On 26 Aug 2008, at 2:29 pm, Perry E. Metzger wrote:

I think part of the issue is that most people doing scientific
computing don't have computer science backgrounds, which is a
shame.

There is an unwritten recruitment rule, certainly in my field of science, that the programmer "must understand the science", and actually being able to write good code is very much a secondary requirement. I think this grew out of the last 20 years of exponentially increasing computer power which meant that in many fields you could write crappy code and just wait for hardware improvements to make it faster. This is particularly the case in fields such as bioinformatics where the field came into existence since the days of very limited memory and very slow machines, so they never experienced the world when writing tight code was essential (I started programming in 1984, so I can barely remember those days either). This is further hindered by the fact that no-one doing a masters in Bioinformatics learns a compiled language. They learn things like Java, R, perl, python and ruby.

There is light at the end of the tunnel, though. I'm beginning to see signs that people are starting to be hired primarily as programmers, and not scientists. This is usually in areas where the scientists have hit a brick wall in terms of performance, and with exponentially increasing data quantities, had nowhere else to go. I expect this to gradually expand over the next couple of years, but there's going to be a lot of pain in the meantime - particularly for those of us building and running the systems, who will tend to get the blame when we supply a 10,000 core cluster and the scientists find their code doesn't run any faster than it does on the current 1,000 core system. "It's a more powerful system, it must be your fault it's not working"

Tim


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