# MCFuser: High-Performance and Rapid Fusion of Memory-Bound Compute-Intensive Operators

arXiv: https://arxiv.org/abs/2506.22169 · HTML: https://mlsanity.com/paper/2506.22169/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2025.

## Built on by (most cited first, 2 of 2)

- [Ada-MK: Adaptive MegaKernel Optimization via Automated DAG-based Search for LLM Inference](https://mlsanity.com/paper/2605.11581/) (2026)
- [RedFuser: An Automatic Operator Fusion Framework for Cascaded Reductions on AI Accelerators](https://mlsanity.com/paper/2603.10026/) (2026)

## Abstract (arXiv)

Operator fusion, a key technique to improve data locality and alleviate GPU memory bandwidth pressure, often fails to extend to the fusion of multiple compute-intensive operators due to saturated computation throughput. However, the dynamicity of tensor dimension sizes could potentially lead to these operators becoming memory-bound, necessitating the generation of fused kernels, a task hindered by limited search spaces for fusion strategies, redundant memory access, and prolonged tuning time, leading to sub-optimal performance and inefficient deployment. We introduce MCFuser, a pioneering framework designed to overcome these obstacles by generating high-performance fused kernels for what we define as memory-bound compute-intensive (MBCI) operator chains. Leveraging high-level tiling expressions to delineate a comprehensive search space, coupled with Directed Acyclic Graph (DAG) analysis to eliminate redundant memory accesses, MCFuser streamlines kernel optimization. By implementing guidelines to prune the search space and incorporating an analytical performance model with a heuristic search, MCFuser not only significantly accelerates the tuning process but also demonstrates superior performance. Benchmarked against leading compilers like Ansor on NVIDIA A100 and RTX3080 GPUs, MCFuser achieves up to a 5.9x speedup in kernel performance and outpaces other baselines while reducing tuning time by over 70-fold, showcasing its agility.