Crafting Efficient Kernels with Epilogue Fusion

Epilogue fusion is a technique used in machine learning workloads to reduce global memory traffic by performing additional operations, such as bias addition, activation functions, and type conversion, directly on the accumulator fragments of a General Matrix Multiplication (GEMM) before storing the result to global memory. This approach, particularly effective on platforms like Hopper and Blackwell, allows for the fusion of operations that are elementwise and independent, eliminating the need for intermediate memory reads and writes, which are often the most costly aspect of modern workloads. The blog post explains how CUTLASS facilitates epilogue fusion by integrating these operations into the GEMM epilogue, enhancing efficiency without compromising computational accuracy. It provides examples of prebuilt operations and demonstrates the creation of custom epilogues, such as for the gated-SiLU pattern, which further optimizes performance by reducing output dimensions and memory footprint. The text emphasizes that epilogue fusion enables faster execution by integrating additional computations into the GEMM process itself, thus avoiding redundant data transfers and maintaining high-quality results.