triton-ascend-case-reduction-sum-fused

Optimize reduction and elementwise fusion on Ascend by reorganizing compute to perform elementwise work first and then reduction using Triton, which improves kernel fusion efficiency and overall hardware throughput.

Improve cache locality during row-wise tiling by applying SUB_BLOCK partitioning to reorganize compute, which enhances cache utilization and creates better kernel fusion opportunities on Ascend backends.

Autotune grid and block sizes for Triton kernels on Atlas A2/A3 by exploring combinations of grid and block configurations to identify optimal performance settings across different input shapes.

Yes, the reduction-first fusion workflow supports different input shapes on Ascend by utilizing autotune configurations to explore and identify optimal grid and block settings for each specific shape.

Start running the Triton Ascend reduction and elementwise fusion workflow by executing it with default grid=40 and SUB_BLOCK settings to observe the optimized performance baseline.