As you may already know, virtualization involves a hypervisor running on top of the host machine and utilizing its resources for the virtual machines. Additionally, it’s essential to be aware of GPU virtualization, which may differ from what you initially anticipated.. Here we tell you about it:
What is GPU virtualization?
Virtual machines utilize hardware resources such as vCPU and vRAM. The vGPU or virtual GPU, on the other hand, enables virtual machines to access one of the physical GPUs of the host simultaneously. GPU virtualization is a technology that accelerates graphics or applications running on a virtual machine using a GPU. This involves the use of various techniques such as device emulation and remote API.
This can also bring great benefits by making the system more efficient. This enables better utilization of hardware resources among different virtual machines. In fact, with just the host system, you would not really be taking advantage of all those resources. Especially if the workload is not at its maximum level. This is something we already see in VPS.
GPU virtualization reduces CPU usage by enabling virtual machines to utilize this hardware resource. This happens because the software doesn’t use graphics acceleration. In essence, GPU virtualization enables the same utilization of physical GPUs as with other resources, allowing for rendering and other virtual machine tasks. Hardware graphics acceleration is thus made possible.
Advantages of GPU virtualization
GPU virtualization can not only improve efficiency and performance. In fact, it also has other advantages, especially for data centers or VPS servers. For example, the following should be noted:
- Performance: GPU virtualization improves the performance of virtual machines. Especially everything related to graphics, in addition to accelerating tasks such as AI, ML, GPU computation, etc. Additionally, by reducing the CPU workload by not having to use software acceleration, overall performance is better
- Reduces bottlenecks: Recall that it reduces the workload on the CPU. So it allows the system to perform more smoothly at peak workloads. Virtual machines will be able to better distribute the workload between the CPU and GPU. This ensures a more robust performance.
- Greater wealth:by being able to use applications that rely on GPU capabilities, this can enhance the richness of the virtualized system. For example, being able to run video games, AI software, GPU rendering. It also includes tasks that can make use of the GPU. The GPU decodes some of them instead of the slower CPU.
Ultimately, this allows virtual machines to perform increasingly better and closer to physical machines.
Techniques used in GPU virtualization
As mentioned above, there are several techniques used for GPU virtualization. The most important ones are:
- Remote API: is a technique that allows communication between the graphics API for call forwarding. That is to say, when an application is used in the virtual machine that makes use of graphics, the calls to the graphics API will be forwarded so that they can be attended by the graphics API of the host system. They can then be processed by the GPU. This technique is not perfect. Indeed, call forwarding compromises performance and prevents the virtual machine from being fully isolated from the host system. On the other hand, there is third-party software that can also add support for specific APIs, such as VMGL for OpenGL, rCUDA for CUDA, and so on.
- Fixed pass-through or GPU pass-through: This fixed pass-through is when a single virtual machine directly accesses a GPU on an exclusive and permanent basis. In this technique, performance is better. Indeed, it achieves 96-100% of native performance. In addition to high fidelity. In other words, it is superior to the 86% performance achieved by the previous technique. However, the downside is that multiple virtual machines cannot share the same GPU. That is, each one should have its own additional physical GPU. Consequently, this increases the cost of the required hardware.
- Mediated transfer: This technique allows the GPU hardware to provide contexts with virtual memory ranges for each virtual machine through the IOMMU. As a result, the hypervisor will send the graphics commands from the virtual machines to the GPU. In other words, it is like a kind of partitioning of GPU resources. In this way, they will directly serve the virtual machines. This would be in between the two previous techniques. That is, without the need for API call forwarding, but being able to allow all virtual machines to make use of the same GPU. Technologies such as NVIDIA vGPU, AMD MxGPU, Intel GVT-g, etc., support this capability.
- Device emulation: As GPU architectures are very complex and constantly changing, it is not feasible for hypervisor software developers to virtualize new generations of GPUs in a complex way. It is only possible to do so with some older and simpler models, such as the 3dfx Voodoo2, the S3 ViRGE/DX, etc. This allows that if the virtual machine does not have 3D graphics acceleration, it can at least have a minimum functionality to access these machines through a graphics terminal.
GPU virtualization is a technology that allows multiple users to access and share the same GPU resources. It can support various applications, including machine learning, gaming, data visualization, and more. Benefits include increased efficiency in resource utilization and improved scalability of computing performance. By providing secure access to GPUs from different locations or devices, it also helps reduce costs associated with hardware investments while still delivering high-performance results. In conclusion, GPU virtualization provides many benefits that make it an ideal solution for businesses looking to maximize their computing capabilities without breaking the bank!