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GTC ON-DEMAND

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Abstract:
vMotion is a feature in VMware vSphere that is used to guarantee continuous availability and uptime in the face of planned outages and maintenance operations. It's now available for virtual machines (VMs) with an NVIDIA GRID GPU. We'll present an overview of the architecture of vMotion for vGPU-enabled VMs and discuss how it affects performance. We'll also outline potential vMotion uses for load balancing on vGPU-enabled clouds.
vMotion is a feature in VMware vSphere that is used to guarantee continuous availability and uptime in the face of planned outages and maintenance operations. It's now available for virtual machines (VMs) with an NVIDIA GRID GPU. We'll present an overview of the architecture of vMotion for vGPU-enabled VMs and discuss how it affects performance. We'll also outline potential vMotion uses for load balancing on vGPU-enabled clouds.  Back
 
Topics:
Data Center & Cloud Infrastructure, AI Application, Deployment & Inference
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9411
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Abstract:
Video- and audio-based applications now comprise about 80 percent of Internet traffic, but their quality depends on the network condition. Cloud providers must accurately quantify and monitor video and audio quality so they can maintain the quality of these applications while optimizing cloud resource usage. We'll describe our solution, which uses deep neural networks to measure the quality of video and audio, and demonstrate how we measure the quality of streaming video and audio using VMware Horizon virtual desktops. We'll present our research results showing the capabilities of the latest NVIDIA Pascal and Volta GPUs and NVIDIA GRID on VMware vSphere to accelerate the deep learning-based measurement task for our large-scale performance monitoring need. We will also cover the benefits of using NVIDIA GRID to improve the performance of our application without changing the number of GPUs in the system.
Video- and audio-based applications now comprise about 80 percent of Internet traffic, but their quality depends on the network condition. Cloud providers must accurately quantify and monitor video and audio quality so they can maintain the quality of these applications while optimizing cloud resource usage. We'll describe our solution, which uses deep neural networks to measure the quality of video and audio, and demonstrate how we measure the quality of streaming video and audio using VMware Horizon virtual desktops. We'll present our research results showing the capabilities of the latest NVIDIA Pascal and Volta GPUs and NVIDIA GRID on VMware vSphere to accelerate the deep learning-based measurement task for our large-scale performance monitoring need. We will also cover the benefits of using NVIDIA GRID to improve the performance of our application without changing the number of GPUs in the system.  Back
 
Topics:
Data Center & Cloud Infrastructure, AI Application, Deployment & Inference, Accelerated Data Science
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9435
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Abstract:
Enterprise Digital Workspaces support diverse workloads including virtual desktops, deep learning, big data. Nvidia GPUs bring high performance computing (HPC) for graphics, GPGPU, especially machine learning workloads. They also provide HW encode and decode to accelerate the processing of video contents. In this session, we will explore performance and resource utilization of various workloads that leverage different capabilities of GPU like graphics, compute and H.264 HW encode / decode. Nvidia virtualized GPUs and VMware vSphere brings in tremendous combined benefits for both GPU-based workloads and data center management via virtualization. We will present results of our research on running diverse workloads on vSphere platform using Nvidia GRID GPUs. We explore vSphere features of Suspend/Resume and vMotioning of vGPU based virtual machines. We will quantify benefits of vGPU for data center management using VMware vSphere and describe techniques for efficient management of workloads and datacenter resources.
Enterprise Digital Workspaces support diverse workloads including virtual desktops, deep learning, big data. Nvidia GPUs bring high performance computing (HPC) for graphics, GPGPU, especially machine learning workloads. They also provide HW encode and decode to accelerate the processing of video contents. In this session, we will explore performance and resource utilization of various workloads that leverage different capabilities of GPU like graphics, compute and H.264 HW encode / decode. Nvidia virtualized GPUs and VMware vSphere brings in tremendous combined benefits for both GPU-based workloads and data center management via virtualization. We will present results of our research on running diverse workloads on vSphere platform using Nvidia GRID GPUs. We explore vSphere features of Suspend/Resume and vMotioning of vGPU based virtual machines. We will quantify benefits of vGPU for data center management using VMware vSphere and describe techniques for efficient management of workloads and datacenter resources.  Back
 
Topics:
Data Center & Cloud Infrastructure, GPU Virtualization, HPC and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8250
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Abstract:

Learn why EVERY remote user should have GPU resources available to them. We'll discuss the advantages end-users experience once their virtual desktops/sessions have GPU capabilities. Recent data from the NVIDIA GRID Performance Engineering team shows a significant impact GPUs like the Tesla M10 has on knowledge workers. The data includes real user testing and scientific data like latency, bandwidth, and CPU utilization, which all play a significant role in the overall user experience.

Learn why EVERY remote user should have GPU resources available to them. We'll discuss the advantages end-users experience once their virtual desktops/sessions have GPU capabilities. Recent data from the NVIDIA GRID Performance Engineering team shows a significant impact GPUs like the Tesla M10 has on knowledge workers. The data includes real user testing and scientific data like latency, bandwidth, and CPU utilization, which all play a significant role in the overall user experience.

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Topics:
GPU Virtualization, Data Center & Cloud Infrastructure
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7181
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Abstract:
Efficient deployment of GPU-based machine learning, especially deep learning, in cloud environments is an important focus of research and development. As the leader in cloud infrastructure software, VMware provides multiple solutions that optimize performance and enhance flexibility for machine learning workloads. We'll present the results of our research on machine learning with NVIDIA GPUs on VMware's vSphere platform. Learn different ways to deploy GPU-based workloads developed with popular machine learning frameworks like TensorFlow and Torch in a virtualized environment using VMware DirectPath I/O and NVIDIA GRID vGPU solutions. We'll discuss how to mix workloads to maximize resource utilization and deployment flexibility by running machine learning together with other workloads on the same server. Finally, we'll analyze the performance characteristics of machine learning with GPUs for multiple use cases and at different scales in virtualized cloud data centers.
Efficient deployment of GPU-based machine learning, especially deep learning, in cloud environments is an important focus of research and development. As the leader in cloud infrastructure software, VMware provides multiple solutions that optimize performance and enhance flexibility for machine learning workloads. We'll present the results of our research on machine learning with NVIDIA GPUs on VMware's vSphere platform. Learn different ways to deploy GPU-based workloads developed with popular machine learning frameworks like TensorFlow and Torch in a virtualized environment using VMware DirectPath I/O and NVIDIA GRID vGPU solutions. We'll discuss how to mix workloads to maximize resource utilization and deployment flexibility by running machine learning together with other workloads on the same server. Finally, we'll analyze the performance characteristics of machine learning with GPUs for multiple use cases and at different scales in virtualized cloud data centers.  Back
 
Topics:
GPU Virtualization, Data Center & Cloud Infrastructure, Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7216
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