IEEE Access (Jan 2024)

Improving the Representativeness of Simulation Intervals for the Cache Memory System

  • Nicolas Bueno,
  • Fernando Castro,
  • Luis Pinuel,
  • Jose I. Gomez-Perez,
  • Francky Catthoor

DOI
https://doi.org/10.1109/ACCESS.2024.3350646
Journal volume & issue
Vol. 12
pp. 5973 – 5985

Abstract

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Accurate simulation techniques are indispensable to efficiently propose new memory or architectural organizations. As implementing new hardware concepts in real systems is often not feasible, cycle-accurate simulators employed together with certain benchmarks are commonly used. However, detailed simulators may take too much time to execute these programs until completion. Therefore, several techniques aimed at reducing this time are usually employed. These schemes select fragments of the source code considered as representative of the entire application’s behaviour–mainly in terms of performance, but not plenty considering the behaviour of cache memory levels–and only these intervals are simulated. Our hypothesis is that the different simulation windows currently employed when evaluating microarchitectural proposals, especially those involving the last level cache (LLC), do not reproduce the overall cache behaviour during the entire execution, potentially leading to wrong conclusions on the real performance of the proposals assessed. In this work, we first demonstrate this hypothesis by evaluating different cache replacement policies using various typical simulation approaches. Consequently, we also propose a simulation strategy, based on the applications’ LLC activity, which mimics the overall behaviour of the cache much closer than conventional simulation intervals. Our proposal allows a fairer comparison between cache-related approaches as it reports, on average, a number of changes in the relative order among the policies assessed – with respect to the full simulation – more than 30% lower than that of conventional strategies, maintaining the simulation time largely unchanged and without losing accuracy on performance terms, especially for memory-intensive applications.

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