Write CUDA GEMM Kernel

A custom CUDA GEMM kernel implementation requires defining a tiling hierarchy, planning shared memory layouts for matrix blocks, and handling epilogue logic for operations like alpha*A*B + beta*C to maximize hardware utilization.

Writing a custom CUDA GEMM kernel is necessary for workloads requiring nonstandard memory layouts, fused epilogue operations, or constrained hardware scenarios where cuBLAS or CUTLASS do not fit exact project requirements.

Double buffering and shared memory improve GEMM performance by overlapping data loading with computation, reducing memory latency, and ensuring continuous thread execution during tiled matrix multiplication.

Tensor core eligibility for a CUDA GEMM kernel depends on checking target SM specifications and input dtypes, allowing you to determine if hardware acceleration applies to your FP32 accumulation workload.

Handling boundary conditions in a tiled CUDA GEMM kernel requires careful shared memory planning and explicit checks during matrix block loading to prevent out-of-bounds memory access when dimensions mismatch tile sizes.

The guided CUDA GEMM kernel design framework includes decision logic for handling batched or nonstandard layouts, ensuring correctness guarantees while maintaining performance across multiple matrix operations.