tilelang-api-best-practices

Ascend NPU kernel optimization requires allocating shared/fragment buffers, using copy semantics to move data into L1/L0, and applying T.Pipelined or T.Persistent scheduling for multi-core coordination and prefetching.

The correct way to use memory allocation primitives is to define a prim_func, allocate shared and fragment buffers appropriately, and manage data copy semantics to move tensor blocks into L1/L0 memory levels before invoking compute operations.

Implement cross-core synchronization by using barrier and cross-core sync primitives provided by the TileLang Ascend API. These primitives enable safe multi-core coordination alongside T.Persistent scheduling patterns for concurrent kernel execution.

Yes, you can use T.gemm_v0 or T.mma to implement high-performance GEMM operations. You must apply correct init semantics for matrix multiplication and ensure data is prefetched into the correct memory hierarchy levels beforehand.

The best way to debug and profile Ascend NPU kernels is using device-side printf, dumping tensors for validation, and leveraging msProf profiling alongside pass_config tuning for memory planning and auto-sync optimization.

Yes, the TileLang Ascend API supports element-wise operations, reductions, and tile-level vector operations. It provides structured compute primitives that integrate with kernel definition decorators for correct and efficient hardware-specific tile execution.