![origin graphing so origin graphing so](https://d2mvzyuse3lwjc.cloudfront.net/ftp/ConfWikiLinks/WikiImg9/93_multipanel_legend.png)
But on the linear y-axis in the left-hand graph above, the distance between 1 and 2 is the same as the distance between 3 and 4, so these feats would appear equally impressive. Going from a 1x speedup to a 2x speedup is surely more impressive than going from a 3x speedup to a 4x speedup.In the right-hand graph above, it is immediately obvious that A and B experience a slowdown this is slightly less obvious in the left-hand graph. This fits nicely with logarithmic scales, which can’t go down to 0. That is, we are primarily interested in seeing whether a data point lies above 1 (which indicates a speedup) or below 1 (which indicates a slowdown). The natural origin for a speedup ratio is 1, not 0.There are four reasons why the logarithmic scale is better: The left graph uses a linear scale on the y-axis, while the right one plots the same data on a logarithmic scale. I will illustrate my reasons with reference to the two graphs below, which both show some sort of “speedup” that has been obtained on four benchmark programs, A, B, C, and D. For example: the ratio between the execution time of a program before a proposed compiler optimisation has been applied and the execution time of that program afterwards. By “normalised data”, I mean data that is the ratio between two measurements that have the same dimension. I believe that normalised data should be plotted on a logarithmic scale. I’ll now elaborate on both of these points, drawing upon examples from 31 examples of graphs I found in the proceedings of PLDI 2019. scatter plots can be easier to understand than bar charts.normalised data should usually be plotted on a logarithmic scale, and.To get straight to the point, I have two concrete recommendations:
ORIGIN GRAPHING SO HOW TO
In this post, I’d like to share some thoughts I’ve accumulated over the past few years about how to draw better graphs.