Analytic processor model for fast design-space exploration
In this paper, we propose an analytic model that takes as inputs a) a parametric microarchitecture-independent characterization of the target workload, and b) a hardware configuration of the core and the memory hierarchy, and returns as output an estimation of processor-core performance. To validate our technique, we compare our performance estimates with measurements on an Intel® Xeon® system. The average error increases from 21% for a state-of-The-Art simulator to 25% for our model, but we achieve a speedup of several orders of magnitude. Thus, the model enables fast designspace exploration and represents a first step towards an analytic exascale system model.