Cache sharing among processors is important for Chip Multiprocessors to reduce inter-
thread latency, but also brings cache contention, degrading program performance …

The locality metrics are many, for example, miss ratio to test performance, data footprint to
manage cache sharing, and reuse distance to analyze and optimize a program. It is unclear …

The reuse distance (LRU stack distance) is an essential metric for performance prediction
and optimization of storage and CPU cache. Over the last four decades, there have been …

Reuse distance analysis is a well-established tool for predicting cache performance, driving
compiler optimizations, and assisting visualization and manual optimization of programs …

Reuse distance is a well established approach to characterizing data cache locality based
on the stack histogram model. This analysis so far has been restricted to offline use due to …

Many techniques characterize the program working set by the notion of the program
footprint, which is the volume of data accessed in a time window. A complete …

The reuse distance (least recently used (LRU) stack distance) is an essential metric for
performance prediction and optimization of storage cache. Over the past four decades, there …

When a cache is shared by multiple cores, its space may be allocated either by sharing,
partitioning, or both. We call the last case partition-sharing. This paper studies partition …

One widely used metric that measures data locality is reuse distance—the number of unique
memory locations that are accessed between two consecutive accesses to a particular …

A major challenge in processing real-world graphs stems from poor locality of memory
accesses and vertex reordering algorithms (RAs) have been proposed to improve locality by …