How do I select which GPU to run a job on?

Question

In a multi-GPU computer, how do I designate which GPU a CUDA job should run on?

As an example, when installing CUDA, I opted to install the NVIDIA_CUDA-<#.#>_Samples then ran several instances of the nbody simulation, but they all ran on one GPU 0; GPU 1 was completely idle (monitored using watch -n 1 nvidia-dmi). Checking CUDA_VISIBLE_DEVICES using

echo $CUDA_VISIBLE_DEVICES 

I found this was not set. I tried setting it using

CUDA_VISIBLE_DEVICES=1 

then running nbody again but it also went to GPU 0.

I looked at the related question, how to choose designated GPU to run CUDA program?, but deviceQuery command is not in the CUDA 8.0 bin directory. In addition to $CUDA_VISIBLE_DEVICES$, I saw other posts refer to the environment variable $CUDA_DEVICES but these were not set and I did not find information on how to use it.

While not directly related to my question, using nbody -device=1 I was able to get the application to run on GPU 1 but using nbody -numdevices=2 did not run on both GPU 0 and 1.

I am testing this on a system running using the bash shell, on CentOS 6.8, with CUDA 8.0, 2 GTX 1080 GPUs, and NVIDIA driver 367.44.

I know when writing using CUDA you can manage and control which CUDA resources to use but how would I manage this from the command line when running a compiled CUDA executable?

Answer 1

The problem was caused by not setting the CUDA_VISIBLE_DEVICES variable within the shell correctly.

To specify CUDA device 1 for example, you would set the CUDA_VISIBLE_DEVICES using

export CUDA_VISIBLE_DEVICES=1 

or

CUDA_VISIBLE_DEVICES=1 ./cuda_executable 

The former sets the variable for the life of the current shell, the latter only for the lifespan of that particular executable invocation.

If you want to specify more than one device, use

export CUDA_VISIBLE_DEVICES=0,1 

or

CUDA_VISIBLE_DEVICES=0,1 ./cuda_executable 

Answer 2

In case of someone else is doing it in Python and it is not working, try to set it before do the imports of pycuda and tensorflow.

I.e.:

import os os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" os.environ["CUDA_VISIBLE_DEVICES"] = "0" ... import pycuda.autoinit import tensorflow as tf ... 

As saw here.