![]() ![]() This allocates more or less time "slices" to a VM so that more demanding VMs might get more GPU time, while less demanding or test VMs might get less GPU time. The greater volume of VMs that share a GPU and the level of graphics demands those VMs place upon it, the less effective vGPU performance becomes.Īdministrators can adjust performance by configuring vGPU resources. This combination of GPU time-sharing and nonshared video memory places a practical limit on the number of VMs that can share a GPU. For example, if the graphics adapter has 32 GB and is split between 10 VM-vGPU pairs, each vGPU would have access to 3.2 GB. ![]() The video memory included with the GPU adapter card is split between the VMs using a vGPU. A graphics driver in each VM connects the VM's workload to the vGPU. ![]() This establishes the time-sharing mechanism that enables VMs to share the GPU. The manager handles the segregation of a physical GPU into vGPUs and assigns those vGPUs to VMs. Hyper-V does not yet support vGPU at time of publication, but Citrix XenServer and VMware ESXi can run a virtual GPU manager. The basic relationship of the GPU, hypervisor and VMs is illustrated in the diagram below. This technique is sometimes referred to as virtual shared graphics acceleration, or vSGA. For example, if a GPU is virtualized into 10 vGPUs, and each vGPU is assigned to one of 10 VMs, each VM would have access to the GPU - and its CUDA cores - for 10% of the time. This approach creates a time-sharing environment that enables multiple VMs to share the GPU's resources. The alternative means to virtualize GPUs is the vGPU mode. Microsoft Hyper-V, Citrix XenServer and VMware ESXi all support pass-through mode. This offers the best performance and provides excellent value if many users share the VM. Pass-through mode is ideal for centrally hosted VMs that run the most demanding or graphics-intensive applications. A business might deploy numerous physical servers, each with a multi-GPU graphics adapter, to provide GPU support for many VMs. ![]() For example, the Nvidia M6 has one physical GPU, the Nvidia M60 has two physical GPUs, and the Nvidia M10 has four GPUs - even though a graphics adapter like the M10 hosts 2,560 Nvidia CUDA (Compute Unified Device Architecture) cores. Nevertheless, modern graphics adapters often support multiple GPU chips, and each GPU chip can be assigned to different VMs. Pass-through cannot assign different cores to different VMs within the same GPU chip. This means a GPU chip might have many cores on board, but pass-through assigns the entire GPU chip - and all its cores - to the associated VM. Pass-through operates at the chip level, not the core level. However, other VMs cannot access or benefit from that GPU. This technique is sometimes referred to as virtual dedicated graphics acceleration ( vDGA). The VM then has complete use of the GPU and can realize 100% of the GPU's capabilities, including processing power and associated graphics memory. When a GPU operates in pass-through mode, the hypervisor assigns one GPU to one VM. What is GPU pass-through?Ī pass-through mode provides access, control and use of an entire device. Let's take a closer look at GPU support and review the steps to include GPUs in hypervisors, such as VMware. GPU support often must be enabled deliberately and added to VM configurations before the VM can use those GPU capabilities. Hypervisors support GPUs in either pass-through or virtual GPU ( vGPU) modes. The addition of GPU support to VMs enables virtualized workloads on premises and in the cloud to handle the demanding computation efficiently for tasks such as real-time data visualization and virtual desktop graphics.īut GPU support isn't automatic. ![]()
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