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GTC ON-DEMAND

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Abstract:
Universities have increasing demand for Deep Learning/AI classrooms or labs but are constrained by cost and availability of physical classroom labs. Students require access to a lab 24x7 to work on projects and assignments and find that they have to wait for HPC clusters to be free when submitting their jobs for training. In the past, students and researchers are tethered and require expensive data scientist workstations. Virtual GPUs provide a highly secure, flexible, accessible solution to power AI and deep learning coursework and research. Learn how Nanjing University is using virtual vGPUs with NGC for teaching AI and Deep learning courses, empowering researchers with the GPU power they need, and providing students with mobility to do coursework anywhere. Similarly, discover how other universities are maximizing their data center resources by running VDI, HPC and AI workloads on common infrastructure and even how companies like Esri are using virtualized deep learning classes to educate their user base. Discover the benefits of vGPUs for AI and how you can setup your environment to achieve optimum performance, as well as the tools you can use to manage and monitor your environment as you scale.
Universities have increasing demand for Deep Learning/AI classrooms or labs but are constrained by cost and availability of physical classroom labs. Students require access to a lab 24x7 to work on projects and assignments and find that they have to wait for HPC clusters to be free when submitting their jobs for training. In the past, students and researchers are tethered and require expensive data scientist workstations. Virtual GPUs provide a highly secure, flexible, accessible solution to power AI and deep learning coursework and research. Learn how Nanjing University is using virtual vGPUs with NGC for teaching AI and Deep learning courses, empowering researchers with the GPU power they need, and providing students with mobility to do coursework anywhere. Similarly, discover how other universities are maximizing their data center resources by running VDI, HPC and AI workloads on common infrastructure and even how companies like Esri are using virtualized deep learning classes to educate their user base. Discover the benefits of vGPUs for AI and how you can setup your environment to achieve optimum performance, as well as the tools you can use to manage and monitor your environment as you scale.  Back
 
Topics:
GPU Virtualization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9888
Streaming:
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Abstract:
Esri is currently developing it's new Desktop GIS application, ArcGIS Pro, which uses DirectX and OpenGL rendering libraries for visualization of 2D and 3D spatial and raster data. We foresee heavy use of ArcGIS Pro in virtualized environments that are configured to use the NVIDIA GRID. This presentation will cover our test approach in architecture and methodology, including how we used Visual Studio load tests as well as developed a lightweight add-in, and the lessons learned along the way. Further discussion points include required test metrics and how to acquire those metrics, also GPU monitoring tools used, NVIDIA-SMI, GPU-Z, and internally generated metrics.
Esri is currently developing it's new Desktop GIS application, ArcGIS Pro, which uses DirectX and OpenGL rendering libraries for visualization of 2D and 3D spatial and raster data. We foresee heavy use of ArcGIS Pro in virtualized environments that are configured to use the NVIDIA GRID. This presentation will cover our test approach in architecture and methodology, including how we used Visual Studio load tests as well as developed a lightweight add-in, and the lessons learned along the way. Further discussion points include required test metrics and how to acquire those metrics, also GPU monitoring tools used, NVIDIA-SMI, GPU-Z, and internally generated metrics.  Back
 
Topics:
GPU Virtualization, Performance Optimization1
Type:
Talk
Event:
GTC Silicon Valley
Year:
2015
Session ID:
S5493
Streaming:
Share:
 
 
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