Difference between revisions of "GPGPU-FedCloud"

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*Live VM migration is not supported  
 
*Live VM migration is not supported  
 
*VM snapshot with memory is not supported
 
*VM snapshot with memory is not supported
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== Cloud Management Frameworks ==
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Some initiatives but not completed
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*HeterogeneousGpuAcceleratorSupport in OpenStack (https://wiki.openstack.org/wiki/HeterogeneousGpuAcceleratorSupport)
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*GPU and vGPU support for CloudStack Guest VMs (https://cwiki.apache.org/confluence/display/CLOUDSTACK/GPU+and+vGPU+support+for+CloudStack+Guest+VMs)
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Work to be done:
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*Define VM types/flavors with attributes for GPGPU
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*Modify VM start to allow passthrough or allocate vGPU
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*Modify scheduler to allocate VMs with GPGPU correctly

Revision as of 14:46, 30 April 2015

Status of accelerated computing in Clouds

Need modification/support at all levels

  • Chipset : HW virtualization support (otherwise some limitation)
  • OS level: correct kernel configuration for the accelerators
  • Hypervisor: configuration pass-through, vGPU
  • CMFs: VM start, scheduler
  • FedCloud facilities: accounting, information discovery
  • Application: VM images with correct drivers for specific chipsets


Accelerators

GPGPU (General-Purpose computing on Graphical Processing Units)

NVIDIA GPU/Tesla/GRID, AMD Radeon/FirePro, Intel HD Graphics,...

Virtualization using VGA pass-through, vGPU (GPU partitioning) - NVIDIA GRID accelerators

Intel Many Integrated Core Architecture

Xeon Phi Coprocessor

Virtualization using PCI pass-through

Specialized PCIe cards with accelerators

DSP (Digital Signal Processors)

FPGA (Field Programmable Gate Array)

Not commonly used in cloud environment

Hypervisors

QEMU/KVM

Supports only pass-through virtualization model

vGPU support is under development

Citrix XenServer 6, VMware ESXi 5.1

Support both pass-through and vGPU virtualization models

Limitations:

  • vGPU support require certified server HW
  • Live VM migration is not supported
  • VM snapshot with memory is not supported

Cloud Management Frameworks

Some initiatives but not completed

Work to be done:

  • Define VM types/flavors with attributes for GPGPU
  • Modify VM start to allow passthrough or allocate vGPU
  • Modify scheduler to allocate VMs with GPGPU correctly