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S9814 HOW GPU-ACCELERATED VIRTUAL WORKSTATIONS ENABLE MOBILITY AND - PowerPoint PPT Presentation

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S9814 HOW GPU-ACCELERATED VIRTUAL WORKSTATIONS ENABLE MOBILITY AND COLLABORATION FOR AUTODESK APPLICATIONS WHO ARE WE? Jimmy Rotella Andrew Schilling Jeremy Stroebel Sr. Solutions Architect Chief Infrastructure Officer Director of IT

  1. S9814 – HOW GPU-ACCELERATED VIRTUAL WORKSTATIONS ENABLE MOBILITY AND COLLABORATION FOR AUTODESK APPLICATIONS

  2. WHO ARE WE? Jimmy Rotella Andrew Schilling Jeremy Stroebel Sr. Solutions Architect Chief Infrastructure Officer Director of IT Nvidia CannonDesign Browning Day Mullins Dierdof Architects 2

  3. CANNONDESIGN SFMO - “Single Firm, Multi Office” • 1,100 employees • 19 locations • Named to Fast Company’s 2019 Most Innovative Companies List • #4 Position in 2018 Architect 50 Rankings • #6 Global Health Firm 3

  4. BROWNING DAY MULLINS DIERDORF ARCHITECTS Indianapolis, IN • 55+ employees • 1 office (Indianapolis, IN) • Architecture, Landscape Architecture, Interior Design, and Planning Firm • Founded in 1967 • Won over 150 design awards locally and nationally 4

  5. CONFIGURATIONS AT A GLANCE CannonDesign BDMD Host Platform Cisco B200 M4 Blade Servers Dell R740 servers Nvidia GPU 1x Tesla P6 per host 4-5x Tesla P4s per host Hypervisor Stack Vmware Horizon on ESXi XenDesktop on XenServer Quadro Virtual Data Center Quadro Virtual Data Center Nvidia Virtual GPU Software Workstation Workstation Nvidia vGPU profiles 1gb, 2gb, 4gb 2 GB (P4-2Q) CPU 2-12 vCPU @ 2.6 GHz per guest 4 vCPU @ 3.0 GHz per guest RAM 8GB – 48GB per guest 12-16 GB per guest Storage 256GB – 512GB on NetApp SSD Dell Compellent SC4020 Hybrid 5

  6. WHO ARE WE? Jimmy Rotella Andrew Schilling Jeremy Stroebel Sr. Solutions Architect Chief Infrastructure Officer Director of IT Nvidia CannonDesign Browning Day Mullins Dierdof Architects 6

  7. GPU PROFILER https://github.com/JeremyMain/GPUProfiler/releases • CPU Utilization • GPU Utilization • Frame Buffer Utilization • RAM Utilization • Video Encode • Video Decode 7

  8. NVIDIA TESLA DATA CENTER GPU HARDWARE Maxwell, Pascal, Volta & Turing GPUs supported M10 T4 P4 P40 P6 V100 GPU 4 NVIDIA Maxwell GPUs 1 NVIDIA Turing GPU 1 NVIDIA Pascal GPU 1 NVIDIA Pascal GPU 1 NVIDIA Pascal GPU 1 NVIDIA Volta GPU 2,560 CUDA Cores 2,560 2,560 3,840 2,048 5,120 (640 per GPU) 32 GB GDDR5 Memory Size 16 GB GDDR6 8 GB GDDR5 24 GB GDDR5 16 GB GDDR5 32 GB HBM2 (8 GB per GPU) Max vGPU instances 32 16 8 24* 16 32* 1 GB, 2 GB, 1 GB, 2 GB, 1 GB, 2 GB, (1 GB, 2 GB) 3 GB, 4 GB, 1 GB, 2 GB, 4 GB, (1 GB, 2 GB) 4 GB, vGPU Profiles 4 GB, 8 GB 4 GB, 8 GB, 16 GB 4 GB, 8 GB 6 GB, 8 GB, 12 GB, 24 GB 8 GB, 16 GB 8 GB, 16 GB, 32 GB PCIe 3.0 Dual Slot PCIe 3.0 Single Slot PCIe 3.0 Single Slot PCIe 3.0 Dual Slot MXM PCIe 3.0 Dual Slot Form Factor (rack servers) (rack servers) (rack servers) (rack servers) (blade servers) (rack servers) Max Power 225W 70W 75W 250W 90W 250W Thermal passive passive passive passive bare board passive USER DENSITY PERFORMANCE BLADE MULTI-USE * mileage will vary Optimized Optimized Optimized Optimized 8

  9. NVIDIA T4 FOR VIRTUALIZATION Powering 3D Professional Virtual Workstations GPU Architecture Turing CUDA Cores 2,560 Tensor Cores 320 Memory Size 16 GB 1 GB, 2 GB, 4 GB, 8 vGPU Profiles GB, 16 GB Form Factor PCIe 3.0 single slot Power 70 W Thermal Passive 9

  10. Jimmy Rotella jrotella@nvidia.com Andrew Schilling aschilling@cannondesign.com Jeremy Stroebel jstroebel@bdmd.com

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