>
>
> That's quite an interesting idea. I do think a lot of production Python
> code implicitly depends on the GIL and would need rework for multicore.
> For example, code that expects "n += 1" to be atomic, because the
> CPython bytecode interpreter won't switch threads in the middle of it.
> --
On 4/2/11 2:05 PM, John Nagle wrote:
There's no easy way to speed up Python; that's been tried.
It needs either a very, very elaborate JIT system, more complex
than the ones for Java or Self, or some language restrictions.
The main restriction I would impose is to provide a call that says:
"OK,
John Nagle writes:
> There's no easy way to speed up Python; that's been tried.
> It needs either a very, very elaborate JIT system, more complex
> than the ones for Java or Self, or some language restrictions.
Is it worse than Javascript? Tracemonkey and its descendants produce
pretty fast
On Sat, Apr 2, 2011 at 12:05 PM, John Nagle wrote:
> On 4/2/2011 3:30 AM, Stefan Behnel wrote:
>
>> Cython actually supports most Python language features now (including
>> generators in the development branch), both from Python 2 and Python 3.
>> Chances are that the next release will actually c
On 4/2/2011 3:30 AM, Stefan Behnel wrote:
Cython actually supports most Python language features now (including
generators in the development branch), both from Python 2 and Python 3.
Chances are that the next release will actually compile most of your
Python code unchanged, or only with minor ad
Steven D'Aprano, 02.04.2011 12:04:
On Fri, 01 Apr 2011 17:45:39 +0200, Stefan Behnel wrote:
Steven D'Aprano, 01.04.2011 14:57:
I suggest you check out the competitors:
Shedskin is a Python to C++ compiler; Psyco is a JIT specialising
compiler; Nuitka claims to be a C++ implementation that com
On Fri, 01 Apr 2011 17:45:39 +0200, Stefan Behnel wrote:
> Steven D'Aprano, 01.04.2011 14:57:
>> I suggest you check out the competitors:
>>
>> Shedskin is a Python to C++ compiler; Psyco is a JIT specialising
>> compiler; Nuitka claims to be a C++ implementation that compiles to
>> machine code;
Thanks for all the replies. I wasn't aware of some of these
alternatives. Most of these seem to transform Python code/bytecode into
another language. I was already well aware of Cython. On the Nuitka
blog, I notice it says "Compiling takes a lot [sic] time, ...". Compyler
seems to generate asse
Steven D'Aprano, 01.04.2011 14:57:
I suggest you check out the competitors:
Shedskin is a Python to C++ compiler;
Psyco is a JIT specialising compiler;
Nuitka claims to be a C++ implementation that compiles to machine code;
Berp claims to be a Haskell implementation that does the same;
Compyler
On Thu, 31 Mar 2011 18:33:36 -0400, Rouslan Korneychuk wrote:
> I'm aware that PyPy already has a working JIT compiler, but I figure it
> will be a long time before they have a version of Python that is ready
> for everybody to use, so this could be useful in the mean time.
PyPy is ready to use *
On Thu, Mar 31, 2011 at 5:52 PM, Terry Reedy wrote:
> On 3/31/2011 6:33 PM, Rouslan Korneychuk wrote:
>
>> I was looking at the list of bytecode instructions that Python uses and
>> I noticed how much it looked like assembly. So I figured it can't be to
>> hard to convert this to actual machine c
On 3/31/2011 6:33 PM, Rouslan Korneychuk wrote:
I was looking at the list of bytecode instructions that Python uses and
I noticed how much it looked like assembly. So I figured it can't be to
hard to convert this to actual machine code, to get at least a small
boost in speed.
And so I whipped up
Rouslan Korneychuk, 01.04.2011 00:33:
I was looking at the list of bytecode instructions that Python uses and I
noticed how much it looked like assembly. So I figured it can't be to hard
to convert this to actual machine code, to get at least a small boost in
speed.
I think I recall having read
I was looking at the list of bytecode instructions that Python uses and
I noticed how much it looked like assembly. So I figured it can't be to
hard to convert this to actual machine code, to get at least a small
boost in speed.
And so I whipped up a proof of concept, available at
https://git
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