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

TL;DR

This paper introduces PG-MDP, a profile-guided approach that reduces false dependencies and improves performance in area-constrained cores by efficiently identifying memory independent loads.

Contribution

It proposes a software co-design method to label and bypass memory independent loads, achieving large predictor effectiveness with minimal area overhead.

Findings

Reduces MDP queries by 79%

Decreases false dependencies by 77%

Improves IPC by 1.47% on SPEC2017 CPU

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.