Comment by gdwatson

If we can write code to dispatch different code paths (like has been used for decades for SSE, later AVX support within one binary), then we can write code to parallelize large array execution based on heuristics. Not much different from busy spins falling back to sleep/other mechanisms when the fast path fails after ca. 100-1000 attempts to secure a lock.

For the trivial example of 2+2 like above, of course, this is a moot discussion. The commenter should've lead with a better example.

I mean, theoretically it's possible. A super basic example would be if the data is known at compile time, it could be auto-parallelized, e.g.

    int buf_size = 10000000;
    auto vec = make_large_array(buf_size);
    for (const auto& val : vec)
    {
        do_expensive_thing(val);
    }

If I replace it with int buf_size = 10000000; cin >> buf_size; auto vec = make_large_array(buf_size); for (const auto& val : vec) { do_expensive_thing(val); }

the compiler could generate some code that looks like: if buf_size >= SOME_LARGE_THRESHOLD { DO_IN_PARALLEL } else { DO_SERIAL }

With some background logic for managing threads, etc. In a C++-style world where "control" is important it likely wouldn't fly, but if this was python...

    arr_size = 10000000
    buf = [None] * arr_size
    for x in buf:
        do_expensive_thing(x)