I’m just curious about which is the most efficient way of doing this kind of node enumiration:

for i in something():
    o=[var1,var2,var3,varN][i]
    o.new()
    o.do_something_based_on_number_of_loops()
    add_child(o)

or

for i in something():
    match i:
        0:
            o=var1
            o.new()
            o.do_something_based_on_number_of_loops()
            add_child(o)
        1:
            o=var2
            o.new()
            o.do_something_based_on_number_of_loops()
            add_child(o)
        2:
            o=var3
            o.new()
            o.do_something_based_on_number_of_loops()
            add_child(o)
        N-1:
            o=varN
            o.new()
            o.do_something_based_on_number_of_loops()
            add_child(o)

or

var items = [var1,var2,var3,varN]
for i in something():
    o=items[i]
    o.new()
    o.do_something_based_on_number_of_loops()
    add_child(o)

Or is there a more efficient way of doing it?

Edit: Sorry if that wasn’t clear. Is it better to constantly get something from an “unstored list”, store the list in a variable, or not use a list and use a match statement instead? Do they have any advantages/disadvantages that make them better in certain situations?

  • @Walnut356@programming.dev
    1•9 months ago

    I did not dismiss it, I said measure the performance yourself.

    Then why does anyone ask anything? Just figure it out yourself. Oh you read a book or went to college? Why? Should have just reinvented computers yourself man. Taking advantage of collective knowledge is for suckers /s

    This means that the code performance is highly dependent on runtime conditions, and needs to be measured in the place where it’s used.

    How is that helpful for OP? For example, if his question was about rust i’d say “options 1 or 2 should be identical for speed”, but if it’s python i’d say “match statements are just chained if/else chains, so a direct array index would be faster”. For another example, in python attribute access is a function call. x.y in a loop is slower than assigning z = x.y outside of the loop and calling z in the loop.

    You can absolutely generalize and have rules of thumb for performance.

    If they already measured, then they would know which one is faster, because they measured it.

    Measurements can have unintuitive results based on the dataset used (which, for benchmarks, usually end up being artificial datasets). OP’s measurements may not have been consistent with their working understanding, thus they ask outside sources to confirm the truth. Idk why you cant just give them the benefit of the doubt and like… answer the question they actually asked? The explanations for “why” that accompany that answer can also be incredibly helpful.

    And all of these optimizations are just as effective after you measure them to see if they’re needed, and they’re no longer premature.

    That implies that these optimizations are harder than doing it the “mundane” way. They’re not.

    Here’s a fun micro optimization for compiled languages: on modern CPUs

    x = x * (arr[0] * arr[1]) in a loop has better performance characteristics than

    x = (x * arr[0]) * arr[1]

    even though they do the same thing (in short, it’s because of the data dependencies for out-of-order execution - compilers wont make this optimization automatically for floats). How much harder is it to write the first one compared to the second one? How much harder to read is the first compared to the second?

    So why would you not just make the first one your default? Now all your future uses of that pattern will perform better, for no extra effort except the amortized cognitive fee of changing your default option.

    Look at OPs question. Could the answer fall under a rule of thumb that they can apply as their default option for a scenario? I’m pretty sure it can. So who cares about “premature” or not?

    The particular question asked by the OP is very very unlikely to have any significant performance impact at all, unless it’s in an extremely hot loop running millions of times per frame

    So instead of answering their question, you assume it isnt impacting performance and they’re just asking for no reason?

    I literally had this exact question about python like 8 months ago. I had a file parser that needed to process different chunks based on a tag. Performance was critical, several thousand files, each ~3mb), the tag dispatch happened about 100,000-300,000 times per file. The original was implemented with if/else. I switched it to match because i thought it was faster, it wasnt. I looked at a lot of threads with answers like yours until stumbling upon dictionary dispatch (i.e. key = tag, value = first class function to call on the tag’s data) and array dispatch.

    That change alone was a 15% performance improvement.

    You have no idea how their program works, what their hot loop is, if they’re just asking out of curiosity, whatever. Just answer their fuckin question my dude. Platitudes are a waste of everyone’s time.

    • @mcribbs@lemmy.world
      2•9 months ago

      I’m with you. “Measure it yourself” is a bit of a non-answer. Sure, being able to measure things yourself is also a useful skill, but asking questions and having people with experience answer them is largely the point of places like this.