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GTC ON-DEMAND

Presentation
Media
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 and Cloud Infrastructure, AI Application Deployment and Inference, Accelerated Data Science
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9435
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Abstract:
You'll learn about enabling virtualized GPUs for machine learning workloads on VMware vSphere and combining GPU performance with data center management benefits of VMware vSphere. NVIDIA's Pascal GPU is the first GPU to offer both virtualized Compute/CUDA and virtualized Graphics. It supports multiple virtual machines (VM) sharing GPU for both compute and graphics capabilities. We will present our research results of machine learning workloads with vSphere platform using NVIDIA's virtualized GPUs. Learn different ways to deploy GPU-based workloads developed with popular machine learning frameworks like TensorFlow and Caffe using VMware DirectPathIO and NVIDIA vGPU solutions. We will discuss use cases to leverage best scheduling methods Equal Share, Fixed Share and Best Effort for virtualized GPUs and illustrate their benefits via our performance study. We address the scalability of machine learning workloads in term of the number of VMs per vSphere server and the number of GPUs per VM. Data center resource utilization of these workloads on vSphere with NVIDIA GPUs is also analyzed and presented.
You'll learn about enabling virtualized GPUs for machine learning workloads on VMware vSphere and combining GPU performance with data center management benefits of VMware vSphere. NVIDIA's Pascal GPU is the first GPU to offer both virtualized Compute/CUDA and virtualized Graphics. It supports multiple virtual machines (VM) sharing GPU for both compute and graphics capabilities. We will present our research results of machine learning workloads with vSphere platform using NVIDIA's virtualized GPUs. Learn different ways to deploy GPU-based workloads developed with popular machine learning frameworks like TensorFlow and Caffe using VMware DirectPathIO and NVIDIA vGPU solutions. We will discuss use cases to leverage best scheduling methods Equal Share, Fixed Share and Best Effort for virtualized GPUs and illustrate their benefits via our performance study. We address the scalability of machine learning workloads in term of the number of VMs per vSphere server and the number of GPUs per VM. Data center resource utilization of these workloads on vSphere with NVIDIA GPUs is also analyzed and presented.  Back
 
Topics:
GPU Virtualization, Performance Optimization, HPC and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8249
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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 and 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 and Cloud Infrastructure, Deep Learning and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7216
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Abstract:
We'll provide a technical deep dive into performance characteristics, application tuning options, as well as hardware and platform design considerations for administering 3D graphics-intensive applications in VMware Horizon environments. We'll review various application performance benchmark results from NVIDIA and VMware joint testing of NVIDIA GRID vGPUs (K2 and M60). These results will showcase scalability with different hardware and software configurations. We'll also discuss the considerations required for choosing the correct NVIDIA GRID vGPU profile to deliver a great user experience in VMware Horizon environments.
We'll provide a technical deep dive into performance characteristics, application tuning options, as well as hardware and platform design considerations for administering 3D graphics-intensive applications in VMware Horizon environments. We'll review various application performance benchmark results from NVIDIA and VMware joint testing of NVIDIA GRID vGPUs (K2 and M60). These results will showcase scalability with different hardware and software configurations. We'll also discuss the considerations required for choosing the correct NVIDIA GRID vGPU profile to deliver a great user experience in VMware Horizon environments.  Back
 
Topics:
GPU Virtualization, Performance Optimization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2016
Session ID:
S6595
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Abstract:
If you are looking for the guidance and the "tool" on how to do the scaling for the 3D workloads in Horizon 6 with View on NVidia Grid GPU, you have come to the right session. In this session, we provide a deep dive on the scale testing of various 3D workloads using View Planner 3.5 tool. View Planner 3.5 is a capacity planning tool that supports the real user workload including Office applications, video, audio, Interactive (mouse) tests and characterizes the true user experience for desktops and also has a feature of bring your own applications (BYOA). Using the BYOA feature of this tool, we show how you can quickly characterize your GRID GPU to get the scaling results for different 3D workloads and benchmarks while meeting the desired user experience.
If you are looking for the guidance and the "tool" on how to do the scaling for the 3D workloads in Horizon 6 with View on NVidia Grid GPU, you have come to the right session. In this session, we provide a deep dive on the scale testing of various 3D workloads using View Planner 3.5 tool. View Planner 3.5 is a capacity planning tool that supports the real user workload including Office applications, video, audio, Interactive (mouse) tests and characterizes the true user experience for desktops and also has a feature of bring your own applications (BYOA). Using the BYOA feature of this tool, we show how you can quickly characterize your GRID GPU to get the scaling results for different 3D workloads and benchmarks while meeting the desired user experience.  Back
 
Topics:
GPU Virtualization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2015
Session ID:
S5385
Streaming:
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