Branch prediction is critical for high-performance CPUs, with mispredictions causing significant execution inefficiencies. Modern server workloads exacerbate the challenge due to expanding instruction and branch working sets, while predictor capacities remain limited to avoid latency increases. A recently introduced hierarchical branch predictor design, LLBP, demonstrated a way to reduce misprediction rates by augmenting an unmodified TAGE-based predictor with a decoupled high-capacity metadata store. Despite using a large amount of storage, LLBP was shown to achieve only a fraction of the accuracy gain of an equal-sized (but impractical) TAGE-based predictor.

This work provides a detailed analysis of LLBP, identifying sources of its accuracy loss. Chief among these are contention within certain sets of LLBP’s high-capacity metadata store (namely those containing patterns for hard-to-predict branches), as well as duplication of patterns, which leads to prolonged training time. To address these, we propose dynamic context depth adaptation, an enhancement to the baseline LLBP design that achieves much better spreading of patterns for hard-to-predict branches, which reduces contention in the associated sets, while also reducing the incidence of duplication of patterns in LLBP’s sets. Our proposed design that realizes dynamic context depth adaptation requires only small modifications to the baseline LLBP while increasing its accuracy by 0.7-15.3% (3.8% on average).