triton-ascend-case-matmul-swizzle2d

To improve cache locality for Ascend matrix multiplication, apply Swizzle2D grouping. This reorders blocks at the group level to maximize data reuse and increase cache hits across processing cores.

Large dense matmul on Ascend suffers from load imbalance when launching many blocks. Fixed-core startup fixes this by assigning a grid of cores to process multiple blocks, balancing computation across the hardware.

Swizzle2D grouping targets large dense matmul workloads on Ascend hardware. It is designed for contexts where kernel scheduling and data reuse dominate performance, making it unsuitable for small matrix operations.

The best way to tune block sizes for Ascend matmul kernels is to select BLOCK_M, BLOCK_K, and BLOCK_N based on your specific data type and cache size. This block-size tuning ensures optimal throughput.

Adaptive grouping direction works by automatically choosing row- or column-first grouping based on the M vs N dimensions of your A[M,K] x B[K,N] matrices. This ensures balanced computation and optimal cache locality.