A lot of my own work revolves around making algorithms scale better, and one of the preferred ways of showing parallel scaling and/or parallel efficiency is to plot the performance of an algorithm/code over the number of cores, e.g.

![artificial parallel scaling plot](https://i.stack.imgur.com/KVVLE.png)

where the $x$-axis represents the number of cores and the $y$-axis some metric, e.g. work done per unit of time. The different curves show parallel efficiencies of 20%, 40%, 60%, 80%, and 100% at 64 cores respectively.

Unfortunately though, in many publications, these results are plotted with a *log-log* scaling, e.g. the results in [this](http://bioinformatics.oxfordjournals.org/content/early/2013/02/21/bioinformatics.btt055.short) or [this](http://bioinformatics.oxfordjournals.org/content/early/2013/02/21/bioinformatics.btt055.short) paper. The problem with these log-log plots is that it's incredibly difficult to assess the actual parallel scaling/efficiency, e.g.

![enter image description here](https://i.stack.imgur.com/731uY.png)

Which is the same plot as above, yet with log-log scaling. Note that now there is no big difference between the results for 60%, 80%, or 100% parallel efficiency. I've written a bit more extensively about this [here](http://www.dur.ac.uk/pedro.gonnet/?p=141).

So here's my question: **What rationale is there for showing results in log-log scaling?** I regularly use linear scaling to show my own results, and regularly get hammered by referees saying that my own parallel scaling/efficiency results don't look as good as the (log-log) results of others, but for the life of me I can't see why I should switch plot styles.