When working with tile‑level GEMM kernels you often hit three practical roadblocks: shared‑memory limits, schedule sensitivity, and numerical drift. The tilelang example shows how a tiled tensor‑core matmul can be expressed in just a few lines, but getting it to run fast and correctly on real hardware requires a disciplined tuning loop.
First, calculate the shared‑memory footprint before launching the kernel. The helper smem_bytes tells you exactly how much memory a given (block_M, block_N, block_K, stages) configuration will consume. If the value exceeds the device’s SMEM_CAP, reduce num_stages or shrink one of the tile dimensions until the fit is safe. This prevents illegal memory allocation errors that would otherwise abort your program.
Second, treat the schedule knobs as experiments, not fixed constants. Vary block_M, block_N, block_K, num_stages, thread count, and the use_swizzle flag in a small grid. For each candidate, launch the kernel, compare the output to a reference a @ b using a relative tolerance (e.g., 2e‑2), and measure elapsed time with a benchmark wrapper. Discard any configuration that fails the numeric check or exceeds the shared‑memory budget. Keep the fastest valid run; the “winner” will be architecture‑ and problem‑size dependent, which is why a sweep (as shown in section 3) is essential.
Third, verify that the emitted PTX/Sass actually uses the expected tensor‑core instructions. Search the kernel source for patterns like mma.sync, wgmma, ldmatrix, cp.async, or tl::gemm. Their presence confirms that the compiler mapped your tiled GEMM to the hardware matrix‑multiply units; absence usually means you need to adjust tile sizes or enable swizzling to expose the required memory‑access patterns.
By following this three‑step workflow—memory‑budget pre‑check, systematic schedule sweep with numeric validation, and instruction‑level verification—you turn a concise tilelang prototype into a reliable, high‑performance GEMM routine that matches or exceeds cuBLAS on your target GPU. #AI #Product #HPC #GPU #DeepLearning #Performance