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Attaching a GPU to a VM

Warning

The HPC Cloud service stopped offering / supporting Marketplace images with GPU drivers pre-installed, see our changelog. Contact the Service Helpdesk helpdesk@surfsara.nl for options.

Using GPU devices to do (part of) your computation on, enables a form of parallelization that could be much faster than multiple CPU core usage. However, the software you are using must be enabled to use GPU's and programming for GPU's is difficult and often not ideal for novice programmers. If you are not sure whether or not you could profit from using GPU's, please feel free to contact us at helpdesk@surfsara.nl.

This page teaches how to attach a GPU device to a VM. It does not show you how to get or write software that uses GPU's.

For more information on our hardware, see our resource overview.

Getting access

By default, you will not have access to the GPU nodes of the HPC Cloud. In order to get this, please send an e-mail to helpdesk@surfsara.nl.

If GPU access was enabled for your account, you will have to use a datastore that is enabled on the GPU nodes. This datastore is called images_ssd_gpu and behaves the same way as the local_images_ssd datastore, except that it is accessible on the GPU nodes only. In other words, using this datastore, makes sure that your VM is run on the nodes which have GPU's installed on them.

Note that the ceph datastore is also enabled on the GPU nodes. However, as described here, it is best to put your OS image on either the local_images_ssd or the images_ssd_gpu datastore. ceph can still be used for larger, data images and for persistent data.

Import appliances from the Apps option

If you want to import an appliance from the Apps option of the Storage section for use on the GPU nodes, you can follow the normal instructions as described on this page.

The only exception is that you have to select a different datastore. In the screen shown below, make sure you choose the 'images_ssd_gpu' datastore.

Using an existing image

When you already have an image that you want to use on the GPU nodes, you will have to create a clone in the correct datastore:

Go to 'Storage' > 'Images' for an overview of your images
Select the image you want to copy
Click on 'Clone' at the top of the table with images.
Click on the 'Advanced options' button. This will show you a screen like this:
Select the correct datastore 'images_ssd_gpu'
Optionally, change the name of the image
Click the 'Clone' button
Optionally, you can now delete the old image. This is not necessary, but note that changes in one image will not affect the other!

Adding a GPU device to your VM

GPU devices are attached to VM using 'pci passthrough'. This means that your VM will have direct access to the hardware, instead of through a virtualisation layer. This should give an optimal performance. You have the option of attaching a Nvidia GRID K2 (1536 CUDA cores, 4GB) or Tesla P100 GPU (3584 CUDA cores, 12GB). Please be aware that the Nvidia GRID K2 GPUs are older and the existing drivers might not support your application.

NOTE:

Before proceeding, make sure that your OS image is on the images_ssd_gpu datastore and possible extra images are on the ceph datastore.

To attach a GPU device to your VM, either create a new template or edit an existing template as described on this page. Then:

Go to the 'Other' tab while editing the template. You should see a screen like below:
Click on the '+ Add PCI device' button. This adds a line to the table above it (already visible in the screenshot above).
In the new line, under 'Device name', choose the GPU in the dropdown list. The 'Vendor', 'Device' and 'Class' will automatically be set. Please note the chosen GPU type as the driver installation instructions differ for both types.
If no other changes to the template are needed, click on the green 'Create' or 'Update button (depending on whether you are creating a new template or editing an existing one) will save the template.

Inside your VM

To make full use of the GPU capabilities please install the corresponding drivers and toolkit for your distribution from the official Nvidia repositories which can be found here. From there please follow the post installation instructions

Example for installing NVIDIA drivers on Ubuntu 18.04

Launch your VM and force an installation of any security updates, reboot the VM afterwards:

sudo systemctl start apt-daily.service && sleep 60 && sudo reboot

Now follow the instructions for the type of GPU you attached to your VM.

GPU Specific

Grid K2

Remove any old drivers, install prerequisites, then download and install the drivers:

sudo apt-get purge nvidia-* libcuda-*
sudo apt-get update && sudo apt-get install -y gcc make g++ libglu1-mesa libxi-dev libxmu-dev libglu1-mesa-dev software-properties-common gpg-agent gnupg2 unzip
wget http://us.download.nvidia.com/XFree86/Linux-x86_64/367.130/NVIDIA-Linux-x86_64-367.130.run
sudo sh NVIDIA-Linux-x86_64-367.130.run -s

Tesla P100

Remove any old drivers, add the Nvidia repository and install the drivers:

sudo apt purge nvidia-* libcuda1-*
wget http://us.download.nvidia.com/tesla/375.66/nvidia-diag-driver-local-repo-ubuntu1604_375.66-1_amd64.deb
sudo dpkg -i nvidia-diag-driver-local-repo-ubuntu1604_375.66-1_amd64.deb
sudo apt-get update && sudo apt-get install -y cuda-drivers

All GPU types

Check with nvidia-smi that the card is detected. It should show something like this:

$ nvidia-smi
Fri Nov  8 16:32:22 2019
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 367.130                Driver Version: 367.130                   |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GRID K2             Off  | 0000:01:01.0     Off |                  Off |
| N/A   42C    P0     1W / 117W |      0MiB /  4036MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID  Type  Process name                               Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

Now, reboot the OS and run nvidia-smi again and check for a similar output:

sudo reboot
nvidia-smi

Example for installing NVIDIA drivers on Ubuntu 16.04

Note:

We now recommend that you use Ubuntu 18.04

Launch your VM and force an installation of any security updates, reboot the VM afterwards:

sudo systemctl start apt-daily.service && sleep 60 && sudo reboot

NOTE: When installing the drivers they are compiled for the current kernel version. You will need to reinstall them if/when you update the kernel.

Now follow the instructions for the type of GPU you attached to your VM.

GPU Specific

Warning

Linux kernel 4.4.0-143 seems to be backward-incompatible for compiling the NVidia drivers! Therefore, please do the following to stick to 4.4.0-142 (note that the operating system will reboot):

wget https://raw.githubusercontent.com/sara-nl/clouddocs/gh-pages/assets/ubuntu16gpukernelpin.sh
chmod +x ubuntu16gpukernelpin.sh
sudo ./ubuntu16gpukernelpin.sh

The previous lines make sure you have the proper kernel version installed. The following lines make sure your configuration stays as it should:

sudo apt-mark hold linux-image-4.4.0-142-generic linux-headers-4.4.0-142 linux-image-extra-4.4.0-142-generic

Grid K2

Remove any old drivers, install prerequisites, then download and install the drivers:

sudo apt-get purge nvidia-* libcuda-* libcuda1-*
sudo apt-get update && sudo apt-get install -y gcc make g++ libglu1-mesa libxi-dev libxmu-dev libglu1-mesa-dev
wget http://us.download.nvidia.com/XFree86/Linux-x86_64/367.130/NVIDIA-Linux-x86_64-367.130.run
sudo sh NVIDIA-Linux-x86_64-367.130.run -s

Tesla P100

Remove any old drivers, add the Nvidia repository and install the drivers:

sudo apt purge nvidia-* libcuda1-*
wget http://us.download.nvidia.com/tesla/375.66/nvidia-diag-driver-local-repo-ubuntu1604_375.66-1_amd64.deb
sudo dpkg -i nvidia-diag-driver-local-repo-ubuntu1604_375.66-1_amd64.deb
sudo apt-get update && sudo apt-get install -y cuda-drivers

All GPU types

Check with nvidia-smi that the card is detected. It should show something like this:

Wed Jul 12 16:36:24 2017
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 367.130                Driver Version: 367.130                   |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GRID K2             Off  | 0000:01:01.0      On |                  Off |
| N/A   29C    P8    18W / 117W |     17MiB /  4036MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID  Type  Process name                               Usage      |
|=============================================================================|
|    0       802    G   /usr/lib/xorg/Xorg                              16MiB |
+-----------------------------------------------------------------------------+

Now, reboot the OS and run nvidia-smi again and check for a similar output:

sudo reboot
nvidia-smi

Install CUDA 8 on Ubuntu

Install CUDA prerequisites, download and install CUDA 8:

sudo apt-get install -y gcc make g++ build-essential dkms linux-headers-$(uname -r)
wget https://developer.nvidia.com/compute/cuda/8.0/Prod2/local_installers/cuda_8.0.61_375.26_linux-run
sudo service lightdm stop
sudo sh cuda_8.0.61_375.26_linux-run --silent --samples --toolkit --override

Reboot your VM once more: sudo reboot

NOTE:

Make sure the libraries and binaries paths are part of your environment (these should be added as part of the installation), if they are not they can be added with:

export PATH=$PATH:/usr/local/cuda-8.0/bin

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-8.0/lib64

CUDA should be ready to use, as a test we will compile and run one of the sample utilities:

cd NVIDIA_CUDA-8.0_Samples/1_Utilities/deviceQuery
make
./deviceQuery

If everything is running as intended the result will look similar to this:

./deviceQuery Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "Tesla P100-PCIE-12GB"
  CUDA Driver Version / Runtime Version          8.0 / 8.0
  CUDA Capability Major/Minor version number:    6.0
  Total amount of global memory:                 12194 MBytes (12786073600 bytes)
  (56) Multiprocessors, ( 64) CUDA Cores/MP:     3584 CUDA Cores
  GPU Max Clock rate:                            1329 MHz (1.33 GHz)
  Memory Clock rate:                             715 Mhz
  Memory Bus Width:                              3072-bit
  L2 Cache Size:                                 3145728 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
  Maximum Layered 1D Texture Size, (num) layers  1D=(32768), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(32768, 32768), 2048 layers
  Total amount of constant memory:               65536 bytes
  Total amount of shared memory per block:       49152 bytes
  Total number of registers available per block: 65536
  Warp size:                                     32
  Maximum number of threads per multiprocessor:  2048
  Maximum number of threads per block:           1024
  Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
  Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
  Maximum memory pitch:                          2147483647 bytes
  Texture alignment:                             512 bytes
  Concurrent copy and kernel execution:          Yes with 2 copy engine(s)
  Run time limit on kernels:                     Yes
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Enabled
  Device supports Unified Addressing (UVA):      Yes
  Device PCI Domain ID / Bus ID / location ID:   0 / 1 / 1
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 8.0, CUDA Runtime Version = 8.0, NumDevs = 1, Device0 = Tesla P100-PCIE-12GB
Result = PASS

NOTE:

During the installation the Nvidia drivers have been compiled for the current kernel version. The apt-daily service may upgrade your kernel if it's marked as a security update requiring you to reinstall the driver after a reboot. To prevent any (automatic) kernel upgrades, run sudo apt-mark hold linux-image-generic linux-headers-generic.

Running a Hello World

Note: For Ubuntu 18.04 you must install an earlier version of g and g++, so:

sudo apt install gcc-5 g++-5
sudo ln -s /usr/bin/gcc-5 /usr/local/cuda/bin/gcc
sudo ln -s /usr/bin/g++-5 /usr/local/cuda/bin/g++

Download, uncompress and compile Nvidia's Hello World example:

wget http://developer.download.nvidia.com/compute/developertrainingmaterials/samples/cuda_c/HelloWorld.zip
unzip HelloWorld.zip
nvcc hello.cu

Run the compiled program, which if the compilation succeeded will be called a.out:

./a.out

That yields the output:

$ ./a.out
H
E
L
L
O

W
O
R
L
D
!

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gpu-attach.markdown

gpu-attach.markdown

Attaching a GPU to a VM

Getting access

Import appliances from the Apps option

Using an existing image

Adding a GPU device to your VM

Inside your VM

Example for installing NVIDIA drivers on Ubuntu 18.04

GPU Specific

Grid K2

Tesla P100

All GPU types

Example for installing NVIDIA drivers on Ubuntu 16.04

GPU Specific

Grid K2

Tesla P100

All GPU types

Install CUDA 8 on Ubuntu

Running a Hello World

Files

gpu-attach.markdown

Latest commit

History

gpu-attach.markdown

File metadata and controls

Attaching a GPU to a VM

Getting access

Import appliances from the Apps option

Using an existing image

Adding a GPU device to your VM

Inside your VM

Example for installing NVIDIA drivers on Ubuntu 18.04

GPU Specific

Grid K2

Tesla P100

All GPU types

Example for installing NVIDIA drivers on Ubuntu 16.04

GPU Specific

Grid K2

Tesla P100

All GPU types

Install CUDA 8 on Ubuntu

Running a Hello World