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Computer Vision 3D Flow and Function AI with LiDAR
Introduction Solution overview Partner overview Architecture overview Sizing and scaling
Sizing and scaling
Sizing the solution
GPU requirements Sizing approach for LiDAR Sizing the SENSR nodes Storage sizing Network sizing
Scaling the solution
Adding LiDAR processing Future considerations
Use case validation Conclusions References

Scaling the solution

Home > Workload Solutions > Computer Vision > White Papers > Computer Vision 3D Flow and Function AI with LiDAR > Scaling the solution

Scaling the solution

  • Adding LiDAR processing

    During testing we processed 4 LiDAR streams with a combined ~4.8 million points per second of point cloud data using a single Algo processing node VM configured on the VxRail. The VM was given access to one quarter of the resources from an A16 GPU to provide enhanced object detection and higher performance.

    Each additional VxRail node added to the cluster can support up to 8 additional Algo/Main VMs. There are many ways to configure the additional capacity to support combinations of Main and Algo nodes. We found that 1 Main node could manage the traffic for 3 Algo nodes processing 12 data streams reliably. This configuration ratio would result in 2 new SENSR clusters (1 x Main, 3 x Algo) that support up to 24 additional LiDAR streams of 1.2 million points per second.