Code benchmarking: why should we spend time on it?

Benchmarking of a numerical code means comparing the numerical solution obtained with solving the system of linear equations with (i) analytical solutions (ii) results of physical (analogue) experiments (iii) numerical results from other (well-established) codes and (iv) general physical considerations. Benchmarking of newly created numerical tools is sometimes very tedious, but an absolutely necessary stage of code development as its purpose is to test the code's robustness in a broad range of situations relevant to geodynamic modelling applications. For instance, if you plan to model with your code shear heating processes in deforming rocks – make sure that your code provides the correct temperature changes related to mechanical energy dissipation; if you model subduction – make sure that your code handles correctly large viscosity contrasts and has no notable numerical diffusion of the temperature field and composition; if you intend to model self-gravitating planetary bodies – make sure that your code computes correct gravity field etc. We should not be lazy and limit ourselves to one or two common benchmarks, such as the Rayleigh–Taylor instability and convection with constant viscosity hoping that everything else will work automatically. No, it will not! Therefore, test your code on a broad range of challenging cases (several of which are discussed below), to explore its limitations and be creative in inventing and calibrating new numerical benchmarks.