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GTC On-Demand

GPU Virtualization
Presentation
Media
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 Compu ...Read More
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.  Back
 
Topics:
GPU Virtualization, Performance Optimization, HPC and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8249
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