A binary runtime choice between two L1D prefetchers recovers most of the performance left by any fixed policy, with a small decision tree or bandit rule as the selector.
Micro-armed bandit: Lightweight and reusable reinforcement learning for microarchitecture decision-making,
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Beyond Static Policies: Dynamic Selection Among Modern Microarchitectural Policies
A binary runtime choice between two L1D prefetchers recovers most of the performance left by any fixed policy, with a small decision tree or bandit rule as the selector.