> is big-O an appropriate way of measuring in this context?
I don't have formal CS education. I'm less interested in math and more oriented towards practical aspects. Infinity doesn't exist in practice (except these IEEE float magic values), but we can reason about shape of functions, e.g. "count of executed machine instructions as a function of N".
Not the same thing as CS-defined big O, but close.
Can be useful to reason about complexity of algorithms. More precisely, about complexity of their implementations.
Usually useless to reason about time: a machine instruction takes variable time to run on a modern CPU, the same instruction can take less than 1 cycle or much more than 1M cycles.
I don't have formal CS education. I'm less interested in math and more oriented towards practical aspects. Infinity doesn't exist in practice (except these IEEE float magic values), but we can reason about shape of functions, e.g. "count of executed machine instructions as a function of N".
Not the same thing as CS-defined big O, but close.
Can be useful to reason about complexity of algorithms. More precisely, about complexity of their implementations.
Usually useless to reason about time: a machine instruction takes variable time to run on a modern CPU, the same instruction can take less than 1 cycle or much more than 1M cycles.