triton-ascend-case-reduction-mean-large

Optimize large-scale mean reductions on Ascend by applying row-wise tiling to reduce thread blocks and in-kernel sub-blocking to avoid cache overflows. This approach targets 2D tensors where the reducing axis dominates.

Yes, Triton autotune works for 2D reduction kernels on Atlas A2/A3 by exploring configurations for BLOCK_SIZE_M, SUB_BLOCK_SIZE_M, and BLOCK_SIZE_N to find optimal grid settings for peak performance.

UB overflows during large 2D mean reductions occur when in-kernel data exceeds buffer limits. Applying in-kernel sub-blocking partitions the reduction data to avoid cache and buffer overflows, ensuring stable performance.

The best block size parameters for Triton mean reduction on Ascend are BLOCK_SIZE_M, SUB_BLOCK_SIZE_M, and BLOCK_SIZE_N. These must be tuned via autotune to match your specific input shapes and grid sizes.

Use row-wise tiling for mean reduction on Ascend when processing 2D tensors with a moderate non-reducing axis and a large reducing axis. This technique improves cache locality and reduces total kernel thread blocks.