triton-ascend-case-reduction-mean-medium

What problem does it solve?

This Skill targets optimizing the mean reduction on the first axis for medium-scale 2D tensors using Triton-Ascend, reducing the number of intermediate reductions and improving latency with grid-size tuning.

Core Features & Use Cases

• Axis-first reduction optimization: recomposes computation to minimize intermediate reductions and leverage optimal grids (e.g., grid=32) to reach best latency.
• Autotune guidance: provides grid configurations and benchmarking notes aligned with Atlas A2/A3 hardware.
• Use Case: apply to 2D reductions where both axes are medium-sized (millions of elements) to maximize throughput.

Quick Start

Run the Triton-Ascend mean-reduction case on a medium-scale 2D tensor to observe improved throughput.