CacheQuery: Learning Replacement Policies from Hardware Caches

TL;DR

This paper introduces CacheQuery, a method that uses automata learning and program synthesis to infer cache replacement policies from hardware caches, revealing new policies and improving scalability.

Contribution

It presents a novel approach combining automata learning and program synthesis to accurately infer cache replacement policies from hardware, uncovering previously undocumented policies.

Findings

Successfully inferred cache replacement policies using timing measurements.

Discovered two previously undocumented cache replacement policies.

Demonstrated improved scope and scalability over prior methods.

Abstract

We show how to infer deterministic cache replacement policies using off-the-shelf automata learning and program synthesis techniques. For this, we construct and chain two abstractions that expose the cache replacement policy of any set in the cache hierarchy as a membership oracle to the learning algorithm, based on timing measurements on a silicon CPU. Our experiments demonstrate an advantage in scope and scalability over prior art and uncover 2 previously undocumented cache replacement policies.