triton-ascend-elementwise-reduce-fused

What problem does it solve?

Fusion of per-element transformations and global reductions into a single kernel to avoid intermediate memory reads/writes, boosting performance on Triton-Ascend pipelines.

Core Features & Use Cases

• Single-kernel fusion combines elementwise computations with reduction to minimize memory traffic.
• Cross-block aggregation uses atomic operations to accumulate results efficiently.
• Use Case: accelerate common loss functions and elementwise-then-reduce operators in neural network workloads on Ascend hardware.

Quick Start

Run a fused elementwise-reduce kernel on your tensors to observe end-to-end performance gains.