[P] CUDA scan kernels: hierarchical vs single-pass, decoupled lookbacks
A technical deep dive reveals how modern single-pass scan kernels achieve speed with decoupled lookbacks.
Deep Dive
Developer Shreyansh26 published a detailed analysis of efficient scan (prefix-sum) algorithms for NVIDIA GPUs. The post compares hierarchical and modern single-pass 'domino' approaches, explaining how decoupled lookbacks and warp-window optimizations prevent deadlock and improve coordination. It includes H100 benchmark timings and performance comparisons against NVIDIA's CUB library, providing concrete code and data for developers optimizing low-level GPU compute kernels.
Why It Matters
For AI engineers, faster scan operations can accelerate core tasks in model training, inference, and data preprocessing on GPUs.