Needless copying in iterations?
steve at REMOVE-THIS-cybersource.com.au
Mon Sep 17 00:48:19 CEST 2007
On Sun, 16 Sep 2007 11:49:15 -0400, Steve Holden wrote:
>> It seems to me that the "consenting adults" philosophy of Python
>> doesn't extend to the compiler, and perhaps it should. Maybe Python
>> could optimize common cases, and if developers wanted to do wacky
>> things, let them turn optimization off on a module-by-module basis.
>> Or even function-by-function. Wouldn't it be nice to have decorators
>> that could optimize the functions they decorated?
> No, it would be disastrous,
Disastrous, as in thousands dead, panic in the street, martial law
declared? I'm sure your program in important, but is it really *that*
> unless you could manage to implement a
> mechanism that *told* you when the optimizations were playing havoc with
> your program's execution.
How about "the test suite passes when I turn optimization off, but fails
when I turn optimization on"? How is that any different from "the test
suite passes when I use StringIO, but not if I use cStringIO"?
> The problem is that debugging only works if you can assume a
> deterministic environment. If you are going to put funky optimizations
> in that break determinism in "little-used" corner cases, then debugging
> the situations when the optimizations *do* affect program execution will
> be a complete pain.
Right, so we're back to the "any pixel is as likely as any other pixel"
example from Joel On Software I mentioned earlier. To prevent some
hypothetical developer doing some wacky thing from having to work harder
to debug some weird corner case, *everybody else* has to miss out on
optimizations that could lead to significantly better performance.
You might think that's a good trade-off. I don't.
>>>> for item in alist[1:5]:
>>>> alist.append(item) # side-effects DON'T matter
>>> The compiler doesn't know that, at the time of invocation,
>>> 'alist.append' doesn't have side effects that matter.
>> It might if it knows what type alist is, and how long it is (as Marc
>> Rintsch points out). That's why I used "alist" in my example rather
>> than "an_arbitrary_sequence".
> But alist doesn't *have* a type, and static program analysis would have
> to be extensive to determine that it could only ever be of a specific
Of course alist has a type. Python is a strongly-typed language, not
And I don't believe I specifically mentioned that static program analysis
was the only optimization possible. But even if it was...
I can *hand optimize* a function that I write, yes? I might *choose* to
say to myself, "Screw duck typing, in *my* program function foo will only
ever be called with an honest-to-god built-in list argument, I'm going to
optimize it for that case". We're all consenting adults here, right?
Well, what's the difference between me hand optimizing the function, and
calling a built-in decorator that does it for me? Why is it okay for me
to shoot myself in the foot, but not for me to tell the compiler to shoot
me in the foot?
>> Because of its very nature, optimizing Python is hard. Even something
>> as simple as tail-call recursion optimization is verboten, because
>> Guido considers good stack traces more important than preventing stack
>> overflows in the first place:
> And who's to say he's wrong, especially since Python is intended to be
> an environment that's friendly to beginners.
I'm not saying he's *wrong*, I'm saying he's making trade-offs I would
like to see relaxed.
But in the specific case of recursion... have you seen the exception you
get when the recursion limit is hit? By default, you get 1000 lines of '
File "foo", line 2, in function_name' followed by "RuntimeError: maximum
recursion depth exceeded". Maybe I'm missing something there, but I don't
see how that's much of a help in debugging.
>> Anyway, I'm not really complaining. I like Python, I'm grateful for the
>> work the Python-dev people have already done, and I'm in no position to
>> volunteer to write an optimizing compiler. And it may be that,
>> regardless of how smart you make the compiler, Python simply can't be
>> optimized because of design decisions made back in 1990-whatever when
>> Guido first started his grand plan.
> There is some of that. The environment is so dynamic that even something
> as innocuous as attribute lookup can actually involve properties (for
> example) reading a relational database! I'm not saying that optimization
> under these circumstances is possible, but that it's much more difficult
> than is easily anticipated, and that the benefits to be gained might be
> less than you would think.
I'm mostly objecting to people who say that Python's compiler "can't" be
optimized, which is not true. As I mentioned, there's already a peephole
optimizer. There are runtime optimizations possible. Psycho could become
a built-in, if the right people wanted it to become a built-in. Etc. With
a few million in funding, Python could become as heavily optimized as
Of course, whether that would still be the Python we know *now* is
another question. But then, with decorators and generator expressions and
iterators, Python 2.6 is not precisely the same as the Python we knew
back in 1997, is it?
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