Is python not good enough?
steve at holdenweb.com
Sun Jan 17 00:03:18 CET 2010
John Nagle wrote:
> Nobody wrote:
>> On Fri, 15 Jan 2010 12:34:17 -0800, John Nagle wrote:
>>> Actually, no. It's quite possible to make a Python
>>> implementation that
>>> runs fast. It's just that CPython, a naive interpreter, is too
>>> to do it. I was really hoping that Google would put somebody good at
>>> compilers in charge of Python and bring it up to production speed.
>>> Look at Shed Skin, a hard-code compiler for Python
>> A hard-code compiler for the subset of Python which can easily be
>> Shed Skin has so many restrictions that it isn't really accurate to
>> describe the language which it supports as "Python".
>> Hardly any real-world Python code can be compiled with Shed Skin. Some of
>> it could be changed without too much effort, although most of that is the
>> kind of code which wouldn't look any different if it was implemented in
>> C++ or Java.
>> The monomorphism restriction is likely to be particularly onerous: the
>> type of a variable must be known at compile time; instances of subclasses
>> are allowed, but you can only call methods which are defined in the
>> compile-time class.
>> If you're writing code which makes extensive use of Python's dynamicity,
>> making it work with Shed Skin would require as much effort as re-writing
>> it in e.g. Java, and would largely defeat the point of using Python in
>> first place.
>> If you want a language to have comparable performance to C++ or Java, you
>> have to allow some things to be fixed at compile-time. There's a reason
>> why C++ and Java support both virtual and non-virtual ("final") methods.
> My point is that Python is a good language held back by a bad
> implementation. Python has gotten further with a declaration-free syntax
> and declarations had to be retrofitted. Python has survived without them.
> (Yes, there are hokey extensions like Psyco declarations and "decorators",
> but both are marginal concepts.)
> The key to hard-compiling Python is that you have to compile the
> whole program, not individual modules. You can't tell how an individual
> module will be used until you've seen its callers. If the compiler
> looks at the whole program at once, type inference has a good chance of
> disambiguating most type issues.
> If you can see the whole program at once, most dynamism can be
> What's really needed is to detect the most common case, where objects don't
> have unexpected dynamism and can be implemented as hard structures.
> John Nagle
Of course, Guido has left the path to declarations open through the use
of function argument annotation. If you wanted to write programs that
reasoned about Python programs to optimize them, annotations could come
in very useful.
Steve Holden +1 571 484 6266 +1 800 494 3119
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