In general, this is not a language issue. Python is not “failing”, nor is Julia. You’ll get exactly the same thing if you just use + in any language using double-precision floating-point arithmetic (the default precision in most languages).
As another example, sum([1e-100, 1.0, 1e-100, -1.0]) also gives 0.0 — it is exactly the same problem, just rescaled, in which the exactly rounded answer is 2e-100. Is this a big error or a small error?
The key question is, compared to what.
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It is a big error (100%) compared to the correct sum (i.e. the relative forward error). That’s because we are computing an ill-conditioned function (a sum with a catastrophic cancellation), so the relative error in the output is large even for tiny roundoff errors. You have to be careful when doing anything ill-conditioned in finite precision.
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It is a small error compared to the magnitude of the inputs, i.e. the typical scale of the problem we are giving it (
1e100in your example,1.0in mine). A more precise way of saying this is that the “backwards relative error” is small.
Summation is the easiest case to analyze, and the easiest for which you can write exactly rounded algorithms like fsum or xsum that do the computation in effectively infinite precision, as well as nearly perfect algorithms like compensated summation. But this simplicity is a two-edged sword — it also means that summation perhaps gets disproportionate attention, when in fact ill-conditioning and similar problems are something you have to be aware for anything in finite-precision arithmetic.