Abstract
With exascale-level computation on the horizon, the art of predicting the cost of computations has acquired a renewed focus. This task is especially challenging in the case of iterative methods, for which convergence behaviour often cannot be determined with certainty a priori (unless we are satisfied with potentially outrageous overestimates) and which typically suffer from performance bottlenecks at scale due to synchronization cost. Moreover, the amplification of rounding errors can substantially affect the practical performance, in particular for methods with short recurrences. In this article, we focus on what we consider to be key points which are crucial to understanding the cost of iteratively solving linear algebraic systems. This naturally leads us to questions on the place of numerical analysis in relation to mathematics, computer science and sciences, in general. This article is part of a discussion meeting issue 'Numerical algorithms for high-performance computational science'.