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In the UK at least, humanities folk can no longer afford to ask such questions – the logic of efficiency has found the humanities wanting. Will there be anyone left to ask which came first, the dogma of economic efficiency or the dogma of computational efficiency?

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I think that much of the move towards high level languages and development frameworks over the last few decades has been precisely about trading slower program runtimes for greater programmer efficiency and expressiveness.

In some cases the trade-off isn’t even inevitable. I’m reminded of an anecdote from Steve Yegge’s 2008 Google Talk “Dynamic Programming Languages Strike Back”, in which he’s explaining why he thinks his first employer went out of business after Microsoft’s “slow”, high-level framework out-performed their hand-tuned low-level assembly:

[I]t’s because we wrote fifteen million lines of 8086 assembly language. We had really good tools, world class tools: trust me, you need ‘em. But at some point, man…

The problem is, picture an ant walking across your garage floor, trying to make a straight line of it. It ain’t gonna make a straight line. And you know this because you have perspective. You can see the ant walking around, going hee hee hee, look at him locally optimize for that rock, and now he’s going off this way, right?

This is what we were, when we were writing this giant assembly-language system. Because what happened was, Microsoft eventually released a platform for mobile devices that was much faster than ours. OK? And I started going in with my debugger, going, what? What is up with this? This rendering is just really slow, it’s like sluggish, you know. And I went in and found out that some title bar was getting rendered 140 times every time you refreshed the screen. It wasn’t just the title bar. Everything was getting called multiple times.

Because we couldn’t see how the system worked anymore!

Small systems are not only easier to optimize, they’re possible to optimize. And I mean globally optimize.

So when we talk about performance, it’s all crap. The most important thing is that you have a small system. And then the performance will just fall out of it naturally

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(This begins a series of posts on the rudimentary elements of computing, inspired by Matthew Fullers Software Studies book and a review of an Introduction to Computer Science Course.)

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On Efficiency

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Efficiency is more than the end of computational practice, more than a God, more than an exquisite delight, it is the chimera, the holy grail, the pwnership, the rad ollie, the be all and end all of most programming challenges for to find the more efficient solution is to find the better solution.

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Of course, claims of progress, efficiency, and one dominant realm of evaluation lead me (as a good humanities scholar) to doubt the universality of the criteria.

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That said, Intro to Computer Science courses stress anticipating (and calculating) Worst Case Scenarios. How many operations might this program have to execute to reach the same or an equivalent answer. Is it the whopping, exponential n^2 or the beautiful, much-slighter slope of n*log(n)

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The difference is money. The difference is time. The difference is the possibility or probability that the program would process all day and night without ever stopping for a break or coming up with a solution.
Put such a program inside your traffic light. Put such a program in your tax software. Do you need to go? Do you need to drive?

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Yet, somehow, I cant help but wonder if slower is not sometimes better? (Humanities folk can afford to ask such questions.) Could there not be algorithms that do a better job by including more processing cycles? Such a naive question, I know.

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Where else is efficiency not just the fetish but an ultimate standard? Engineering? No doubt? Law, not so much.
Again, obfuscation exercises only reinforce this rule.

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So whenever we look at code, we evaluate code, we are measuring it according to how efficient it is and all the code it saved.

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How does that effect CCS?

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We can approach software with a sense that one of the paradigms brought to this problem was to maximize efficiency. When reading code, we can examine it against that paradigm. More importantly, we can come to interpret efficiency and what that means for a particular process. We can also take into account that code is not merely the written lines but what those lines of code do when they execute. A program could be five lines long, but 2 of those lines could be iterated hundreds of thousands of time.

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More to come on this.

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