[Phillip J. Eby]
>> Still, it's rather interesting that tuple.__contains__ appears slower than a
>> series of LOAD_CONST and "==" operations, considering that the tuple
>> should be doing basically the same thing, only> without bytecode fetch-and-
>> decode overhead. Maybe it's tuple.__contains__ that needs optimizing
>> here?
[Fredrik Lundh]
> wouldn't be the first time...
How soon we forget <wink>. Fredrik introduced a pile of optimizations
special-casing the snot out of small integers into ceval.c a long time
ago, like this in COMPARE_OP:
case COMPARE_OP:
w = POP();
v = TOP();
if (PyInt_CheckExact(w) && PyInt_CheckExact(v)) {
/* INLINE: cmp(int, int) */
register long a, b;
register int res;
a = PyInt_AS_LONG(v);
b = PyInt_AS_LONG(w);
switch (oparg) {
case PyCmp_LT: res = a < b; break;
case PyCmp_LE: res = a <= b; break;
case PyCmp_EQ: res = a == b; break;
case PyCmp_NE: res = a != b; break;
case PyCmp_GT: res = a > b; break;
case PyCmp_GE: res = a >= b; break;
case PyCmp_IS: res = v == w; break;
case PyCmp_IS_NOT: res = v != w; break;
default: goto slow_compare;
}
x = res ? Py_True : Py_False;
Py_INCREF(x);
}
else {
slow_compare:
x = cmp_outcome(oparg, v, w);
}
That's a hell of a lot faster than tuple comparison's deferral to
PyObject_RichCompareBool can be, even if we inlined the same blob
inside the latter (then we'd still have the additional overhead of
calling PyObject_RichCompareBool). As-is, PyObject_RichCompareBool()
has to do (relatively) significant work just to out find which
concrete comparision implementation to call.
As a result, "i == j" in Python source code, when i and j are little
ints, is much faster than comparing i and j via any other route in
Python. That's mostly really good, IMO -- /F's int optimizations are
of major value in real life. Context-dependent optimizations make
code performance less predictable too -- that's life.
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