Tag Archives: computation

Asynchronicity Traps

Don’t you love it when lack of planning comes together?

Last Thursday, I learned about JavaScript callbacks, which reminded me of OpenVMS Asynchronous Traps (ASTs.) I learned to code in a world of single-threaded processing, so this was an advanced topic, along with my interest in occam and Communicating Sequential Processes. Back then, only real-time coders and those looking to the future cared about parallelism. I remember not really seeing the point of Yourdon ‘state-diagrams’. I’d never experienced the complex state network that a GUI with a few option buttons can generate.

Last night I came across debates about the advisability of abandoning JS callbacks for the HTML5 ‘promise’ construct; “callback hell”, they called it. Promises are functions. This is another area where the elegant simplicity of functional programming appears to offer hope. Functions are mathematical constructs, so in functional languages perhaps all possible states that code might enter can be identified.

Alongside this, I’ve been reading about research into the energy requirements of computation. For a long time, computer scientists thought that every logic operation would have a cost in terms of energy and hence entropy, but that appears not to be true. It is information deletion that costs energy, so immutable data is more energy efficient. I’m only up (down?) to quantum bits, so I’ll have to let you know how the cat gets on another day. I worry when physics starts to look like mystical religions.

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Software Life-cycle. Part 2 – From Craftsmanship to Computational Science

I decided to learn the programming language Python. I was steered towards the MIT OpenCourseware ‘Introduction to Computer Science and Programming’ 6.00 course, taught by Prof. Eric Grimson and Prof. John Guttag (they say it is a course about computational thinking.)

http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-00-introduction-to-computer-science-and-programming-fall-2008/video-lectures/

As the first lecture felt a bit basic, at great personal risk of uncovering ‘a spoiler’, I skipped on to the course summary in the last lecture to see if it was worth sticking around. I found it inspirational. Prof. John Guttag explains computation in the context of ‘The Scientific Method’. I’ve since realised that his explanation maps with great accurately onto Agile iterative methods. Agilists aren’t engineers, we’re scientists again. Engineering Project Management uses experience of similar previous projects. Why would you ever write similar software twice? Most of the work is already done. Every change to a computational system should be R&D.

Every Scrum Sprint is a suite of computational experiments. The Product Owner is our test subject. This feels right. I never felt like a computer scientist. In James Gleich’s ‘The Information’, he explains that Alan Turing introduced Babbage’s mechanical Difference Engine when talking to non-specialist, to emphasise that computing is an abstract concept, independent from computers and electricity. I’ve always had a computational scientist trying to get out <Woo asplodes>.