CA_CODES = extend_front_and_back(read_in_codes(
joinpath(dirname(pathof(GNSSSignals)), "..", "data", "codes_gps_l1.bin"),
37,
1023
))
```
Remark: I need the codes to be of `ComplexF32` type due to performance reasons in calling of CUBLAS functions in other packages.
**Error stacktrace:**
When compiling the dev package on this [branch](https://github.com/ozmaden/GNSSSignals.jl/tree/feature/gpu) with the above mentioned implementation:
```julia
julia> using GNSSSignals
[Info: Precompiling GNSSSignals [52c80523-2a4e-5c38-8979-05588f836870]
julia> GNSSSignals.GPS_CA_CODES
1026×37 CUDA.CuArray{Float32,2}:
Error showing value of type CUDA.CuArray{Float32,2}:
ERROR: CUDA error: invalid argument (code 1, ERROR_INVALID_VALUE)
Stacktrace:
[1] throw_api_error(::CUDA.cudaError_enum) at /home/coz/.julia/packages/CUDA/d6WNR/lib/cudadrv/error.jl:103
[2] macro expansion at /home/coz/.julia/packages/CUDA/d6WNR/lib/cudadrv/error.jl:110 [inlined]
[3] cuMemcpyDtoH_v2(::Ptr{Float32}, ::CUDA.CuPtr{Float32}, ::Int64) at /home/coz/.julia/packages/CUDA/d6WNR/lib/utils/call.jl:93
[4] #unsafe_copyto!#6 at /home/coz/.julia/packages/CUDA/d6WNR/lib/cudadrv/memory.jl:395 [inlined]
[5] unsafe_copyto! at /home/coz/.julia/packages/CUDA/d6WNR/lib/cudadrv/memory.jl:388 [inlined]
[6] unsafe_copyto! at /home/coz/.julia/packages/CUDA/d6WNR/src/array.jl:314 [inlined]
[7] copyto!(::Array{Float32,2}, ::Int64, ::CUDA.CuArray{Float32,2}, ::Int64, ::Int64) at /home/coz/.julia/packages/CUDA/d6WNR/src/array.jl:289
[8] copyto! at /home/coz/.julia/packages/GPUArrays/uaFZh/src/host/abstractarray.jl:104 [inlined]
[9] copyto_axcheck! at ./abstractarray.jl:946 [inlined]
[10] Array at ./array.jl:562 [inlined]
[11] Array at ./boot.jl:430 [inlined]
[12] convert at ./array.jl:554 [inlined]
[13] adapt_storage at /home/coz/.julia/packages/CUDA/d6WNR/src/array.jl:267 [inlined]
[14] adapt_structure at /home/coz/.julia/packages/Adapt/8kQMV/src/Adapt.jl:42 [inlined]
[15] adapt at /home/coz/.julia/packages/Adapt/8kQMV/src/Adapt.jl:40 [inlined]
[16] convert_to_cpu at /home/coz/.julia/packages/GPUArrays/uaFZh/src/host/abstractarray.jl:49 [inlined]
[17] print_array(::IOContext{REPL.Terminals.TTYTerminal}, ::CUDA.CuArray{Float32,2}) at /home/coz/.julia/packages/GPUArrays/uaFZh/src/host/abstractarray.jl:54
[18] show(::IOContext{REPL.Terminals.TTYTerminal}, ::MIME{Symbol("text/plain")}, ::CUDA.CuArray{Float32,2}) at ./arrayshow.jl:358
[19] display(::REPL.REPLDisplay, ::MIME{Symbol("text/plain")}, ::Any) at /buildworker/worker/package_linuxaarch64/build/usr/share/julia/stdlib/v1.5/REPL/src/REPL.jl:214
[20] display(::REPL.REPLDisplay, ::Any) at /buildworker/worker/package_linuxaarch64/build/usr/share/julia/stdlib/v1.5/REPL/src/REPL.jl:218
[21] display(::Any) at ./multimedia.jl:328
[22] print_response(::IO, ::Any, ::Bool, ::Bool, ::Any) at ./c.jl:0
[23] print_response(::REPL.AbstractREPL, ::Any, ::Bool, ::Bool) at /buildworker/worker/package_linuxaarch64/build/usr/share/julia/stdlib/v1.5/REPL/src/REPL.jl:223
[24] (::REPL.var"#do_respond#54"{Bool,Bool,VSCodeServer.var"#36#37"{REPL.LineEditREPL,REPL.LineEdit.Prompt},REPL.LineEditREPL,REPL.LineEdit.Prompt})(::Any, ::Any, ::Any) at /buildworker/worker/package_linuxaarch64/build/usr/share/julia/stdlib/v1.5/REPL/src/REPL.jl:822
[25] #invokelatest#1 at ./essentials.jl:710 [inlined]
[26] invokelatest at ./essentials.jl:709 [inlined]
[27] run_interface(::REPL.Terminals.TextTerminal, ::REPL.LineEdit.ModalInterface, ::REPL.LineEdit.MIState) at /buildworker/worker/package_linuxaarch64/build/usr/share/julia/stdlib/v1.5/REPL/src/LineEdit.jl:2355
[28] run_frontend(::REPL.LineEditREPL, ::REPL.REPLBackendRef) at /buildworker/worker/package_linuxaarch64/build/usr/share/julia/stdlib/v1.5/REPL/src/REPL.jl:1143
[29] (::REPL.var"#38#42"{REPL.LineEditREPL,REPL.REPLBackendRef})() at ./task.jl:356
```
No errors when running MWE in REPL :
```julia
...
julia> const GPS_CA_CODES = extend_front_and_back(read_in_codes(
joinpath(dirname(pathof(GNSSSignals)), "..", "data", "codes_gps_l1.bin"),
37,
1023
))
1026×37 CuArray{Complex{Float32},2}:
-1.0-0.0im -1.0-0.0im -1.0-0.0im -1.0-0.0im -1.0-0.0im 1.0-0.0im … 1.0-0.0im 1.0-0.0im -1.0-0.0im -1.0-0.0im 1.0-0.0im
...
julia> typeof(GPS_CA_CODES)
CuArray{Complex{Float32},2}
```
**System info:**
Julia v1.5,
CUDA.jl v1.3.0 with CUDA 10.0
Linux (aarch64) on Nvidia Jetson AGX Xavier
---
### Author:  [@ozmaden](https://discourse.julialang.org/u/ozmaden)
#### Post date: [October 18, 2020, 12:23pm UTC](https://discourse.julialang.org/t/cuda-error-in-constant-cuarray-with-conditional-usage/48581/2 "2020-10-18T12:23:39Z")
**Additional information:**
Tried without the conditional usage, error persists.
Tried to declare `GPS_CA_CODES` without `const` in front of it, error persists.
---
### Author:  [@maleadt](https://discourse.julialang.org/u/maleadt)
#### Post date: [October 19, 2020, 6:14am UTC](https://discourse.julialang.org/t/cuda-error-in-constant-cuarray-with-conditional-usage/48581/3 "2020-10-19T06:14:31Z")
You cannot precompile a CuArray, they are session dependent. Change it to a Ref or something and create it during ` __init__ ` (or at another time, but at run-time).
---
### Author:  [@ozmaden](https://discourse.julialang.org/u/ozmaden)
#### Post date: [October 19, 2020, 10:37pm UTC](https://discourse.julialang.org/t/cuda-error-in-constant-cuarray-with-conditional-usage/48581/4 "2020-10-19T22:37:12Z")
Thanks for the answer