An algorithm's memory usage is the storage it occupies under a specified accounting convention. Total storage can include input and output, while auxiliary space complexity counts extra working memory; recursion stacks, temporary arrays and cached results must be included when relevant. An iterative binary search needs constant auxiliary storage, but a recursive implementation can keep logarithmically many stack frames. Reporting memory without specifying whether counts bits, elements or machine words can hide a substantial difference. Peak live storage and cumulative allocated storage are also different measurements.
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