I remember when I was in COMPSCI-101 or something like that and was introduced to sorting, and after coming up with a basic bubble sort algorithm, thinking that there was no way that could be improved because it looked self-evident that you needed to do all the work the algorithm was doing or you couldn't be sure the list was really sorted. Then, of course, I was introduced to multiple better algorithms like merge sort and quick sort which showed just how wrong I was...
Perhaps, for the "scientist" writing that initial code, their algorithm was also already the best that could be done because they just don't know much about algorithms and techniques to make them faster?! But once you learn about things like divide-and-conquer, work avoidance etc. it becomes pretty much self-evident when your algorithm sucks... it's really not that hard - definitely not for people whose job is stuff like climate science who have a very good understanding of maths.
Maybe by teaching scientists the basic "smart" algorithms and just a little about algorithm analysis (Big O notation), massive computation improvements could be had.
Perhaps, for the "scientist" writing that initial code, their algorithm was also already the best that could be done because they just don't know much about algorithms and techniques to make them faster?! But once you learn about things like divide-and-conquer, work avoidance etc. it becomes pretty much self-evident when your algorithm sucks... it's really not that hard - definitely not for people whose job is stuff like climate science who have a very good understanding of maths.
Maybe by teaching scientists the basic "smart" algorithms and just a little about algorithm analysis (Big O notation), massive computation improvements could be had.