Complexity-based program phase analysis and classification

Modeling and analysis of program behavior are at the foundation of computer system design and optimization. As computer systems become more adaptive, their efficiency increasinsgly depends on program dynamic characteristics. Previous studies have revealed that program runtime execution manifests phase behavior. Recently, methods and tools to analyze and classify program phases have also been developed. However, very few studies have been proposed so far to understand and evaluate program phases from their dynamics and complexity perspectives. In this work, we propose new methods, metrics and frameworks which aim to analyze, quantify, and classify the dynamics and complexity of program phases. Our methods use wavelet techniques to represent program phases at multiresolution scales. The cross-correlation coefficients between phase dynamics observed at different scales are then computed as metrics to quantify phase complexity. We propose to apply wavelet-based multiresolution analysis and data clustering to classify program execution into phases that exhibit similar degree of complexity. Experimental results on SPEC CPU 2000 benchmarks show that the proposed schemes classify complexity-based program phases better than currently used approaches.