We have devised similarity matrices similar to Brad Calder's phase work based on our component power data, which we name Power Vectors, and demonstrated phase information with Matrix Plots. Then we developed a similarity metric based on the original Manhattan Distance (L1 Norm) idea, which we found more suitable for power behavior similarity. We used a thresholding algorithm to group application's execution points into phases and demonstrated the groups with Grouping Matrices. Afterwards, we have seen that we can represent the overall program power behavior with a small set of signature vectors.

We do this research following the hope to be able to use the signatures for program identification, phase prediction, etc. Also we want to see how well simulations based on only representative points can approximate overall power behavior.