Choose Tile Size and Work Partitioning

To choose tile sizes for CUDA or Triton kernels, balance shared memory use, register pressure, and occupancy against your problem shape and hardware limits to determine viable BLOCK_M, BLOCK_N, and BLOCK_K dimensions.

The best work partitioning strategy for a tiled GPU kernel applies a grid layout tailored to your problem dimensions, explicitly stating block configurations to maximize throughput while avoiding shared memory overruns.

Triton kernels face low occupancy with irregular shapes when tile sizes cause high register pressure or exceed shared memory budgets, requiring a structured evaluation of hardware constraints to rebalance the configuration.

You can autotune tile sizes when multiple viable configurations exist by generating an optional autotuning plan that evaluates different launch configurations against your kernel's specific hardware limits.

Balancing shared memory and registers for GEMM-like tiling requires evaluating hardware smem budgets and register pressure to select explicit tile sizes that maximize occupancy without exceeding memory limits.