The Nvidia is responsible for offering some of the most efficient and powerful data center GPUs which help in scaling the varying requirements of the industry. The clients can hyperscale workloads of data centers and speed up the most challenging HPC via Nvidia server GPU. The data scientists and researchers can now parse petabytes of data with faster processing power than it was possible with the conventional CPUs, in various applications from energy exploration to deep learning. The Nvidia accelerators also provide the necessary horsepower that is required for running the bigger simulations at faster speeds than ever before. The Nvidia GPUs are also responsible for highest performance and user density for applications, workstations and virtual desktops.
The Nvidia partners offer a wide array of cutting-edge servers which are capable of AI, diverse HPC and they accelerate the computing workloads. The Nvidia provides GPU-accelerated server platforms for the promotion of optimal servers for different workloads and the manufacturer suggests the ideal classes of servers for various training inference applications and supercomputing. These platforms align the entire data center server ecosystem and as the customers select a specific server platform according to the accelerated computing application, they are given access to some of the finest performance in the industry.
The feature of virtual computation for the server intensive workloads
The virtual compute server of the Nvidia server GPU is responsible for enabling data centers to speed up the server virtualization via the modern data center GPUs from the company. This ensures that some of the most intense and sophisticated computational workloads such as artificial intelligence, deep learning and data science can be executed as well as operated in virtual machine.
The virtualization provides various benefits and features that are extremely productive as well as highly efficient. Some of these features are GPU sharing, GPU aggregation, management and monitoring, GPUDirect, ECC and page retirement, Multi-instance GPU, NGC, and Peer-to-peer computing. We will have a brief look at some of these features.
GPU aggregation: A virtual machine can get access to numerous GPUs through GPU aggregation, which is often necessary for computational intensive workloads. The vCS provides support to both the peer-to-peer computing and multi-vGPU. The GPUs are not directly connected in the multi-vGPU whereas they are directly connected via NVLink for the higher bandwidth in the peer-to-peer setup.
GPU sharing: The fractional GPU sharing is only possible through Nvidia's vGPU technology. This enables the multiple virtual machines to share the GPU thereby maximizing the utilization of lighter workloads which need the GPU acceleration.
NGC: The Nvidia GPU cloud (NGC) is the hub for the GPU-optimized software which simplifies the workflows for the machine learning, deep learning and the HPC. It now supports the virtualized environments through Nvidia vCS.
Management and monitoring: The vCS gives support to the host, guest and app level monitoring. Apart from the proactive management features gives the capability of doing live migration, suspension, resumption, and the creation of thresholds which expose the consumption trends that impact user experiences. This is all done through vGPU management SDK.