Why NVIDIA GRID™ vGPU™ for desktop virtualization?

A Look Into Desktop Virtualization

‘What is VDI?’

Not surprisingly, this is an oft-repeated question among professionals newly introduced to the concept of VDI especially in non-technical environments.

Let’s begin with a simple VDI definition. Desktop virtualization is software technology which isolates users’ PC applications from the physical computer and allows them to access the VM from their client devices across a network. You can use your virtual desktop in the same way as you would interact with a physical desktop. The added benefit is you can gain remote access to your virtual desktop and applications anytime and on any device.

Why Use GPU for VDI?

Today, there is a large number of technological platforms to choose from for increasing IT infrastructure and capability. Hence, there comes an even greater need for solutions that are cost-effective and rich in terms of UX. Desktop virtualization accommodates this by assigning a dedicated GPU to each VM to improve performance and accelerate end UX.

However, finding the perfect virtual desktop solution which meets varying IT needs remains one of the most challenging undertakings for IT administrators. Also, with a larger number of IT resources and users in the workplace, businesses must facilitate access to high-performance virtualization applications for maximum performance.

Keeping this in view, let’s look at what vGPU technology can do for desktop virtualization.

The following is a brief guide to selecting the perfect GPU for your virtual desktop requirements. After this, we shall look at how NVIDIA GRID vGPU complements VDI.

Choosing the Best GPU for Desktop Virtualization

While most GPUs offer scalability and complete application availability, the real challenge is to provide a smooth and interactive UX in broad use cases. Ideally, businesses should come up with a solution by classifying users according to their GPU requirements in virtualized environments.

This is because users who perform basic operational applications do not have professional graphics design related duties. These teams may not require hardware accelerated graphics or a dedicated graphics card for extensive uses like Big Data visualization which involve high resolution and frame rates. However, other users might perform advanced IT responsibilities and have a deeper understanding of complex business tasks and processes.

Considering their varying skill levels, the system requirements for these users must be segregated into high-end, mid-range and standard groups.

For instance, users who run video games based tasks and professional 3D applications require heavy duty graphics cards for pushing visual quality to the next level. Examples are designers who often build, edit and render massive and advanced 3D models while engineers follow up by editing these prototypes. They demand industry standard graphic accelerators and resources which can manage the graphical and computational workflows in virtualized environments.

Mid-level users typically perform on a smaller scale than power users. Nevertheless, some of their workloads may involve numerous graphics applications where they must draw up comprehensive 3D models for implementing related tasks.

Hence, conducting workload analysis is necessary for GPU-based scalability and a good optimization strategy.

How does NVIDIA GRID vGPU work?

NVIDIA developed GRID software to enhance mobility, security and productivity by virtualizing and allocating GPUs across a large number of VMs and applications. Using NVIDIA vGPU technology drives the highest possible standards of elasticity, security and manageability in all virtual workloads. In essence, the GRID-enabled hypervisor and vGPU solution is deployed at the virtualization layer. vGPU software then creates vGPUs so that the physical GPU installed on the server is allocated between all VMs.

The virtualization software comes with an independent graphics driver for every virtual machine. This allows the CPU to seamlessly offload workloads to the GPU which supports graphics-intensive applications in virtualized workspaces.

Now, let’s look at why businesses should adopt NVIDIA GRID vGPU for VDI.

Rich End User Experience (UX)

Successful desktop virtualization planning begins with companies recognizing and understanding the diversity of user requirements in the workplace. As established, NVIDIA software optimizes desktop, application and workstation virtualization by distributing vGPUs to every user across the organization. NVIDIA vGPU technology also supports a wide range of heavy lifting multimedia, creative and engineering applications in different industries with virtualized environments.

Team members have revolutionized the way they perform in these VDI environments. As a result, modern graphics-intensive applications and OS must be immersive, consistent and productive. This is especially important for remote users demanding access to any specialized application on all possible devices. With a GRID-based virtualized platform, users can access, create, evaluate and work on rich visual content anywhere and anytime.

NVIDIA vGPU also facilitates both high density and general purpose computing environments by implementing extensive server-side graphics, monitoring and administrative efficiencies. This allows virtualized environments to stand out with quality graphics in AI, creative, designing and engineering (CAE/CAM/CAD) forums, leading to the highest possible levels of UX.

Powerful Management and Monitoring Competencies

IT administrators materialize business computing strategy and outcomes by overseeing virtual workflow processes where users often implement multiple parallel tasks simultaneously. Two common examples include image and video rendering.

Hence, businesses should ensure proactive end-to-end monitoring with NVIDIA vGPU in desktop virtualization. This generates actionable user and application insights that help achieve complete workspace visibility and transparency. Organizations can also uncover excessive consumption patterns by identifying and isolating potential GPU bottlenecks that may affect productivity and the overall UX.

Monitoring vGPU usage on VMs in real-time also makes it easier for IT to keep track of VMs. This boosts operational responsiveness to workflows and GPU performance especially during crunch time. Also, using NVIDIA vGPU to identify potential lags in VMs before they cause any real damage promotes fast issue resolution and smart resource planning for streamlining workplace activities.

Furthermore, businesses can consolidate NVIDIA vGPU software into an open NVIDIA SDK for creating customized IT system monitoring and management settings. This leverages host, guest and application analytics to promote data-driven decision making and improve UX at every touch point. It also becomes easier for users to consistently meet expected performance levels, hence maximizing business productivity in enterprise-scale virtualized environments.

Flexible Administration and Scaling

NVIDIA GRID vGPU plays an important role in virtualizing graphics and ensuring interactive onsite and offsite experiences by securing workloads within the datacenter. For this reason, monitoring GPU is important so that businesses are aware of the extent to which they are drawing on its capability.

A good GPU management solution is the NVIDIA Data Center GPU Manager (DCGM). This helps IT implement system policies, track GPU health and increase datacenter throughput in the accelerated datacenter. Companies can leverage both vGPU and DCGM to raise productivity standards to a whole new level in enterprise-class initiatives.

Furthermore, NVIDIA not only provides advanced solutions that scale across GPUs in a system. Its range also consists of GPU Cloud Rendering which facilitates interactive 3D UX on smartphones, laptops and tablets. This provides easier and more powerful access to users in companies where VDI and BYOD programs work in tandem to deliver the ultimate experience.

Additionally, organizations can configure unique user login credentials on the basis of their graphics power requirements and application workloads. For instance, IT can assign a NVIDIA Tesla M60 board to sixteen engineers, data scientists and researchers who frequently engage in deep learning tasks. Task or knowledge workers performing less complex roles can access a Tesla M10 board which supports approximately thirty two VMs. Using this model, IT can simplify the process of scaling user capacity as per their application and project demands.

Another example is an international architectural company which supervises geographically dispersed teams whose members interact with one another in the same way as they would within one physical workspace. Desktop virtualization with vGPU-enabled technology secures data in a single location and customizes performance by allowing IT to individually personalize UX based on each user’s specific needs.

In this scenario, a design engineer developing 3D graphics content will expect to receive the entire server’s GPU resources. On the other hand, knowledge workers collaborating on 2D applications would generally be delegated a relatively smaller portion of the system’s resources. In both cases, the organization will analyze the needs of team players and scale performance accordingly so that IT can set parameters for optimal GPU usage. This allows them to measure and maintain consistent individual productivity standards.

Is vGPU suitable for all use cases?

vGPU effectively supports VDI once you establish why and when you should adopt this technology. It ultimately comes down to your end users and the nature of work they do. For instance, implementing vGPU technology would not be the right fit for your business if your staff mostly consists of task workers. Using the standard Raspberry Pi thin client for VDI will suffice for them because the client side does not involve intensive processing where their work is concerned.

Next, you must identify which GPU technology would work best for your organization. Will it be integrated or dedicated? Once again, deciding between the best options will require considerable time and consideration.

A quick and easy alternative is to consider ClearCube’s CVDI solutions for your virtual desktop deployment. For example, our PCoIP Blade PCs work well in circumstances where power users would otherwise need a dedicated graphics card for implementing their roles. These integrated PCoIP and GPU endpoints provide the fastest datacenter to desktop connections in such environments where great performance and easy manageability are the keys to success.

For further information on our CVDI range, please click here .

Conclusion

NVIDIA GRID vGPU technology is synonymous with rich and interactive graphics experience across virtual machines. In a nutshell, NVIDIA vGPU system implementations go beyond promising security, manageability and support across virtualized environments. The technology serves as a benchmark for creating quality enterprise IT deliverables which enable users to follow through with actionable performance plans.

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