triton-ascend-fused-operator-optimization

Optimizing fused operators for Ascend NPU requires applying performance-ceiling analysis and multi-pass fusion strategies to generate a deterministic fusion plan based on operator graphs, input shapes, and data types.

The two-stage normalization decision workflow on Ascend NPU separates the normalization process into distinct statistic and normalize passes to optimize data access patterns and improve memory bandwidth utilization.

Improving memory bandwidth for elementwise and matmul activation fusion involves restructuring data access patterns within the operator graph to minimize overhead across varying data layouts on Ascend backends.

Fused operator optimization explicitly supports softmax and topk fusion scenarios, alongside elementwise, normalization, and matmul activation fusion, by evaluating native Ascend NPU operators and applying multi-pass strategies.

Generating a deterministic fusion plan requires providing the operator graph, input shapes, data types, and accuracy constraints to evaluate the performance ceiling and apply appropriate multi-pass merging strategies.

Multi-pass fusion strategies should be used when optimizing complex kernels like elementwise and normalization operations on Ascend NPU, where restructuring data access patterns can break through the performance ceiling.