CODEWITHSUNDEEP
cudainstallationdevelopment-environmentgpgpu
einpoklum
einpoklum

Reputation: 131646

Installing CUDA as a non-root user with no GPU

I have a desktop without a GPU, on which I would like to develop code; and a machine on some cluster which has a GPU, and CUDA installed, but where I really can't "touch" anything and on which I won't run an IDE etc. I don't have root on any of the machines, woe is me.

So, essentially, I want to be able to compile and build my CUDA code on my own GPU-less desktop machine, then just copy it and test it on the other machine.

Can this be done despite the two hindering factors: I seem to recall the CUDA installer requiring the presence of a GPU; playing with the kernel; and doing other root-y stuff.

Notes:

Upvotes: 2

Views: 3956

Answers (1)

Robert Crovella
Robert Crovella

Reputation: 151944

  1. Assuming you want to develop codes that use the CUDA runtime API, you can install the cuda toolkit on a system that does not have a GPU. Using the runfile installer method, simply answer no when prompted to install the driver.

  2. If you want to compile codes (successfully) that use the CUDA driver API, that process will require a libcuda.so on your machine. This file is installed by the driver installer. There are various methods to "force" the driver installer to run on a machine without a GPU. You can get started by extracting the driver runfile installer (or downloading it separately) and passing the --help command line switch to the installer to learn about some of the options.

  3. These methods will not allow you to run those codes on a machine with no GPU of course. Furthermore, the process of moving a compiled binary from one machine to another, and expecting it to run correctly, is troublesome in my opinion. Therefore my suggestion would be to re-compile the code on a target machine. Otherwise getting a compiled binary to run from one machine to the next is a question that is not unique to CUDA, and is outside the scope of my answer.

  4. If you have no intention of running the codes on the non-GPU machine, and are willing to recompile on the target machine, then you can probably develop driver API codes even without libcuda.so (or there is a libcuda.so stub that you could try linking against just for compilation-test purposes, which is installed by the CUDA installer, if you search for it: /usr/local/cuda/lib64/stubs). If you don't link your driver API code against -lcuda, then you'll get a link error of course, but it should not matter, given the previously stated caveats.

  5. Fedora 22 is not officially supported by CUDA 7.5 or prior. YMMV.

  6. If you don't run the driver installer, you don't need to be a root user for any of this. Of course the install locations you pass to the installer must be those that your user privilege allows access to.

Upvotes: 3

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