language: en

Predicting Data Cache Misses in Non-Numeric Applications Through Correlation Profiling

Todd C. Mowry Chi-Keung Luk
Department of Computer Science Department of Computer Science Carnegie Mellon University University of Toronto
Pittsburgh, PA 15213 Toronto, Canada M5S 3G4
tcm@cs.cmu.edu luk@eecg.toronto.edu

Abstract To maximize the benefit and minimize the overhead of software-based latency tolerance techniques, we would like to apply them precisely to the set of dynamic references that suffer cache misses. Unfortunately, the information provided by the state-of-the-art cache miss profiling technique (summary profiling) is inadequate for references with intermediate miss ratios|it results in either failing to hide latency, or else inserting unnecessary overhead. To overcome this problem, we propose and evaluate a new technique| correlation profiling|which improves predictability by correlating the caching behavior with the associated dynamic context. Our experimental results demonstrate that roughly half of the 22 non-numeric applications we study can potentially enjoy significant reductions in memory stall time by exploiting at least one of the three forms of correlation profiling we consider.

1 Introduction