You can leverage the most advanced GPU on IBM Power machine. The Power System S822LC for High-Performance Computing (code-named “Minsky”) contains two Power8 CPUs and two Nvidia Tesla P100 GPUs. It is available at Minicloud 🙂
This post is an updated version of the steps described here.
Verify that the GPU cards are detected
sudo lspci | grep -i nvidia
Install basic development packages (CUDA toolkit dependencies)
$ sudo apt-get install build-essential
Download the DEB package of the CUDA repository for Ubuntu 16.04 on POWER8
wget https://developer.nvidia.com/compute/cuda/8.0/prod/local_installers/cuda-repo-ubuntu1604-8-0-local-ga2_8.0.54-1_ppc64el-deb
Install the DEB package
sudo dpkg -i ./cuda-repo-ubuntu1604-8-0-local-ga2_8.0.54-1_ppc64el-deb sudo dpkg -f install
Update the package manager’s definitions
sudo apt-get update
Install the CUDA toolkit
sudo apt-get install cuda
Configure the search paths for the CUDA commands and libraries, making the setting available to all users and persistent across reboots
echo 'export PATH=$PATH:/usr/local/cuda-8.0/bin' | sudo tee echo /usr/local/cuda-8.0/lib64 | sudo tee /etc/ld.so.conf.d/cuda-8-0.conf sudo ldconfig
Reboot the system
sudo reboot
After rebooting, verify that the nvidia module is loaded and the GPU devices are available
sudo dmesg | grep -i nvidia sudo lsmod | grep nvidia nvidia-smi --list-gpus
Perform a simple test with the CUDA sample
mkdir ~/samples cp -r /usr/local/cuda-8.0/samples/ ~/samples/ cd ~/samples/7_CUDALibraries/simpleCUFFT make ./simpleCUFFT
References
http://www.redbooks.ibm.com/redpapers/pdfs/redp5169.pdf
https://developer.nvidia.com/cuda-downloads-power8
http://www.tomshardware.com/news/ibm-power8-nvidia-tesla-p100-minsky,32661.html
https://www.ibm.com/ms-en/marketplace/high-performance-computing