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Unlocking Extreme Performance with VMware+vGPU Benchmarks

Updated on Mar 22,2024

Unlocking Extreme Performance with VMware+vGPU Benchmarks

Table of Contents

  1. Introduction
  2. What is Virtualization of GPUs and Their Benefits
  3. Performance of Virtualized GPUs
  4. Scaling Machine Learning Workloads with NVIDIA Virtual GPUs
  5. Inferencing Performance with NVIDIA Virtualized GPUs
  6. Conclusion

Introduction

In this article, we will explore the world of GPUs and vGPUs (virtualized GPUs) and delve into their performance capabilities. We have a special guest, Uday, a machine learning expert at VMware, who will guide us through the fundamentals and showcase the incredible performance delivered by vGPUs. Let's dive in and discover how VMware vSphere and NVIDIA vGPUs offer bare metal-like performance for AI workloads.

What is Virtualization of GPUs and Their Benefits

Virtualization of GPUs offers a multitude of benefits for VMware customers. It allows them to leverage their GPU investments across different domains and utilize ML workloads on Linux, virtual desktop infrastructure for 3D CAD and office workers on Windows, and more. This enables improved data center resource utilization by harnessing the power of NVIDIA vGPUs in VMware vSphere. We will explore how customers are currently using vGPUs to accelerate 2D/3D graphics workloads, enable VMware Blast Extreme protocol, and enhance machine learning and high-performance computing tasks.

Performance of Virtualized GPUs

When it comes to virtualization and running workloads in a virtualized environment, the performance aspect is crucial. In this section, we will compare the performance of virtualized GPUs with native GPUs, specifically focusing on training workloads. By examining various benchmarks from different domains, including Speech Synthesis, Speech Recognition, recommendation engines, language translation, and object detection, we will highlight that vGPUs deliver bare metal-like performance. This dispels any notions of significant virtualization overhead, confirming that virtualization is a viable option for machine learning workloads.

Scaling Machine Learning Workloads with NVIDIA Virtual GPUs

Scalability is essential for machine learning workloads, and by adding NVIDIA GPUs, particularly NVIDIA's vGPUs, businesses can achieve enhanced scalability. We will explore the concept of scaling from one machine to multiple machines, each equipped with virtualized NVIDIA A100 GPUs. By connecting these machines using RDMA over converged Ethernet, we can demonstrate how vGPUs deliver bare metal performance across multiple machines. This allows for distributed deep learning training, making it easier to Scale machine learning workloads and harness the power of virtualization.

Inferencing Performance with NVIDIA Virtualized GPUs

Inferencing performance is another critical aspect when considering the capabilities of virtualized GPUs. We will analyze the results submitted to MLPerf, focusing on two different types of workloads: natural language processing (NLP) and object detection. By examining the normalized performance in comparison to bare metal, we will demonstrate how vGPUs offer bare metal-like performance. The results showcase the capability to process samples per Second for NLP and images per second for object detection without sacrificing performance, even in a virtualized environment.

Conclusion

In conclusion, the combination of NVIDIA AI Enterprise with VMware vSphere provides users with superior power, eliminating any concerns regarding virtualization overhead. The advent of vGPUs allows for bare metal-like performance, making it a compelling solution for machine learning workloads. Additionally, businesses can benefit from the consolidation of workloads, optimizing data center resource utilization and ultimately making their operations more efficient. By embracing the power of virtualization, users can maximize the potential of NVIDIA GPUs, coupled with the virtualization benefits of VMware vSphere.

Highlights

  • The virtualization of GPUs facilitates the utilization of investments across different domains and workloads.
  • Virtualized GPUs in VMware vSphere offer bare metal-like performance for training and inferencing workloads.
  • Adding NVIDIA vGPUs enables enhanced scalability for machine learning tasks.
  • Inferencing performance with virtualized GPUs matches or exceeds the performance of bare metal systems.
  • VMware vSphere combined with NVIDIA AI Enterprise delivers superior power and virtualization benefits for machine learning workloads.

FAQ

Q: Can virtualized GPUs match the performance of bare metal systems? A: Yes, virtualized GPUs, specifically NVIDIA vGPUs in VMware vSphere, deliver bare metal-like performance for various workloads.

Q: How can businesses benefit from scaling machine learning workloads using NVIDIA vGPUs? A: By adding NVIDIA vGPUs and leveraging distributed deep learning training, businesses can scale their machine learning workloads while maintaining bare metal-like performance.

Q: Can virtualized GPUs optimize data center resource utilization? A: Absolutely! Virtualized GPUs allow for the consolidation of workloads, maximizing resource utilization and making data centers more efficient.

Q: Do virtualization benefits come at the cost of performance? A: No, virtualization benefits, such as mobility and resource consolidation, can be achieved without sacrificing performance when using NVIDIA vGPUs in VMware vSphere.

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