Efficient Representations and Abstractions for Quantifying and Exploiting Data Reference Locality

Abstract

With the growing processor-memory performance gap, understanding and optimizing a program's reference locality, and consequently, its cache performance, is becoming increasingly important. Unfortunately, current reference locality optimizations rely on heuristics and are fairly ad-hoc. In addition, while optimization technology for improving instruction cache performance is fairly mature (though heuristic-based), data cache optimizations are still at an early stage. We believe the primary reason for this imbalance is the lack of a suitable representation of a program's dynamic data reference behavior and a quantitative basis for understanding this behavior.