Memory Dependence Prediction (MDP) is a speculative technique to predict which stores, if any, a given load will depend on. Area-constrained cores are increasingly relevant in various applications such as energy-efficient or edge systems, and often have limited space for MDP tables. This leads to a high rate of false dependencies as memory independent loads alias with unrelated predictor entries, causing unnecessary stalls in the processor pipeline. The conventional way to address this problem is with greater predictor size or complexity, but this is unattractive on area-constrained cores. This paper demonstrates that targeting the predictor working set delivers the majority of available performance without scaling any hardware structures. We achieve this with profile-guided memory dependence prediction (PG-MDP), a hardware-software co-design to label consistently memory independent loads via their opcode and remove them from the MDP working set. These loads bypass querying the MDP and always issue as soon as possible. In the event that a labeled load incorrectly passes a store to the same address, a rollback is triggered as usual but no new MDP entry is created. Across the SPECspeed 2017 suites, PG-MDP reduces MDP load queries by 80%, false dependencies by 84%, and improves geomean IPC for a small (ROB=128) simulated core by 4.6% (to within 1.2% of the IPC when using 8x more predictor entries), with no area cost and no additional instruction bandwidth.

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