It seems that JIT is overloaded with at least 2 meaning.
The canonical definition of JIT is "compilation during execution of a program". Usually, a program is being interpreted first, then switches to compiled code in the middle of execution. This is not what this article does.
What this article does is sometimes called on-the-fly AOT, or just on-the-fly compilation. I'd prefer not overloading the term "JIT".
I always took the distinction to be the executable memory bit as opposed to writing an executable and then launching it in the usual way. Of course high performance runtimes that contain JITs do typically interpret first in order to get type information and reduce startup latency, but that’s more a function of the language not being efficiently AOT compileable rather than fundamental to the concept of a JIT
Following is the closed form solution linked in the article (from a since-deleted Reddit comment):
from functools import reduce
def recurrence(ops, a0, n):
def transform(x, op):
return eval(repr(x) + op)
ops = ops.split()
m = reduce(transform, [op for op in ops if op[0] in ('*', '/')], 1)
b = reduce(transform, ops, 0)
for k in range(n + 1):
print('Term 0: ', a0 * m ** k + b * (m ** k - 1) / (m - 1))
> This is really only interesting if a particular (potentially really large) term of the sequence is desired, as opposed to all terms up to a point. The key observation is that any sequence of the given set of operations reduces to a linear recurrence which has the given solution.
The canonical definition of JIT is "compilation during execution of a program". Usually, a program is being interpreted first, then switches to compiled code in the middle of execution. This is not what this article does.
What this article does is sometimes called on-the-fly AOT, or just on-the-fly compilation. I'd prefer not overloading the term "JIT".