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Asked: 2026-05-13T08:38:41+00:00

In general use, should I bet on memory efficiency or processor efficiency? In the

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In general use, should I bet on memory efficiency or processor efficiency?

In the end, I know that must be according to software/hardware specs. but I think there’s a general rule when there’s no boundaries.

Example 01 (memory efficiency):

int n=0;   
if(n < getRndNumber())
    n = getRndNumber();

Example 02 (processor efficiency):

int n=0, aux=0;
aux = getRndNumber();
if(n < aux)
    n = aux;

They’re just simple examples and wrote them in order to show what I mean. Better examples will be well received.

Thanks in advance.

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  1. Editorial Team

    I’m going to wheel out the universal performance question trump card and say “neither, bet on correctness”.

    Write your code in the clearest possible way, set specific measurable performance goals, measure the performance of your software, profile it to find the bottlenecks, and then if necessary optimise knowing whether processor or memory is your problem.

    (As if to make a case in point, your ‘simple examples’ have different behaviour assuming getRndNumber() does not return a constant value. If you’d written it in the simplest way, something like n = max(0, getRndNumber()) then it may be less efficient but it would be more readable and more likely to be correct.)

    Edit:

    To answer Dervin’s criticism below, I should probably state why I believe there is no general answer to this question.

    A good example is taking a random sample from a sequence. For sequences small enough to be copied into another contiguous memory block, a partial Fisher-Yates shuffle which favours computational efficiency is the fastest approach. However, for very large sequences where insufficient memory is available to allocate, something like reservoir sampling that favours memory efficiency must be used; this will be an order of magnitude slower.

    So what is the general case here? For sampling a sequence should you favour CPU or memory efficiency? You simply cannot tell without knowing things like the average and maximum sizes of the sequences, the amount of physical and virtual memory in the machine, the likely number of concurrent samples being taken, the CPU and memory requirements of the other code running on the machine, and even things like whether the application itself needs to favour speed or reliability. And even if you do know all that, then you’re still only guessing, you don’t really know which one to favour.

    Therefore the only reasonable thing to do is implement the code in a manner favouring clarity and maintainability (taking factors you know into account, and assuming that clarity is not at the expense of gross inefficiency), measure it in a real-life situation to see whether it is causing a problem and what the problem is, and then if so alter it. Most of the time you will not have to change the code as it will not be a bottleneck. The net result of this approach is that you will have a clear and maintainable codebase overall, with the small parts that particularly need to be CPU and/or memory efficient optimised to be so.

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