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This page is used to track the GPGPU support related activities.
For user information check Federated Cloud GPGPU.
Providers willing to expose GPGPU resources check the documentation below.


To provide support for accelerated computing in EGI-Engage federated cloud.


Viet Tran (IISAS) viet.tran _at_

Jan Astalos (IISAS)

Miroslav Dobrucky (IISAS)

Current status

Status of OpenNebula site

IISAS-GPUCloud site with GPGPU has been established and integrated into EGI federated cloud

HW configuration:

6 computing nodes IBM dx360 M4 server with two NVIDIA Tesla K20 accelerators.
Ubuntu 14.04.2 LTS with KVM/QEMU, PCI passthrough virtualization of GPU cards.

SW configuration:

Base OS: Ubuntu 14.04.2 LTS
Hypervisor: KVM
Middleware: Openstack Liberty
GPU-enable flavors: gpu1cpu6 (1GPU + 6 CPU cores), gpu2cpu12 (2GPU +12 CPU cores)

EGI federated cloud configuration:

Openstack endpoint:
OCCI endpoint:
Supported VOs:, ops, dteam, moldyngrid,,,

Applications being tested/running on IISAS-GPUCloud


For information and support, please contact us via cloud-admin _at_

How to use GPGPU on IISAS-GPUCloud

For EGI users:

Join EGI federated cloud

Install your rOCCI client if you don't have it already (in Linux: just single command "curl -L | sudo /bin/bash -" )

Get VOMS proxy certificate from or any supported VO with -rfc (on rOCCI client: "voms-proxy-init --voms -rfc")
Choose a suitable flavor with GPU (e.g. gpu1cpu6, OCCI users: resource_tpl#f0cd78ab-10a0-4350-a6cb-5f3fdd6e6294)

Choose a suitable image (e.g. Ubuntu-14.04-UEFI, OCCI users: os_tpl#8fc055c5-eace-4bf2-9f87-100f3026227e)
Create a keypair for logging in to your server (and stored in tmpfedcloud.login context-file)

Create a VM with the selected image, flavor and keypair (OCCI users: copy the following very long OCCI command
          occi  --endpoint \
          --auth x509 --user-cred $X509_USER_PROXY --voms --action create --resource compute \
          --mixin os_tpl#8fc055c5-eace-4bf2-9f87-100f3026227e --mixin resource_tpl#f0cd78ab-10a0-4350-a6cb-5f3fdd6e6294 \
          --attribute occi.core.title="Testing GPU" \
          --context user_data="file://$PWD/tmpfedcloud.login"
       remark: check the proper os_tpl-ID by
          occi  --endpoint \
          --auth x509 --user-cred $X509_USER_PROXY --voms --action describe --resource os_tpl | grep -A1 Ubuntu-14 
Assign a public (floating) IP to your VM (using VM_ID from previous command and /occi1.1/network/PUBLIC
          occi --endpoint  \
          --auth x509 --user-cred $X509_USER_PROXY --voms --action link \
          --resource$YOUR_VM_ID_HERE -j /occi1.1/network/PUBLIC)

Log in the VM with your private key and use it as your own GPU server (ssh -i tmpfedcloud cloudadm@$VM_PUBLIC_IP)
    Remark: please update the VM-OS immediately: sudo apt-get update && unattended-upgrade; sudo reboot

Delete your VM to release resources for other users:
          occi --endpoint  \
          --auth x509 --user-cred $X509_USER_PROXY --voms --action delete \

Please remember to delete/terminate your servers when you finish your jobs to release resources for other users

For access to IISAS-GPUCloud via portal:

Get a token issued by Keystone with VOMS proxy certificate. You can use the tool from

Login into Openstack Horizon dashboard with the token via

Create and manage VMs using the portal.
Note: All network connections to/from VMs are logged and monitored by IDS.
If users have long computation, please inform us ahead. VMs with longer inactivity will be deleted for releasing resources 
The default user account for VM created from Ubuntu-based images via Horizon is "ubuntu". 
The default user account for VM created by rOCCI is defined in the context file "tmpfedcloud.login"

How to create your own GPGPU server in cloud

It is a short instruction to create a GPGPU server in cloud from Ubuntu vanilla image

Create a VM from vanilla image with UEFI support (e.g. Ubuntu-14.04-UEFI, make sure with flavor with GPU support)

Install gcc, make and kernel-extra: "apt-get update; apt-get install gcc make linux-image-extra-virtual"

Choose and download correct driver from, and upload it to the VM

Install the NVIDIA driver: "dpkg -i nvidia-driver-local-repo-ubuntu*_amd64.deb" (or "./NVIDIA-Linux-x86_64-*.run" )

Download CUDA toolkit from (choose deb format for smaller download)
Install the CUDA toolkit: "dpkg -i cuda-repo-ubuntu*_amd64.deb; apt-get update; apt-get install cuda" (very large install, 650+ packages, take a long time ~15 minutes)
  and set the environment (e.g. "export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}; export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64 ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}" )
Your server is ready for your application. You can install additional software (NAMD, GROMACS, ...) and your own application now

For your convenience, a script is created for installing NVIDIA + CUDA automatically
Be sure to make a snapshot of your server for later use. You may need to suspend your server before creating snapshot (due to KVM passthrough). 
Do not terminate your server before creating snapshot, whole server will be deleted when terminated
Other scripts for creating GPGPU servers with NVIDIA + CUDA on the cloud via occi, cloud-init and ansible roles have been developed as result of a collaboration with INDIGO and 
West-life, and are available at

Verify if CUDA is correctly installed

]$ sudo apt-get install cuda-samples-8-0
]$ cd /usr/local/cuda-8.0/samples/1_Utilities/deviceQuery
]$ sudo make
/usr/local/cuda-8.0/bin/nvcc -ccbin g++ -I../../common/inc  -m64    -gencode arch=compute_20,code=sm_20 -gencode 
mkdir -p ../../bin/x86_64/linux/release
cp deviceQuery ../../bin/x86_64/linux/release

]$ ./deviceQuery
./deviceQuery Starting...

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

Detected 1 CUDA Capable device(s)

Device 0: "Tesla K20m"
  CUDA Driver Version / Runtime Version          8.0 / 8.0
  CUDA Capability Major/Minor version number:    3.5
  Total amount of global memory:                 4743 MBytes (4972937216 bytes)
  (13) Multiprocessors, (192) CUDA Cores/MP:     2496 CUDA Cores
  GPU Max Clock rate:                            706 MHz (0.71 GHz)
  Memory Clock rate:                             2600 Mhz
  Memory Bus Width:                              320-bit
  L2 Cache Size:                                 1310720 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
  Maximum Layered 1D Texture Size, (num) layers  1D=(16384), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(16384, 16384), 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:                     No
  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 / 0 / 7
  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 K20m
Result = PASS

How to enable GPGPU passthrough in OpenStack

For admins of cloud providers

On computing node, get vendor/product ID of your hardware: "lspci | grep NVDIA" to get pci slot of GPU, then "virsh nodedev-dumpxml pci_xxxx_xx_xx_x"
On computing node, unbind device from host kernel driver
On computing node, add "pci_passthrough_whitelist = {"vendor_id":"xxxx","product_id":"xxxx"}" to nova.conf
On controller node, add "pci_alias = {"vendor_id":"xxxx","product_id":"xxxx", "name":"GPU"}" to nova.conf
On controller node, enable PciPassthroughFilter in the scheduler
Create new flavors with "pci_passthrough:alias" (or add key to existing flavor) e.g. nova flavor-key  m1.large set  "pci_passthrough:alias"="GPU:2"

How to transfer the ownership (user) of a VM in OpenStack

For admins of cloud providers

On controller node, execute this script:
#original author:
#--defaults-file=pw contains: user, password and other settings
[ -z "$*" ] && echo "Usage: $0 destination_user_id VM-1_id VM-2_id ..... VM-x_id" && exit 1
[ -z "$2" ] && echo "Usage: $0 destination_user_id VM-1_id VM-2_id ..... VM-x_id" && exit 1
for i
if [ "$i" != "$1" ]; then
echo "moving instance id " $i " to user id" $1;
mysql --defaults-file=pw <<query
use nova;
update instances set user_id="$1" where uuid="$i";
#get project id of the instance before update
proj_id=$(mysql --defaults-file=pw <<query
use nova;
select project_id from instances where uuid="$2";
#get user id of the instance before update
old_user_id=$(mysql --defaults-file=pw <<query
use nova;
select user_id from instances where uuid="$2";
echo "original_user=" $old_user_id project_id=$proj_id

And the "pw" file contains the following lines:



  • May 2015
    • Review of available technologies
    • GPGPU virtualisation in KVM/QEMU
    • Performance testing of passthrough
HW configuration: 
IBM dx360 M4 server with two NVIDIA Tesla K20 accelerators.
Ubuntu 14.04.2 LTS with KVM/QEMU, PCI passthrough virtualization of GPU cards.
Tested application:
NAMD molecular dynamics simulation (CUDA version), STMV test example (
Performance results:
Tested application runs 2-3% slower in virtual machine compared to direct run on tested server.
If hyperthreading is enabled on compute server, vCPUs have to be pinned to real cores so that
whole cores will be dedicated to one VM. To avoid potential performance problems, hyperthreading 
should be switched off.

  • June 2015
    • Creating cloud site with GPGPU support
Configuration: master node, 2 worker nodes (IBM dx360 M4 servers, see above)
Base OS: Ubuntu 14.04.2 LTS
Hypervisor: KVM
Middleware: Openstack Kilo
  • July 2015
    • Creating cloud site with GPGPU support
Cloud site created at, master + two worker nodes, configuration reported above
Creating VM images for GPGPU (based on Ubuntu 14.04, GPU driver and libraries)
  • August 2015
    • Testing cloud site with GPGPU support
Performance testing and tuning with GPGPU in Openstack 
 - comparing performance of cloud-based VM with non-cloud virtualization and physical machine, finding discrepancies and tuning them
 - setting CPU flavor in Openstack nova (performance optimization) 
 - Adjusting Openstack scheduler
Starting process of integration of the site to EGI FedCloud
 - Keystone VOMS support being integrated
 - OCCI in preparation, installation planned in September
  • September 2015
 Continue integration to EGI-FedCloud
  • October 2015
 Full integration to EGI-FedCloud, being in certification process
 Support for moldyngrid, and VO
  • November 2015
 Create new authentication module for logging into Horizon dashboard via keystone token
 Various client tools: getting token, installing nvidia+cuda,
 Participation on EGI Community Forum v Bari
 Site certificated
  • December 2015
 User support: adding and testing images from various VOs, solving problems with  multiple-VO users
 Maintenance: security updates and minor improvements
  • January 2016
 Testing + performance tuning OpenCL
 Updating images with CUDA
 Adding Openstack Ceilometer for betting resource monitoring/accounting
  • February-March 2016
 Testing VM migration
 Examining GLUE schemes
 Examining accounting format and tools
  • April 2016
 Status report presented at EGI Conference 2016
  • May 2016
GLUE2.1 draft discussed at GLUE-WG meeting and updated with relevant Accelerator card specific attributes.
GPGPU experimental support enabled on CESNET-Metacloud site. VMs with Tesla M2090 GPU cards tested with DisVis program. 
Working on support for GPU with LXC/LXD hypervisor with Openstack, which would provide better performance than KVM.
  • Next steps
Production, application support
Cooperation with APEL team on accounting of GPUs
Generating II according to GLUE 2.1

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