Universities and colleges face a looming challenge as we approach the 2021-2022 academic year: to provide optimal learning and teaching experiences for students and staff during blended learning.
A far cry from the early months of the pandemic, when universities turned to emergency remote learning as quickly as possible. Over the next academic year, higher education institutions will focus on better blended learning solutions. According to a recent survey by the Institute of International Education, 62% of U.S. post-secondary schools will offer a combination of distance and in-person learning.
The challenge is to ensure that institutional IT infrastructures are up to the task. Since students and staff will likely still be dispersed in multiple locations, they will need reliable access and robust network performance available anywhere, anytime.
Higher education institutions need solutions that can meet today’s demands, with the ability to scale to meet changing demands.
This is where tools like workspace virtualization through NVIDIA Virtual GPU (vGPU) Software can help. But what exactly is NVIDIA vGPU technology? And how can virtual desktop infrastructure (VDI) help create quality online learning experiences?
What is NVIDIA vGPU technology?
When it comes to meeting the high expectations of Gen Z students for user experience and performance, NVIDIA vGPU virtualization technology is essential. NVIDIA vGPU software provides virtual workspaces equivalent to physical workstations. It enables server-class performance for even the most compute-intensive courses. NVIDIA vGPU software enables IT teams to help remote learners stay productive, whether they’re working on sophisticated artificial intelligence applications or collaborating on team projects in real time.
In practice, this means shifting the computing processing load from local physical devices using VDI, which delivers desktop applications and services through virtual machines (VMs). These are delivered to end users over a network connection.
“On a physical device, the GPU is typically installed in the device and handles all of the rendering,” says Phoebe Lee, product marketing manager at NVIDIA. “VDI-based NVIDIA vGPU technology enables GPUs in the data center to provide GPU processing. All rendering is done on the server.
Universities and colleges can opt for on-premises or cloud deployments depending on their needs. For example, some schools prefer on-premises solutions that offer maximum provisioning flexibility. “For this implementation, many schools use the data center to provision and create virtual machines to run student and staff workloads in the morning. Then, at night, they use the same GPU resources that are freed up from morning classes to run high-performance computing tasks,” Lee explains.
For higher education institutions that prefer cloud computing, NVIDIA works with major cloud service providers as well as smaller and regional providers to provide maximum flexibility.
Lee simply describes NVIDIA’s approach to GPU delivery: “It’s no longer a one-to-one relationship, but one-to-many that provides different solutions based on user use cases and needs.”
How does NVIDIA vGPU help universities deliver quality education?
To handle compute-intensive workloads on campus, universities and colleges have historically outfitted student and staff workspaces with desktop computers each containing a dedicated GPU.
However, online learning and teaching introduced a new challenge: if students could not afford the right technology, they were effectively excluded from an engaging and quality education.
As noted by Lee, NVIDIA’s vGPU solution helps create a “wireless campus” that supports virtualized classrooms and lab sessions as well as distance learning programs. “At a high level,” she says, “universities can extend education anywhere to anyone at any time.
For Clemson University, this involved deploying a vGPU infrastructure to better support its in-house developed high-performance computing cluster, Palmetto. This resulted in reduced IT costs, with fewer on-premises servers required. It also improved resource utilization and optimized performance. the University of Colorado School of Dentistry, meanwhile, relied on NVIDIA to perform a VDI hardware refresh. This has improved access for distance learning students to essential dental education tools.
How can universities effectively implement NVIDIA vGPU on campus?
If a university or college already has an established VDI framework, the implementation is simple: the vGPU simply leverages existing infrastructure to provide on-demand compute resources. For higher education institutions that do not have their own data center or VDI infrastructure, NVIDIA works with large and small cloud providers to offer VM solutions.
With vGPU, customization is standard. “There are several ways to provision resources,” Lee explains. “Schools can slice a single GPU into multiple smaller GPUs. For some schools with compute-heavy workloads, we see them tying multiple GPUs together to provide more power. It really depends on the workload.
Additionally, vGPU solutions do not require physical device upgrades to meet changing software or hardware requirements.
This is the core value proposition of NVIDIA’s vGPU: based on student, faculty, and staff workloads, resources can be allocated on demand to enhance in-person and online classroom experiences.