A detailed profile of exascale applications helps to understand the computation, communication and memory requirements for exascale systems and provides the insight necessary for fine-tuning the computing architecture. Obtaining such a profile is challenging as exascale systems will process unprecedented amounts of data. Profiling applications at the target scale would require the exascale machine itself. In this work we propose a methodology to extrapolate the exascale profile from experimental observations over datasets feasible for today’s machines. Extrapolation models are carefully selected by means of statistical techniques and a high-level complexity analysis is included in the selection process to speed up the learning phase and to improve the accuracy of the final model. We extrapolate run-time properties of the target applications including information about the instruction mix, memory access pattern, instruction-level parallelism, and communication requirements. Compared to state-of-the-art techniques, the proposed methodology reduces the prediction error by an order of magnitude on the instruction count and improves the accuracy by up to 1.3× for the memory access pattern, and by more than 2× for the communication requirements.