Predicting Data Cache Misses in Non - Numeric

To maximize the beneet and minimize the overhead of software-based latency tolerance techniques, we would like to apply them precisely to the set of dynamic references that suuer cache misses. Unfortunately , the information provided by the state-of-the-art cache miss prooling technique (summary prooling) 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 prooling|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 signiicant reductions in memory stall time by exploiting at least one of the three forms of correlation prooling we consider.