Accelerating BEV Pooling on NVIDIA GPUs for Physical AI Applications | NVIDIA Technical Blog

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Introduces BEVPoolV3 with four key algorithmic changes: reduced duplicate depth loads, a five-array INT32 scatter map, precomputed indices eliminating runtime integer division, and interval-owned output writes. Details a practical workflow for optimizing BEV pooling operators: classifying the working set by L2 fit, eliminating redundant scatter traffic, mapping kernel implementation to the target NVIDIA GPU architecture, and validating bottlenecks with NVIDIA Nsight Compute. Demonstrates up to 22x speedup (FP16) on DRAM-bound paths and up to 42x speedup (FP8) on L2-resident paths over BEVPoolV2 on NVIDIA RTX GPUs.