Research software code is likely to remain a tangled mess

Derek Jones from The Shape of Code

Research software (i.e., software written to support research in engineering or the sciences) is usually a tangled mess of spaghetti code that only the author knows how to use. Very occasionally I encounter well organized research software that can be used without having an email conversation with the author (who has invariably spent years iterating through many versions).

Spaghetti code is not unique to academia, there is plenty to be found in industry.

Structural differences between academia and industry make it likely that research software will always be a tangled mess, only usable by the person who wrote it. These structural differences include:

  • writing software is a low status academic activity; it is a low status activity in some companies, but those involved don’t commonly have other higher status tasks available to work on. Why would a researcher want to invest in becoming proficient in a low status activity? Why would the principal investigator spend lots of their grant money hiring a proficient developer to work on a low status activity?

    I think the lack of status is rooted in researchers’ lack of appreciation of the effort and skill needed to become a proficient developer of software. Software differs from that other essential tool, mathematics, in that most researchers have spent many years studying mathematics and understand that effort/skill is needed to be able to use it.

    Academic performance is often measured using citations, and there is a growing move towards citing software,

  • many of those writing software know very little about how to do it, and don’t have daily contact with people who do. Recent graduates are the pool from which many new researchers are drawn. People in industry are intimately familiar with the software development skills of recent graduates, i.e., the majority are essentially beginners; most developers in industry were once recent graduates, and the stream of new employees reminds them of the skill level of such people. Academics see a constant stream of people new to software development, this group forms the norm they have to work within, and many don’t appreciate the skill gulf that exists between a recent graduate and an experienced software developer,
  • paid a lot less. The handful of very competent software developers I know working in engineering/scientific research are doing it for their love of the engineering/scientific field in which they are active. Take this love away, and they will find that not only does industry pay better, but it also provides lots of interesting projects for them to work on (academics often have the idea that all work in industry is dull).

    I have met people who have taken jobs writing research software to learn about software development, to make themselves more employable outside academia.

Does it matter that the source code of research software is a tangled mess?

The author of a published paper is supposed to provide enough information to enable their work to be reproduced. It is very unlikely that I would be able to reproduce the results in a chemistry or genetics paper, because I don’t know enough about the subject, i.e., I am not skilled in the art. Given a tangled mess of source code, I think I could reproduce the results in the associated paper (assuming the author was shipping the code associated with the paper; I have encountered cases where this was not true). If the code failed to build correctly, I could figure out (eventually) what needed to be fixed. I think people have an unrealistic expectation that research code should just build out of the box. It takes a lot of work by a skilled person to create to build portable software that just builds.

Is it really cost-effective to insist on even a medium-degree of buildability for research software?

I suspect that the lifetime of source code used in research is just as short and lonely as it is in other domains. One study of 214 packages associated with papers published between 2001-2015 found that 73% had not been updated since publication.

I would argue that a more useful investment would be in testing that the software behaves as expected. Many researchers I have spoken to have not appreciated the importance of testing. A common misconception is that because the mathematics is correct, the software must be correct (completely ignoring the possibility of silly coding mistakes, which everybody makes). Commercial software has the benefit of user feedback, for detecting some incorrect failures. Research software may only ever have one user.

Research software engineer is the fancy title now being applied to people who write the software used in research. Originally this struck me as an example of what companies do when they cannot pay people more, they give them a fancy title. Recently the Society of Research Software Engineering was setup. This society could certainly help with training, but I don’t see it making much difference with regard status and salary.

Main memory: the crucial component that vendors don’t mention

Derek Jones from The Shape of Code

CPU performance hogs the limelight when people discuss the year-on-year increases in computing power that used to occur.

This focus on cpu performance was/is driven by marketing, the people with the money either don’t want customers thinking about the performance impact of main memory size or speed, or want them to treat the processor as the most important component of a computer. Vendors want processor performance to drive customer purchase decisions.

Hardware manufacturers used to entice new customers with low cost machines, containing minimal memory. Once a customer started to use their shiny new computer, they found that it did save them lots of time and money, but also they needed more memory (which could only be brought from the manufacturer and was not cheap).

The plot below shows the prices IBM charged for System 360s, in 1966. Anti-trust investigations uncover all kinds of interesting data, like selling low-spec equipment at a loss to entice customers and make life difficult for competitors (code+data for all plots).

Profit margin on IBM 360s sold with various memory sizes

The plot below (data from the 19 Aug 1985 issue of ComputerWorld) shows how the price of computers increased as the minimum about of memory they supported increased.

Yes, in 1985 top end computers came with over 50M of memory; but most customers thought themselves lucky if they had a few megabytes.

If the processor is slow, it just takes longer for programs to run. If the computer does not have enough memory, programs cannot run. For most applications memory requirements are addressed first, followed by processor performance; memory requirements is the number one issue. The optimizations that commercial compilers could perform were limited by the memory capacity of developer machines.

List price of computers, in 1985, supporting the given minimum amount of  memory

Intel’s main line of business used to be selling memory chips, but these chips became commodity items as more companies entered the market; Intel bet the farm on selling processors and the rest is history. As a seller of a unique product it was/is in Intel’s interest to spend lots of money on marketing the benefits of processor performance; sellers of commodity items (such as memory chips) don’t have nearly as much to gain from generic product marketing, because customers may choose to buy from other sellers (in such markets sellers have to concentrate on marketing themselves).

Memory capacity/speed and cpu speed are two aspects of system performance; they need to be balanced to meet customer drive application requirements. The plot below shows the SPEC cpu integer performance of 4,332 systems running at various clock rates; the colors denote the different peak memory transfer rates of the memory chips in these systems (code+data).

SPEC cpu integer performance vs. cpu clock rate

These days (and perhaps in the past, I don’t have any data), memory performance is a much better predictor of system performance, but vendors don’t have an incentive to market this fact.