Optimize Triton Block Parameters

Tune Triton block sizes by systematically searching BLOCK_M, BLOCK_N, and BLOCK_K configurations to maximize GEMM kernel throughput. The autotune workflow benchmarks representative shapes against device peak to determine the optimal configuration.

Configure BLOCK_M, BLOCK_N, BLOCK_K alongside num_warps and num_stages for GEMM-style kernels. Ensure the autotune key includes all shape dimensions affecting tile geometry and shared memory usage to preserve correctness across instances.

Yes, the autotuning workflow supports hardware targets including A100, H100, and RTX GPUs. It systematically evaluates block sizes and parallelism configurations to improve performance across these specific hardware architectures.

The best way is running a reproducible benchmark-driven workflow that tests num_warps and num_stages combinations alongside block sizes. It documents the winning configuration and expected throughput against device peak efficiency.

Triton autotune fails when the search key omits shape dimensions that affect tile geometry and smem usage. Include all relevant problem dimensions in the autotune key to ensure shape- and dtype-aware configuration that preserves efficiency.