Simplified workload characterization using unified prediction

Quantitative workload characterization is essential to high performance computer architecture design. Unfortunately, quantitative results are typically hard to interpret, reproduce and compare, due to the staggering amount of detail inherent in modern architecture. Source language, compiler technology target ISA, and micro-architecture, intertwined with system aspects such as memory hierarchy and multitasking regime, all add to the complexity of workload characterization. We propose two simple metrics to characterize program execution: a footprint measures the "size" of a program, and a Unified Prediction profile shows its "complexity". These metrics are architecture-independent, and allow meaningful comparisons of program behavior at a numerical but abstract level. We believe they can provide direction to subsequent, more detailed and costly simulation efforts.