Beyond Static Policies: Dynamic Selection Among Modern Microarchitectural Policies

2026-08-02

AI Summary

This work addresses the phase-sensitive interplay between prefetching and replacement policies in modern processors, where static configurations often fail to sustain optimal performance due to dynamic workload behavior. For the first time, it reveals the strong phase dependency of combined L1D/L1I prefetcher and L2 replacement strategies and proposes a lightweight dynamic selection mechanism governed by a single-bit control signal, framing policy switching as an information acquisition problem. Leveraging phase-level performance analysis, execution feedback, passive memory monitoring, and counterfactual evaluation, the approach recovers 62.4%–73.4% of the oracle performance gap across diverse workloads. Notably, the Berti/Gaze combination—dynamically switching only the L1D prefetcher—nearly matches the performance of an eight-policy oracle, achieving an average IPC gap as low as 0.039%.

Abstract

Modern processors gain performance from interacting policies: prefetchers, predictors, replacement rules, and schedulers. These policies are often evaluated one at a time, yet a policy that wins in one stack may lose in another. To study these effects, we present the first systematic composition study of two L1D prefetchers, two L1I prefetchers, and two L2 replacement policies across 490 phases from 49 SPEC CPU2006 and SPEC CPU 2017 traces. We define the best global static policy (BGSP) by phase-level oracle-win frequency. Gaze/Entangling/Mockingjay is the BGSP, winning 33.47% of phases, yet it remains 1.33% below the phase oracle on average, with 52 phases across eight benchmarks losing more than 2.5%. The opportunity is highly compressible: a Berti/Gaze pair that changes only the L1D prefetcher comes within 0.039% aggregate IPC of the eight-configuration oracle, reducing runtime control to one bit per 200K-instruction window. Given that one-bit interface, we frame selector design as an information problem: what can hardware know before choosing? We evaluate selectors that use only chosen-policy IPC, selectors that passively monitor the demand stream before either prefetcher changes cache state, and an ideal counterfactual observer that exposes the inactive-policy winner signal. The main practical result is that both executed-performance feedback and passive demand monitoring techniques capture much of the two-policy opportunity, recovering 62.4% to 73.4% of the pairwise oracle gap without executing or emulating the inactive prefetcher. The counterfactual study shows that inactive-policy observation must be nearly exact and available within one window to improve on executed-performance or passive demand monitoring. These results suggest a general method for adapting among microarchitectural policies as an additional pathway for processor improvement, distinct from structural resizing.

Problem

• microarchitectural policies
• dynamic selection
• prefetchers
• replacement policies
• performance optimization

Innovation

• dynamic policy selection
• microarchitectural composition
• prefetcher adaptation
• phase-aware optimization
• hardware selector design