This strong scaling aspect is of great importance for spectroscopy simulations (more so than energy calculations) simply due to the very large number of Fock matrices that need to be determined, and arguably, it makes sense to view the number of Fock matrices produced per hour as a key performance indicator (KPI) of the code. From Table , we see that this KPI for the large model system and using four nodes is equal to 10.4 h -1 -as given by 3,600 sec/hour divided by (340.7 + 5.1) sec. For energy calculations, this number is more than adequate but for spectrum simulations it is just barely sufficient for practical work as it will take several hundreds of wall time hours to produce a spectrum. This KPI number is determined from data taken from the SCF optimization where the density matrices are always symmetric. In the response part (that dominates the computational cost), there is a mix of symmetric and anti-symmetric auxiliary density matrices and for the latter ones there is no Coulomb contribution. Taking the average of 105 Fock builds in one prototypical iteration of the linear response equation solver, we obtain a KPI of 14.1 h -1 with use of four nodes. On eight nodes, the KPI number reaches a value of 20.7 h -1 when averaged over 105 Fock builds, and this number can be pushed a bit further with use of up to more nodes simply because the strong scaling performance should benefit from the system being larger than that used in Fig. . With use of 12 and 16 nodes, we reach KPI results of 24.3 and 26.3 h -1 , respectively.