Identifying program power phase behavior using power vectors

Abstract

Characterizing program behavior is important for both hardware and software research. Most modern applications exhibit distinctly different behavior throughout their runtimes, which constitute several phases of execution that share a greater amount of resemblance within themselves compared to other regions of execution. These execution phases can occur at very large scales, necessitating prohibitively long simulation times for characterization. Due to the implementation of extensive clock gating and additional power and thermal management techniques in modern processors, these program phases are also reflected in program power behavior, which can be used as an alternative means of program behavior characterization for power-oriented research. In this paper, we present our methodology for identifying phases in program power behavior and determining execution points that correspond to these phases, as well as defining a small set of power signatures representative of overall program power behavior. We define a power similarity metric as an intersection of both magnitude based and ratio-wise similarities in the power dissipation of processor components. We then develop a thresholding algorithm in order to partition the power behavior into similarity groups. We illustrate our methodology with the gzip benchmark for its whole runtime and characterize gzip power behavior with both the selected execution points and defined signature vectors.