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GTC ON-DEMAND

Presentation
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Abstract:
End-to-end machine learning workloads perform well using NVIDIA virtual GPUs in VMware vSphere. We'll discuss how to combine the performance of NVIDIA GPUs with manageability and scalability features and maximize GPU utilization for machine learning workloads using VMware and NVIDIA technology. We will outline end-to-end machine learning, including training, deploying for inferencing, and managing a production environment using VMware vSphere and VMware's Pivotal Kubernetes Service. NVIDIA Turing architecture is positioned for mixed-precision training and inferencing workloads. We'll describe ways to deploy GPU-Based workloads developed with machine learning frameworks like TensorFlow and Caffe2 by using VMware DirectPathIO and NVIDIA virtual GPU (vGPU). We'll also provide case studies that leverage vGPU scheduling options such as Equal Share, Fixed Share, and Best Effort, and illustrate their benefits with our performance study.
End-to-end machine learning workloads perform well using NVIDIA virtual GPUs in VMware vSphere. We'll discuss how to combine the performance of NVIDIA GPUs with manageability and scalability features and maximize GPU utilization for machine learning workloads using VMware and NVIDIA technology. We will outline end-to-end machine learning, including training, deploying for inferencing, and managing a production environment using VMware vSphere and VMware's Pivotal Kubernetes Service. NVIDIA Turing architecture is positioned for mixed-precision training and inferencing workloads. We'll describe ways to deploy GPU-Based workloads developed with machine learning frameworks like TensorFlow and Caffe2 by using VMware DirectPathIO and NVIDIA virtual GPU (vGPU). We'll also provide case studies that leverage vGPU scheduling options such as Equal Share, Fixed Share, and Best Effort, and illustrate their benefits with our performance study.  Back
 
Topics:
GPU Virtualization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9815
Streaming:
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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
Streaming:
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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
Streaming:
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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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