Mercury: Unlocking Multi-GPU Operator Optimization for LLMs via Remote Memory Scheduling

In this paper, we propose Mercury, a multi-GPU operator compiler based on a loop-based intermediate representation, CommIR. At the core of Mercury is an abstraction that treats remote GPU memory as an explicitly managed extension of the memory hierarchy, expanding the available storage and communication resources beyond local HBM. This unified view enables the compiler to reason holistically about data placement and inter-device communication, unlocking a vastly larger design space that encompasses and extends beyond existing manual strategies. As a result, Mercury is able to automatically reproduce the performance of hand-optimized baselines like RingAttention and Ulysses, and in some configurations, even discovers more effective strategies that manual designs have overlooked. Our implementation is open-sourced at https://github.com/ChandlerGuan/mercury_artifact.