PG-MDP: Profile-Guided Memory Dependence Prediction for Area-Constrained Cores

2026-04-09

AI Summary

This work addresses the performance limitations of area-constrained small cores, where limited memory dependence predictor (MDP) capacity leads to excessive false dependencies and pipeline stalls. The authors propose a hardware-software co-design approach that leverages static opcode analysis to identify load instructions that are inherently memory-independent and bypasses them from MDP queries. This significantly reduces the effective working set of the predictor without increasing its capacity, area overhead, or instruction bandwidth. Evaluated on the SPEC2017 CPU intspeed suite, the technique reduces MDP queries by 79% and false dependencies by 77%, yielding an average IPC improvement of 1.47% in small cores—achieving performance comparable to that of a much larger predictor with 16× more entries.

Abstract

Memory Dependence Prediction (MDP) is a speculative technique to determine 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 proposes that targeting the predictor working set is as effective as growing the predictor, and can deliver performance competitive with large predictors while still using very small predictors. This paper introduces profile-guided memory dependence prediction (PG-MDP), a 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 when dispatched and always issue as soon as possible. Across SPEC2017 CPU intspeed, PG-MDP reduces the rate of MDP queries by 79%, false dependencies by 77%, and improves geomean IPC for a small simulated core by 1.47% (to within 0.5% of using 16x the predictor entries), with no area cost and no additional instruction bandwidth.

Problem

• Memory Dependence Prediction
• Area-constrained cores
• False dependencies
• Alias conflict
• Speculative execution

Innovation

• Memory Dependence Prediction
• Profile-Guided Optimization
• Area-Constrained Cores
• Speculative Execution
• Hardware-Software Co-Design