Load Value Prediction via Path-based Address Prediction: Avoiding Mispredictions due to Conflicting Stores

Current flagship processors excel at extracting instruction-level-parallelism (ILP) by forming large instruction windows. Even then, extracting ILP is inherently limited by true data dependencies. Value prediction was proposed to address this limitation. Many challenges face value prediction, in this work we focus on two of them. Challenge #1: store instructions change the values in memory, rendering the values in the value predictor stale, and resulting in value mispredictions and a retraining penalty. Challenge #2: value mispredictions trigger costly pipeline flushes. To minimize the number of pipeline flushes, value predictors employ stringent, yet necessary, high confidence requirements to guarantee high prediction accuracy. Such requirements can negatively impact training time and coverage. In this work, we propose Decoupled Load Value Prediction (DLVP), a technique that targets the value prediction challenges for load instructions. DLVP mitigates the stale state caused by stores by replacing value prediction with memory address prediction. Then, it opportunistically probes the data cache to retrieve the value(s) corresponding to the predicted address(es) early enough so value prediction can take place. Since the values captured in the data cache mirror the current program data (except for in-flight stores), this addresses the first challenge. Regarding the second challenge, DLVP reduces pipeline flushes by using a new context-based address prediction scheme that leverages load-path history to deliver high address prediction accuracy (over 99%) with relaxed confidence requirements. We call this address prediction scheme Path-based Address Prediction (PAP). With a modest 8KB prediction table, DLVP improves performance by up to 71%, and 4.8% on average, without increasing the core energy consumption. CCS CONCEPTS • Computer systems organization → Superscalar architectures; Pipeline computing; Reduced instruction set computing;