A new effort at the Texas Advanced Computing Center is aimed at teaching scientists and engineers about supercomputing. They argue that "Anyone looking to do relevant computational work today in the sciences and engineering must have these skills." They offer a certificate or portfolio in "Scientific Computation."
Greg Wilson has been going after this same goal using a different strategy. He suggests that before we can teach scientists and engineers about high-performance computing, we first have to teach them about computing. He leads an effort called "Software Carpentry" to figure out what to teach scientists and engineers about computing:
I’ve been trying to construct a better answer for the past thirteen years; Software Carpentry (http://software-carpentry.org/blog/) is what I’ve arrived at. It’s the 10% of software engineering (with a very small ‘e’) that scientists and engineers need to know *before* they tackle GPUs, clusters, and other fashionable Everests. Like sanitation and vaccination, the basic skills it teaches are cheap and effective; unfortunately, the other characteristic they share is that they’re not really what photo ops are made of. We’ve also found a lot of resistance based on survivor bias: all too often, senior scientists who *have* managed to get something to work on a supercomputer say, “Well, I didn’t need version control or unit testing or any of that guff, so why should my students waste their time on it?” Most scientists (rightly) regard computing as a tax they have to pay in order to get results.
The evidence is that the problem of teaching everyone else about computer science is bigger than teaching computer science majors about computer science. Chris Scaffidi, Mary Shaw, and Brad Myers have estimated that, by 2012, there will be about 3 million professional software developers in the United States, but there will also be about 13 million end-user programmers -- people who program as part of their work, but who do not primarily develop software. That result suggests that for every student in your computer science classes, there are four more students who could use some help in learning computer science. Those scientists and engineers who will one day be programming are in those other four.
Maybe we should be teaching scientists and engineers about computer science more generally. But as Greg Wilson points out, they don't want much -- they see computer science as a "tax." What's the core of computer science that even scientists and engineers ought to know? Alan Kay recently suggested a "Triple Whammy" defining the core of computer science:
That's a pretty powerful set. That goes way beyond Python vs. Java, or using Perl to check genome sequences with regular expressions vs. using MATLAB for analyzing data from ecological simulations. How do we frame the "Triple Whammy" in a way that fledgling scientists and engineers would find valuable and learnable?
To me, the Triple Whammy seems more like why computation is interesting philosophically, but it's not what engineers and scientists need to know.
I see engineers and scientists go astray when they develop -- or request the development of -- some computational solution all the time. They start wrong and then, when they get in trouble, they don't know how to recover. They start wrong because they don't know about modular (de)composition, i.e., attacking problems from the outside in. When they hit serious performance barriers, they try changing languages and other hacks, because they don't know about algorithmic analysis and design.
The value of algorithmic design is easy to motivate via examples. The value of modular (de)composition is more challenging to make stick, despite the many decades that have passed Abelson and Sussman first brought those ideas to engineers.
I believe what people don't see is that Computer Science is that part 'upto' the programming. Making sure that the algorithms you will be using are appropriate, that the tables you're using are normalised, ensuring that your design doesn't need to be reworked when a new module is to be included, etc.
The worrying trend I see is that many computer engineering graduates are interested in only learning a large set of programming languages but dislike courses like algorithm design, not realising that these languages are merely tools for implementing solutions. The end result is what you could call technicians but not engineers.
I think students are interested in learning languages because employers at interviews ask, "Do you know the X language?" That's especially true for entry-level, non-PhD candidates.
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