triton-ascend-case-reduction-amin-large

What problem does it solve?

The optimized approach for extremely large-scale 1D amin reductions, using two-stage tiling and compute reorganization to minimize UB usage and reduce the total number of reduction steps, achieving near-peak performance on Ascend AI cores.

Core Features & Use Cases

• Two-stage tiling to prevent UB overflow and improve cache efficiency.
• Compute reorganization to minimize reduction steps and maximize throughput for 1D data with millions of elements.
• Autotune configurations to approach optimal grid settings (e.g., grid=32) and minimize latency on Ascend AI cores.
• Use Case: Efficiently reduce amin across 4 million-element vectors in downstream AI workloads.

Quick Start

Run the autotuned Triton kernel for amin reduction on a 4194304-element 1D array using the provided configs.