triton-ascend-case-matmul-swizzle2d

Optimize large-scale matrix multiplication on Ascend NPU by applying fixed-core execution and Swizzle2D tiling. This Triton-based kernel binds a fixed number of AI cores and reorders compute blocks to maximize data reuse and cache efficiency for very large matrices.

Swizzle2D tiling in Triton kernel optimization reorders compute blocks within groups to maximize data reuse and cache efficiency. It adapts block grouping direction row- or column-first based on M versus N dimensions to minimize redundant data loads.

Run a Triton kernel on Ascend NPU with fixed-core execution by binding a fixed number of AI cores, such as 20, and having each core loop over multiple blocks. This balances work across the hardware for stable, high-performance execution.

Use adaptive block grouping for high-throughput matrix multiplication when processing very large matrices with M and N in the millions. It chooses row- or column-first grouping based on M versus N to minimize redundant data loads and yield higher sustained throughput.

The Swizzle2D matmul optimization targets large-scale matrices with M and N in the millions. It is designed for high-throughput scenarios on Ascend NPU where fixed-core execution and adaptive block sizing can significantly improve cache locality.

Ascend NPU matmul kernels may have low throughput with large matrices due to poor cache locality and unbalanced work. Applying a fixed-core launch strategy with Swizzle2D tiling reorders compute blocks to maximize data reuse and balances work across active AI cores.