triton-ascend-case-reduction-mean-large

Optimize large-scale mean reduction in Triton on Ascend by applying row-wise secondary partitioning to reduce thread blocks per core and avoid UB. This technique improves cache hits and bandwidth utilization for 2D reduction scenarios.

You need row-wise secondary partitioning for 2D reduction when your non-reduce axis is moderate and your reduce axis is large. This configuration allows grid sizes to match core counts and significantly boosts reduction performance.

Autotune grid configuration affects Triton kernel optimization on Ascend by exploring different BLOCK_SIZE_M and SUB_BLOCK_SIZE_M combinations under a grid=40 setup. This explicit tuning discovers the optimal partitioning strategy for maximum performance.

This Triton Ascend mean reduction approach is not suited for small reduce axes. It is designed specifically for 2D reductions with moderate non-reduce axes and large reduce axes where grid equals the number of cores.

The best way to start benchmarking a Triton mean reduction kernel is to replace your existing reduction implementation with this row-wise secondary partitioning version. You can then immediately run baseline evaluations to compare performance.

Large-scale mean reduction hits UB limitations on Ascend due to excessive thread blocks per core. Performing secondary partitioning inside the kernel avoids this bottleneck and improves bandwidth utilization.