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


Viet Tran (IISAS)

Jan Astalos (IISAS)

Miroslav Dobrucky (IISAS)

Current status

A working site with GPGPU in EGI federated cloud

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.

SW configuration:

Base OS: Ubuntu 14.04.2 LTS
Hypervisor: KVM
Middleware: Openstack Kilo
GPU-enable flavors: gpu1cpu6 (1GPU + 6 CPU cores), gpu2cpu12 (2GPU +16 CPU cores)

EGI federated cloud configuration:

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

How to use GPGPU on IISAS-GPUCloud

For EGI users:

Join EGI federated cloud

Get VOMS proxy certificate from or any supported VO (voms-proxy-init --voms -rfc)
Choose suitable gpu.* flavor (e.g. gpu1cpu6) (OCCI users: resource_tpl#f0cd78ab-10a0-4350-a6cb-5f3fdd6e6294)

Choose suitable image (e.g. Ubuntu-14.4, OCCI users: os_tpl#3e1ad5a5-4d5a-4dbb-8b93-5c329129d3e6)

Create a keypair for log in to your server (see 

Create a VM with the selected image, flavor and keypair (OCCI users: occi  --endpoint \
                  --auth x509 --user-cred $X509_USER_PROXY --voms --action create --resource compute \
                  --mixin os_tpl#3e1ad5a5-4d5a-4dbb-8b93-5c329129d3e6 --mixin resource_tpl#f0cd78ab-10a0-4350-a6cb-5f3fdd6e6294 \
                  --attribute occi.core.title="Testing GPU" \
                  --context user_data="file://$PWD/tmpfedcloud.login")

Log in the VM and use it as your own GPU server.
Please remember to terminate your server when you finish your jobs to release resources for other users

For access to IISAS-GPUCloud via portal:

Contact with cloud-admin _at_ to get account and access to full-featured graphical portal

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 (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: ./

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, take a long time)

Your server is ready for your application. You can install additional software (NAMD, GROMACS, ...) and your own application now

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

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"


  • 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
  • Next steps
Production, application support
Cooperation with APEL team on accounting of GPUs

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