Program behavior prediction using a statistical metric model

Adaptive computing systems rely on predictions of program behavior to understand and respond to the dynamically varying application characteristics. This study describes an accurate statistical workload metric modeling scheme for predicting program phases. Our evaluations demonstrate the superior performance of this predictor over existing predictors on a wide range of benchmarks. This prediction accuracy lends itself to improved power-performance trade-offs when applied to dynamic power management.