John R. Rice's "Future Scientific Software Systems"

Today I stumbled upon the slides of a presentation, "Future Scientific Software Systems", presented at the first IEEE Computer Society Workshop on Computational Science and Engineering, in October 1996 at Purdue University.  The presenter was John R. Rice (1934-2024), a distinguished professor of computer science at Purdue.

Some remarkable statements in the talk did not make it into the published version, which appeared the following year in the journal, IEEE Computational Science and Engineering. Slide 20, for example, states that Numerical Recipes is "Extremely successful commercially and a failure scientifically.  The lessons learned in the 1950s-1970s are ignored."  Under the bullet for Microsoft Excel, it simply says "Ditto".  Late in the slide, it says "Poor, unreliable, inefficient software can be a commercial success.  Most users have no reliable way to assess software quality."

Much has been written about the flaws of Microsoft Excel elsewhere, so I suspect there won't be much controversy over this statement.  Rice's claim about Numerical Recipes was a shock to my system when I first heard it (from Rice himself, though at a different forum). The first edition of NR (dating to 1986) was the bread and butter of the computational component of my undergraduate research project in 1994-1995; admittedly I had not had any formal training in computer science beyond high school at the time I was working on that project.  However, my elders seemed to treat NR as an authoritative source, and I had no reason to think otherwise until I heard Prof. Rice denounce it just a year or two later.  The fact that NR was written by users (albeit very sophisticated ones) rather than "professional" computer scientists or computational mathematicians, lent some plausibility to the claim.

About a decade later, when I was a working professional, I discovered empirically an example of one of NR's flaws.  I was trying to understand spectral analysis of unevenly spaced data, and NR promotes the Lomb-Scargle method.  I quickly discovered that this was a very active field of research, and there was a plethora of alternatives to Lomb-Scargle, though I didn't have the time to investigate them all and figure out which ones were the most fit for purpose for our work.  No hint of this rich literature is made in NR, not even in the third (2007) edition.  I had finally discovered evidence that what Rice had said a decade earlier might not just be the opinion of a disgruntled academic.  On the other hand, the community of "professionals" could be considered as having equally failed to provide a pragmatic alternative to NR to users.  

Returning to Rice's presentation, the slides after slide 20 start to look ahead in time.  The final slide (slide 33) is amusing as it is titled "High impact applications that won't happen". He predicts that "By 2015 we will not have simulations that are reliable and accurate for"

• Weather forecasts of several days
• Social interactions such as the economy, small groups, warfare, business growth
• Life forms of a single cell
• Geophysics such as earthquakes and volcano eruptions
• Climate
• Software engineering of large Fortran/C applications 

Here we are ten years after 2015, in 2025.  I think the list of applications that did not happen remains accurate, except possibly for the first bullet (weather forecasts).  He does not define how "reliable and accurate" such forecasts need to be, but I think it would be unfair to claim that we cannot make such forecasts reasonably well in 2025.

Rice looked ahead 20 years.  Could we see progress on the rest of the above list in the next twenty?  One thing Rice did not foresee is that simulations might come to be supplanted by artificial intelligence methods, such as large language models. We have already started to see AI encroach as a competitor to simulations in weather forecasting.  Alternatively, simulations and AI might find a way to work together to make progress on these kinds of problems.  I'm reluctant to be completely skeptical that no progress on the above list will be made in the next 20 years, though not necessarily by simulations alone.

 

Reference

J. R. Rice, 1997:  Future scientific software systems.  IEEE Computational Science and Engineering, April/June issue, pp. 44-48.