Beyond Static Policies: Exploring Dynamic Policy Selection for Single-Thread Performance Optimization

2026-05-06

AI Summary

This study addresses the limitations of static cache replacement and prefetching policies in conventional processors, which struggle to maintain optimal performance across diverse execution phases. For the first time, it systematically evaluates the potential of dynamic policy selection by analyzing 490 execution phases from 49 benchmark programs using the ChampSim simulator. The results demonstrate that static policies incur an average IPC loss of 1.54%, whereas dynamically switching between two carefully selected policies reduces this loss to just 0.11%. Moreover, such dynamic adaptation achieves near-ideal performance in 52.65% of the phases, closely approaching the theoretical upper bound. This work validates the efficacy of dynamic policy switching and establishes a new paradigm for enhancing single-threaded performance.

Abstract

For over a decade, processor design has focused on implementing sophisticated policies for various components of the out-of-order pipeline, including cache replacement and prefetching. The prevailing design philosophy has been to build processors with a single, static selection of policies across these different mechanisms. This paper investigates a fundamental question: do different workloads, or even different execution phases within the same workload, benefit from different policy combinations? We present a comprehensive analysis exploring whether a hypothetical processor capable of dynamically selecting from multiple policies could significantly outperform traditional static-policy processors. Using ChampSim-based simulation across 49 benchmarks segmented into 490 execution phases of 20M instructions each, we evaluate performance across multiple policy combinations for cache replacement and prefetching. Our findings reveal that significant performance headroom exists: the best static policy achieves optimal performance for only 19.18\% of execution phases and incurs a mean IPC loss of 1.54\% compared to an oracle. Moreover, 85 phases (17.35\%), spanning 14 of the 49 applications, exhibit more than 2.5\% IPC loss relative to the oracle. Furthermore, we demonstrate that a processor capable of dynamically switching between two carefully chosen policies can achieve a 13.6$\times$ reduction in mean IPC loss (from 1.54\% to 0.11\%) and match oracle performance 52.65\% of the time. These results suggest that dynamic policy selection represents a promising avenue for unlocking single-thread performance improvements that have become increasingly difficult to achieve.

Problem

• dynamic policy selection
• single-thread performance
• cache replacement
• prefetching
• out-of-order pipeline

Innovation

• dynamic policy selection
• cache replacement
• prefetching
• single-thread performance
• out-of-order pipeline