Thesis (Ph. D.)-- University of Rochester. Department of Computer Science, 2019.
Locality analysis is important since the speed of accessing memory is slower
than the speed of computing. In addition, the emerging persistent memory brings
speed and endurance imbalance between reads and writes. Caching is an effective
way to address these problems. To maximize cache performance, it requires
understanding program locality for both cache loads and writebacks.
In this dissertation, I will first introduce locality analysis based on reuse intervals,
which can be measured effciently using the compiler construct dependence
distance. Then, I will describe a more general method called static parallel sampling.
It analyzes loop-based code by generating sampler code which is run to
measure reuse intervals. It can predict precise cache-line granularity miss ratio
curves for complex loops with non-linear array references and even branches. Finally,
I will describe the reuse interval based locality model for writebacks, which
can be analyzed using static parallel sampling.
