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State depends on the optimization. For example, a linear (stride) predictor needs some history to recognize a pattern. Is that history state to be preserved over a process switch? Over an inner loop? Over a call? All such choices have gains, and losses, and costs. Probably process switch is rare enough that the state should simply be discarded. A level-ignorant predictor can catch stride patterns at all nesting levels, but has a limit to the number of tracked addresses that may be exceeded by a call. Giving each frame its own predictor avoids that, but then the predictor state needs to be saved/restored over calls. TANSTAAFL!