triton-ascend-case-elemwise-broadcast-3d

Optimize cross-axis 3D broadcast operations by applying a two-stage kernel strategy that expands the broadcast, reshapes the tensor to 2D, and then performs standard multi-core computation. This approach significantly improves poor vectorization seen in tiny last dimensions.

3D broadcast operations suffer from poor vectorization when the last dimension is very small. A two-stage kernel strategy resolves this by performing a parallel broadcast expansion and reshaping the data to 2D to enable better multi-core processing.

The best way to handle cross-axis broadcast across non-adjacent axes is using a two-stage kernel strategy that targets the large first dimension with parallel broadcast expansion, then reshapes the tensor to 2D for standard multi-core computation.

Ensure numerical correctness during two-stage 3D broadcast by expanding the parallel broadcast and reshaping to 2D while maintaining strict compatibility with existing kernels across the B, H, and W dimensions.

Yes, this two-stage kernel strategy specifically optimizes 3D tensor operations with shapes like (B, H, W) and (1, H, 1) by reshaping them to 2D to boost multi-core processing performance on Ascend backends.