A Gaussian Set Sampling Model for Efficient Shared Cache Profiling on Multi-Cores

Yi Zhang, Zhanwei Ling, Mingsong Lv, Nan Guan

Research output: Journal article publicationJournal articleAcademic researchpeer-review


The last level cache (LLC) has significant impact to system performance on modern multi-core processors. But as cache sizes reach several megabytes and more, the overhead of exploring performance on LLC greatly increases as well. To improve the efficiency of performance analysis, we propose a set-sampling-based cache profiling model for the performance analysis on multi-core LLC. We first explore the memory access distributions on LLC by developing a low-overhead stress-application-based method. The results show that memory access distributions can be approximated by Gaussian distribution function. Based on this observation, a Gaussian-distribution-based set sampling model is proposed which can predict program performance with limited representative samples. We evaluate our model on a contemporary multi-core machine and show that 1) the proposed method can precisely predict program performance on LLC under different contention intensities and 2) our method can achieve similar precision with less samples compared to widely adopted set sampling methods such as the random sampling and the continuous address sampling.

Original languageEnglish
Article number8807151
Pages (from-to)115560-115567
Number of pages8
JournalIEEE Access
Publication statusPublished - 2019


  • Gaussian distribution
  • multi-core
  • set sampling
  • shared cache

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


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