Recently the latest generation of Blue Gene machines became available. In this paper we introduce general metrics to characterize the performance of applications and apply it to a diverse set of applications running on Blue Gene/Q. The applications range from regular, floating-point bound to irregular event-simulator like types. We argue that the proposed metrics are suitable to characterize the performance for a larger set of computational science applications running on today's massively-parallel systems. They therefore do not only allow to assess usability of the Blue Gene/Q architecture for the considered (types of) applications. They also provide more general information on application requirements and valuable input for evaluating the usability of various architectural features, i.e. information, which is needed for future co-design efforts aiming for exascale performance.