triton-ascend-case-reduction-sum-large

To optimize large-scale 2D reduction latency in Triton-Ascend kernels, apply autotuning to tile sizes like BLOCK_SIZE_M and BLOCK_SIZE_N to balance compute and memory. This reorganizes computation to maximize UB occupancy and minimize reduction steps.

Use autotuning for Ascend reduction workloads when processing very large non-reduce axes alongside moderate reduce axes. This scenario benefits significantly from exploring different tile sizes to reduce iterations and improve overall kernel latency.

Compute-tiling optimizations are specifically applicable to 2D reductions with extremely large non-reduce axes and moderate reduce axes. This targeted reorganization maximizes UB occupancy and delivers significant latency improvements for these specific shapes.

Triton-Ascend kernels suffer from excessive reduction calls on small axes due to unoptimized tiling. Enforcing UB occupancy-aware tiling and autotuning BLOCK_SIZE_M and BLOCK_SIZE_N reorganizes the reduction to minimize these iterations.