In my previous posts (e.g. here and here) I showed how to use ps output (e.g. from ExaWatcher) visualization to spot performance problems in Linux. Here I’d like to show that this approach can be taken a little bit further, namely, to find the source of increase in memory usage.
The R code for this is quite straightforward. I also think it shouldn’t be much of a problem to do the same in Python, although I haven’t gotten around to try it myself. In the code below read_ps is the function that reads in a ps output file from ExaWatcher without unzipping it, sum_and_tidy does the aggregation, and visualize_rss does the plotting. There is also an auxillary function keep_top_n which is needed to keep the number of color bands to something reasonable.