Cache Persistence Analysis: Finally Exact

TL;DR

This paper introduces the first exact cache persistence analysis for LRU caches, leveraging a novel abstraction and ZDDs to improve accuracy and efficiency in worst-case execution time analysis.

Contribution

It presents an exact persistence analysis method for LRU caches, filling a gap left by previous approximative approaches, and demonstrates its practical competitiveness.

Findings

The analysis is NP-complete but still efficient in practice.

The exact analysis is competitive with inexact methods in memory and runtime.

Prior inexact methods often approximate the results closely in practice.

Abstract

Cache persistence analysis is an important part of worst-case execution time (WCET) analysis. It has been extensively studied in the past twenty years. Despite these efforts, all existing persistence analyses are approximative in the sense that they are not guaranteed to find all persistent memory blocks. In this paper, we close this gap by introducing the first exact persistence analysis for caches with least-recently-used (LRU) replacement. To this end, we first introduce an exact abstraction that exploits monotonicity properties of LRU to significantly reduce the information the analysis needs to maintain for exact persistence classifications. We show how to efficiently implement this abstraction using zero-suppressed binary decision diagrams (ZDDs) and introduce novel techniques to deal with uncertainty that arises during the analysis of data caches. The experimental evaluation demonstrates that the new exact analysis is competitive with state-of-the-art inexact analyses in terms of both memory consumption and analysis run time, which is somewhat surprising as we show that persistence analysis is NP-complete. We also observe that while prior analyses are not exact in theory they come close to being exact in practice.