Once again, I find myself with a set of broken assumptions. The article itself is about a 10x performance gain by modifying a proven-optimal algorithm to account for virtual mem
One important thing is to realize that the most common usage of big-O notation (to talk about runtime complexity) is only half of the story - there's another half, namely space complexity (that can also be expressed using big-O) which can also be quite relevant.
Generally these days, memory capacity advances have outpaced computing speed advances (for a single core - parallelization can get around this), so less focus is given to space complexity, but it's still a factor that should be kept in mind, especially on machines with more limited memory or if working with very large amounts of data.