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Robadob avatar Robadob commented on August 22, 2024

I think it's supposed to be --std=c++14

(Or atleast, try the long form, iirc we may have had an issue with the short form of some options).

That's how we use it here, in a cross platform CMake project that makes use of Jitify.

Refer to these docs, RTC compiler flags are a subset of those for standard nvcc.
https://docs.nvidia.com/cuda/nvrtc/index.html#group__options

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mattpaletta avatar mattpaletta commented on August 22, 2024

Thanks for the response, I tried it with --std=c++14 and without that parameter, both result in the same error. I will have a look at those docs.

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Robadob avatar Robadob commented on August 22, 2024

I've just tested this small Jitify test rig I had kicking around, by only passing the options -std=c++14 and -arch=compute_30. It works under both Visual Studio 2015 and 2019, with CUDA 10.1 and the latest jitify header from master. I'm unable to reproduce your problem, it runs for me and gives the expected output (1080GTX if that matters).

Looking at the code you've posted, you use a slightly different function (JitCache::program()), but checking it's implementation it calls the same code.

So it's possibly worth looking elsewhere at the blame, I've not seen this particular error before and most results on google suggest particular device/driver/nvrtc versions being to blame. Hopefully a jitify dev knows more.

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benbarsdell avatar benbarsdell commented on August 22, 2024

The issue is that compute_30 is no longer supported as of CUDA 11.0 [1]. Unfortunately the NVRTC docs have not yet been updated to reflect this (this will be fixed in the next release).

The -arch flag is added automatically by Jitify based on the architecture of the GPU you are using. You could try adding -arch=compute_35, but it likely still won't run.

[1] https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html#deprecated-features

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mattpaletta avatar mattpaletta commented on August 22, 2024

@Robadob Thanks so much for investigating and sending over the known code sample.
@benbarsdell Thanks, I reverted to CUDA 10.2 and that resolved my issue.

Both of your input much appreciated I will close this now.

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