Modeling Performance Variation Due to Cache Sharing

Authors: Andreas Sandberg, Andreas Sembrant, Erik Hagersten and David Black-Schaffer
Venue:    HPCA 2013

The authors of this paper present a modeling framework to predict cache contention when co-locating applications. The model is much lighter weight than previous work, and accurate within 0.41% on average. The authors utilize a three-fold approach:

  1. A cache sharing model - Predicts how much cache is used by an application
  2. A cache analysis tool (Cache Pirating) - Artificially reduces cache size
  3. A phase detection framework: (Scarphase) - Divides applications into phases
It should be noted that (1) and (2) can be done directly with what is now Intel RDT, which was not available at the time of publication.
    The authors show that co-location of applications exhibits extensive performance variability depending on alignment, particularly when applications exhibit extensive phase behavior. Therefore to predict performance of co-location, a user may need to profile applications over a hundred times. Using their framework, an application can be modeled quickly and accurately. Many results are presented with CDF graphs, which capture the accuracy of the model with run-to-run variation. 

The authors note the potentials for future work include including bandwidth modeling and prediction, which the state would drastically improve multi-core scaling accuracy.

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