Difference between revisions of "GPGPU-FedCloud"

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* Next steps  
 
* Next steps  
  and production support
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  Production, application support
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Cooperation with APEL team on accounting of GPUs
  
 
= [https://wiki.egi.eu/wiki/EGI-Engage:TASK_JRA2.4_Accelerated_Computing Back to Accelerated Computing task] =
 
= [https://wiki.egi.eu/wiki/EGI-Engage:TASK_JRA2.4_Accelerated_Computing Back to Accelerated Computing task] =

Revision as of 11:19, 11 October 2015

EGI-Engage project: Main page WP1(NA1) WP3(JRA1) WP5(SA1) PMB Deliverables and Milestones Quality Plan Risk Plan Data Plan
Roles and
responsibilities
WP2(NA2) WP4(JRA2) WP6(SA2) AMB Software and services Metrics Project Office Procedures



Objective

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


Participants

Viet Tran (IISAS)

Jan Astalos (IISAS)

Miroslav Dobrucky (IISAS)

Current status

A working site with GPGPU in EGI federated cloud https://cloudmon.egi.eu/nagios/cgi-bin/status.cgi?host=nova3.ui.savba.sk

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

EGI federated cloud configuration:

GOCDB: IISAS-GPUCloud, https://goc.egi.eu/portal/index.php?Page_Type=Site&id=1485
Openstack endpoint: https://keystone3.ui.savba.sk:5000/v2.0
OCCI endpoint: https://nova3.ui.savba.sk:8787
Supported VOs: fedcloud.egi.eu, ops, dteam, moldyngrid

How to use GPGPU on IISAS-GPUCloud

For EGI users:

Join EGI federated cloud https://wiki.egi.eu/wiki/Federated_Cloud_user_support#Quick_Start

Get VOMS proxy certificate from fedcloud.egi.eu or any supported VO (voms-proxy-init --voms fedcloud.egi.eu -rfc)
 
Choose suitable gpu.* flavor (e.g. gpu.medium or gpu.large) (OCCI users: resource_tpl#6 or resource_tpl#7)

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 https://wiki.egi.eu/wiki/Fedcloud-tf:CLI_Environment#How_to_create_a_key_pair_to_access_the_VMs_via_SSH) 

Create a VM with the selected image, flavor and keypair (OCCI users: occi  --endpoint  https://nova3.ui.savba.sk:8787/ \
                  --auth x509 --user-cred $X509_USER_PROXY --voms --action create --resource compute \
                  --mixin os_tpl#3e1ad5a5-4d5a-4dbb-8b93-5c329129d3e6 --mixin resource_tpl#6 \
                  --attribute occi.core.title="Testing GPU" \
                  --context user_data="file://$PWD/tmpfedcloud.login")

Log in the VM and use it as your own GPU server.

For access to IISAS-GPUCloud via portal:

Contact with cloud_admin _at_ savba.sk to get account and access to full-featured graphical portal

How to create your own GPGPU server

It is a short instruction to create a GPGPU server 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 install gcc make linux-image-extra-virtual

Choose and download correct driver from http://www.nvidia.com/Download/index.aspx, and upload it to the VM

Install the NVIDIA driver: ./NVIDIA-Linux-x86_64-346.96.run

Download CUDA toolkit from https://developer.nvidia.com/cuda-downloads (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

Progress

  • 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 (http://www.ks.uiuc.edu/Research/namd/).
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 keystone3.ui.savba.sk, 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, certification 
  • Next steps
Production, application support
Cooperation with APEL team on accounting of GPUs

Back to Accelerated Computing task