triton-ascend-case-matmul-large-k

Use Split-K parallelization: add the K-segment index as an extra grid dimension so multiple cores compute partial results for the same output block. Accumulate partials with tl.atomic_add, or store them to a workspace and reduce externally with torch.sum.

Size the grid so total blocks approach or exceed the core count. For large-K shapes, put SPLIT_K in the grid (e.g., grid = (NUM_MN_BLOCKS, SPLIT_K)) so each core handles a K-range slice instead of leaving cores idle.

tl.debug_barrier forces all cores to wait at one point, serializing CUBE computation and VEC reduction because Triton lacks the AscendC AIC/AIV hardware parallel path. Writing partials to a workspace and reducing with torch.sum externally is over 2x faster.

Yes, the technique targets Ascend backends including Atlas A2, Atlas A3, and Atlas A5 using the Triton Ascend DSL. Grid sizing should be tuned to the actual AI core count of the specific device.

Larger SPLIT_K raises parallelism and core utilization but increases tl.atomic_add contention when accumulating into C. The workspace-plus-reduce variant avoids atomic contention but requires allocating workspace memory sized to hold all partial results.