Program runtime characteristics exhibit significant variation. As microprocessor architectures become more complex, their efficiency depends on the capability of adapting with workload dynamics. Moreover, with the approaching billion-transistor microprocessor era, it is not always economical or feasible to design processors with thermal cooling and reliability redundancy capabilities that target an application’s worst case scenario. Therefore, analyzing complex workload dynamics early, at the microarchitecture design stage, is crucial to forecast workload runtime behavior across architecture design alternatives and evaluate the efficiency of workload scenariobased architecture optimizations. Existing methods focus exclusively on predicting aggregated workload behavior. In this paper, we propose accurate and efficient techniques and models to reason about workload dynamics across the microarchitecture design space without using detailed cyclelevel simulations. Our proposed techniques...