All-window profiling and composable models of cache sharing

  • Authors:

  • Xiaoya Xiang;Bin Bao;Tongxin Bai;Chen Ding;Trishul Chilimbi

  • Affiliations:

  • University of Rochester, Rochester, NY, USA;University of Rochester, Rochester, NY, USA;University of Rochester, Rochester, NY, USA;University of Rochester, Rochester, NY, USA;Microsoft Research, Redmond, WA, USA

  • Venue:
  • Proceedings of the 16th ACM symposium on Principles and practice of parallel programming
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

As multi-core processors become commonplace and cloud computing is gaining acceptance, more applications are run in a shared cache environment. Cache sharing depends on a concept called footprint, which depends on all cache accesses not just cache misses. Previous work has recognized the importance of footprint but has not provided a method for accurate measurement, mainly because the complete measurement requires counting data access in all execution windows, which takes time quadratic in the length of a trace. The paper first presents an algorithm efficient enough for off-line use to approximately measure the footprint with a guaranteed precision. The cost of the analysis can be adjusted by changing the precision. Then the paper presents a composable model. For a set of programs, the model uses the all-window footprint of each program to predict its cache interference with other programs without running these programs together. The paper evaluates the efficiency of all-window profiling using the SPEC 2000 benchmarks and compares the footprint interference model with a miss-rate based model and with exhaustive testing.