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Adaptive computing systems rely on accurate projections of program behavior to understand and respond to the dynamically-varying application characteristics. In this study, we propose a Statistical Metric Model (SMM) that is system- and metric-independent for predicting program phases. SMM is a probability distribution over workload patterns and it attempts to model how frequently a specific behavior occurs. Maximum Likelihood Estimation (MLE) criterion is used to estimate the parameters of the SMM. The model parameters are further refined with a smoothing method to improve the robustness of the proposed program model. Finite sequences of execution metrics, obtained from running workloads are used to train the SMM. The SMM learns the application patterns during runtime, and at the same time predicts the upcoming program phases based on what it has learned so far. The finite prediction horizon can be adjusted based on the available data, which is used to estimate the model parameters. An extensive and rigorous series of prediction experiments demonstrate the superior performance of the SMM predictor over existing predictors on a wide range of benchmarks. For some benchmarks the improvements in prediction error are as much as 10-fold and 3-fold respectively, compared to the existing last-value and table-based prediction approaches.
By: Ruhi Sarikaya; Canturk Isci; Alper Buyuktosunoglu
Published in: ACM Performance Evaluation Review, volume 38, (no 1), pages 371-2 in 2010
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