Mailund on the Internet

There’s a nice short article in American Scientist titled How do scientists really use computers?

An interesting read if you, as I, teach life scientists (and not computer scientists) computer science.

The conclusion doesn’t surprise me much, though:

Our results can be interpreted in many ways, but I think two things are clear. The first is that if funding agencies, vendors and computer science researchers really want to help working scientists do more science, they should invest more in conventional small-scale computing. Big-budget supercomputing projects and e-science grids are more likely to capture magazine covers, but improvements to mundane desktop applications, and to the ways scientists use them, will have more real impact.

Even at BiRC where we do a lot of genome analysis that really do need computer grids, most of our computer use is desktop computers.

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219-220=-1

Gene Frantz asks this question at embedded.com:

I have challenged several of our senior technologists to think about what the state of the art will be in the year 2020. You might say that we need to have 20/20 vision for the year 2020. I have invited a number of technologists to provide their point of view (POV) of what the state of the art in IC technology will be in the year 2020, and I’m interested to hear what you have to say on the topic. But, since this is my blog, I will have the first and last word on what the year 2020 will hold for us.

My guess, which most people will probably agree with, is that 1) clock rate will not be much different from today, 2) the memory architecture (levels of cache, RAM, disk…) will still have orders of magnitude differences in access time, and 3) we are going to see parallelisation – and multiple cores – in a big way.

This means that the (computer science) theoretical RAM model is going to be increasingly bad at modeling real computers.  Access time is not constant and execution is not sequential.

The PRAM model will probably be pretty good at dealing with multiple cores (where it isn’t really that good for modeling distributed computing).

I’m not sure which models there are for dealing with memory hierarchies.  I know there are some, but there were no classes on this when I studied, and I haven’t kept up with this… I know there are cache-oblivious algorithms – I have friends at the CS department who works on this – but I don’t really know much about it.  I should probably start worrying about it before 2020…

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209-206=+3

Now that I am getting into the habit of stealing titles, I’ve done it again. This time from this post that ask that very question.

I am a computer scientist, and I don’t know the answer. Having been educated in a very theoretical computer science department, I would probably group computer science with math (and I do not consider math a science, but it isn’t engineering either). It is not as simple as that, though. Computer science gets its hands in all kinds of things and not always with pure (math) intentions, so who knows what it really is…

I look forward to the rest of the series.