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1101: GRID 技術セッション 2:vGPU Sizing

2015年9月18日開催 GTC Japan 2015 講演資料

エヌビディア合同会社
エンタープライズプロダクト事業部 シニアソリューションアーキテクト Jeremy Main

A walk through of the techniques to monitor existing workstation workloads to create data-driven estimates of recommended user density levels based on the GPU requirements, frame buffer utilization and other factors as well as methods to confirm GPU resource utilization to ensure excellent performing NVIDIA GRID vGPU enabled virtual machines.

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1101: GRID 技術セッション 2:vGPU Sizing

  1. 1. Jeremy Main シニアソリューションアーキテクト GRID GRID Technical Session vGPU Sizing
  2. 2. First Considerations Understand the existing non-VDI environment and workloads Workstation model, CPU generation, CPU speed, memory, storage GPU(s) used, how it was selected or upgraded Applications used, number of displays, special input devices Capture system and GPU performance data and review with user Provide a data-driven recommendation to gain acceptance Segment users GPU requirements and size appropriately
  3. 3. First Considerations Server-rendered FPS vs. remotely delivered FPS Define the per-user network bandwidth requirements Explain the impact of 30 FPS vs. 60 FPS delivered framerates Start with a less dense vGPU profile and access user performance Increase density until performance does not meet acceptance tests Require acceptance testing with real workloads not benchmarks Log and monitor GPU utilization on host Agree on metrics, don’t use subjective criteria…
  4. 4. Understanding your Applications CPU, memory and storage requirements GPU rendering and frame buffer requirements “perfmon” on existing workstation or GPU pass-through VM Memory -> Available MBytes Processor -> % Processor Time NVIDIA_GPU % GPU Usage Total Memory (MB) Available Memory (MB)
  5. 5. Catia V5-6R2012 K5000 Try to limit the number of slides you use Keep text to a minimum Instead, speak more to your audience (engage them with anecdotes/enthusiasm/eye contact) Try not to read your points verbatim; bullet points should be used for key points only Use images/graphics to help convey your message
  6. 6. Catia V5-6R2012 K5000 Application not using all of the CPU cores GPU utilization is low GPU memory use is low 1GB vGPU profile
  7. 7. Catia V5-6R2012 K600 Application not using all of the CPU cores GPU utilization is low GPU memory use is low 1GB vGPU profile
  8. 8. Siemens NX 10 K5000
  9. 9. Siemens NX 10 K5000 Application using more of the CPU cores in some operations GPU utilization is low GPU memory use is low 1GB vGPU profile
  10. 10. Siemens NX 10 K600 Application using more of the CPU cores in some operations GPU utilization is low GPU memory use is low 1GB vGPU profile
  11. 11. SPECviewperf12 K5000
  12. 12. GPU Utilization 0 50 100 % CPU % GPU Benchmarks Push GPU capabilities to their limits and may be heavily dependent on single thread performance Applications Modeling operations and view manipulation consist of periods of activity and inactivity 0 50 100 % CPU % GPU
  13. 13. Monitoring vGPU Within the VM “NVIDIA_GPU” counter, “% GPU Usage” is not supported Monitor at hypervisor-level using nvidia-smi command $ nvidia-smi --query-gpu=¥ timestamp,name,pci.bus_id,driver_version,pstate,¥ pcie.link.gen.max,pcie.link.gen.current,temperature.gpu,¥ utilization.gpu,utilization.memory,memory.total,¥ memory.free,memory.used --format=csv -l 5 Prepend with “timeout –t” with the number of seconds to run
  14. 14. Monitoring protocol HDX3D-Pro FPS wmic /NameSpace:¥¥root¥citrix¥hdx Path Citrix_VirtualChannel_Thinwire_Enum Get Component_Fps /every:5 Verify consistent frame rate delivery based on XenDesktop policy parameters
  15. 15. Monitoring protocol PCoIP FPS Perfmon: PCoIP Session Imaging Statistics -> Imaging Encoded Frames/Sec

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