[nossralf]

If the goal of the competition is to slice apples, then a knife is pretty good compared to a gun. Or if the competition takes place under water. Or if you're Steven Seagal and the ship where you work is taken over by a splendidly over-acting Tommy Lee Jones and his gun-wielding minions.

Execution speed is nice, I don't disagree. But it's not always the be-all and end-all deciding factor for what tool to use to solve a problem in a specific context.

Competitions can be stressful, and choosing a tool that feels like an extension of yourself (be that Ruby, COBOL or whatever) is, to me, the better route. Compared to something that executes faster on your computer but not in your head.

[georgemcbay]

I don't think the parent post (which I totally agree with) was saying that you must always decide by picking an inherently fast language, but rather if you decide to pick one whose execution time is not inherently fast to enter a competition where execution time is factored you will start with a major disadvantage.

That doesn't mean you'll lose (ala your Steven Seagal analogy), maybe you're so much better than everyone else that you can overcome the disadvantage, but you'll surely be starting in a bad position compared to someone holding a 'gun' even if he or she is somewhat less skilled at using the gun than you are at using the knife.

[entropy_]

My experience with GCJ has been that raw execution speed almost never matters as long as you have the right algorithm.

In the case of the OP, for example a trivial optimization(the kind you do without even thinking in these competitions) would've brought his execution time down 10000x. Basically, you are asked about the number of "fair and square" numbers in a certain range, over and over again, in 10000 different cases. He could have just taken his solution, pre-generated all those numbers once and then ran all those test cases by counting in that pregenerated list, instead of generating it for every single test case(all 10^5 of them). It turns out, that in the whole span of 1->10^14 there is only 39 such numbers and it would've taken his program probably less than a minute to generate all of them(I'm being generous here, since it ran 10^5 test cases in 53min).

In fact, if you read the problem analysis later provided by google you will see that in fact you were expected to make this optimization to solve the problem. The fact that C-small had 100 test cases, C-large-1 had 10^5 and C-large-2 had 100 should've been a strong hint that C-large-1 required some pre-caching across test cases.

[nossralf]

Well sure, adding "where execution time is factored" makes it hard to disagree with you. (And ultimately, it usually becomes a factor sooner or later.)

That wasn't the context in which I was writing my original reply, however. I was responding to a post that mentioned "magic" as a reason to disregard Ruby for competitions, which I didn't agree with. (The Ruby solution in the submission was slow even using a while loop, which is far from magic.)

If you're able to solve problems in the same amount of time using a fast and a not so fast language, of course you pick the faster one in a competition. I'm not arguing against that.

[PySlice]

> bring a knife to a gun fight

> If the goal of the competition is to slice apples

What?

[nossralf]

I disagree with the premise in a similarly nonsensical fashion. (I mentioned neither execution performance nor weapons in my reply.)

The ultimate goal in a competition is to solve the problem within a fixed amount of time. This requires you to actually implement the solution to be able to run it. If you are able to do that more easily with a language that is inferior in execution speed, that will probably be the best strategy. At least compared to using a really fast language that you are not as comfortable with.