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Decision models offer fast choices over sup- plied alternatives, while general-purpose large language models interpret open-ended tasks through flexible generation and reasoning at the cost of sequential decoding. Many work- loads expect a generative backend to determine what can be done and therefore provide no well-d...
This study describes GPU-fabric contention in LLM workloads and presents GPUWeaver, a context-aware communication scheduler that monitors application progress and fabric state, identifies contention, and dynamically regulates communication injection according to application performance goals.
Preliminary but compelling evidence that MC-aware scheduling is a practical operating system/runtime direction for deployable embodied AI on unified memory edge devices is provided.
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