Large amount of error output can lead to crashes

[yuvalwas]

Describe the bug

I'm not sure whether this is helpful, but the error message explicitly asked to be submitted, so here it is. I produced it by mistake.

To reproduce

a = CUDA.rand(1000,1000)
idx = CUDA.rand(Bool, 1, 1000)
a[:, idx]

This throws an extremely long message that took quite some time to finish, VSCode could not fit it all, so this does not include the beginning of the message. Each of the below lines was repeated many times.

ERROR: Out-of-bounds array access.
ERROR: a exception was thrown during kernel execution. 
       Run Julia on debug level 2 for device stack traces.
ERROR: Out-of-bounds array access.
ERROR: a exception was thrown during kernel execution. 
       Run Julia on debug level 2 for device stack traces.
ERROR: OER-of-bounds array access.
ERROR: OERRof-bounds array access.
ERROR: OERROR: ounds array access.
ERROR: OERROR: a nds array access.
ERROR: OERROR: a 1330795077s array access.

And finally

Please submit a bug report with steps to reproduce this fault, and any error messages that follow (in their entirety). Thanks.
Exception: EXCEPTION_ACCESS_VIOLATION at 0x7ffabf86b3e0 -- cuProfilerStop at C:\WINDOWS\system32\DriverStore\FileRepository\nv_dispi.inf_amd64_d3828c822366e497\nvcuda64.dll (unknown line)
in expression starting at none:0
Allocations: 114002842 (Pool: 113903511; Big: 99331); GC: 122

Manifest.toml

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[[deps.p7zip_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
version = "17.4.0+0"

Expected behavior

Same as if you drop CUDA in the above code.

Version info

Details on Julia:

Julia Version 1.9.2
Commit e4ee485e90 (2023-07-05 09:39 UTC)
Platform Info:
  OS: Windows (x86_64-w64-mingw32)
  CPU: 16 × 12th Gen Intel(R) Core(TM) i5-12600KF
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-14.0.6 (ORCJIT, alderlake)
  Threads: 1 on 16 virtual cores

Details on CUDA:

CUDA runtime 12.1, artifact installation
CUDA driver 12.2
NVIDIA driver 537.13.0

CUDA libraries:
- CUBLAS: 12.1.3
- CURAND: 10.3.2
- CUFFT: 11.0.2
- CUSOLVER: 11.4.5
- CUSPARSE: 12.1.0
- CUPTI: 18.0.0
- NVML: 12.0.0+537.13

Julia packages:
- CUDA: 4.4.1
- CUDA_Driver_jll: 0.5.0+1
- CUDA_Runtime_jll: 0.6.0+0

Toolchain:
- Julia: 1.9.2
- LLVM: 14.0.6
- PTX ISA support: 3.2, 4.0, 4.1, 4.2, 4.3, 5.0, 6.0, 6.1, 6.3, 6.4, 6.5, 7.0, 7.1, 7.2, 7.3, 7.4, 7.5
- Device capability support: sm_37, sm_50, sm_52, sm_53, sm_60, sm_61, sm_62, sm_70, sm_72, sm_75, sm_80, sm_86

1 device:
  0: NVIDIA GeForce RTX 3060 (sm_86, 11.384 GiB / 12.000 GiB available)

[maleadt]

This throws an extremely long message that took quite some time to finish

That's ultimately due to the CUDA driver not handling the amount of output we throw at it. It shouldn't crash, but we probably also shouldn't be emitting that much output in the first place.

Maybe we could have a global atomic flag that keeps track of how many exceptions have been reported, so that the signal_exception method can bail out.

[Jay-sanjay]

Hi sir, I would be happy to address this issue , According to me the bug could be fixed by following these things for the below provide method.
--> adding a global flag variable
--> increment flag with the number of exceptions received
--> if it crosses a certain thresold value , we exist the method

https://github.com/JuliaGPU/CUDA.jl/blob/master/src/device/runtime.jl#L29-L48

if you agree I can start a PR on it.

[maleadt]

Yes, that may work. Also, no need to ask for permission to work on issues!

[maleadt]

https://developer.nvidia.com/blog/asynchronous-error-reporting-when-printf-just-wont-do/ may contain some useful information for this