Hello.
I am new to Python. It seems a very interesting language to me. Its
simplicity is very attractive.
However, it is usually said that Python is not a compiled but
interpreted programming language —I mean, it is not like C, in that
sense.
I am working on my PhD Thesis, which is about Operations Research,
heuristic algorithms, etc., and I am considering the possibility of
programming all my algorithms in Python.
The usual alternative is C, but I like Python more.
The main drawbacks I see to using Python are these:
* As far as I understand, the fact that Python is not a compiled
language makes it slower than C, when performing huge amounts of
computations within an algorithm or program.
* I don't know how likely it is to find libraries in Python related to
my research field.
* I know Python is a "serious" and mature programming language, of
course. But I do not know if it is seen as "just funny" in a research
context. Is Python considered as a good programming language for
implementing Operations Research algorithms, such as heuristics and
other soft-computing algorithms?
Maybe this is not the right forum, but maybe you can give me some
hints or tips...
Thank you in advance.
Jun 27 '08
53  5256 
On May 20, 7:24 pm, Grant Edwards <gra...@visi.co mwrote:
>
http://docs.python.org/lib/module-ctypes.html
Also see Cython (or Pyrex if you prefer the original). With Cython it
is easy to call C functions, but Cython also alleviates the need for C
to a great extent. The advantage of Cython over C + ctypes is of
course the readability of Python and the presence of Python built-in
types like strings, dicts and lists. Unfortunately, it is still a bit
difficult to use NumPy ndarrays with Cython or Pyrex. NumPy ndarrays
work very well with ctypes though. http://www.cython.org/
Wow this resulted in far more reactions than I had expected ... br************* ****@gmail.com schreef:
On 19 mai, 15:30, Roel Schroeven <rschroev_nospa m...@fastmail.f m>
wrote:
>Bruno Desthuilliers schreef:
>>1/ being interpreted or compiled (for whatever definition of these
terms) is not a property of a language, but a property of an
implementatio n of a language.
2/ actually, all known Python implementations compile to byte-code.
You keep saying that, and in theory you're right.
"In theory" ??? Heck, both points above are mere facts. Well, I may
accept that the 2nd one is a bit overgeneralized , since IIRC there's
an experimental Python to javascript "compiler" in Pypy, but...
> But I'm still inclined to disagree with it, since the practical reality is different.
Do you mean that how source code written in a language (that is : a
grammar + a syntax) finally become a set of instructions executed by
a CPU depends on the language (I repeat : a grammer + a syntax), and
not on a piece of software turning the source code into something that
can actually be executed by the CPU ?
No, that's not what I said; what I said is that some languages where
designed with in the back of the head the idea that they were going to
be compiled to native code, others to be interpreted, and others to be
compiled to byte code.
Wikipedia says about C that "its design goals were for it to be compiled
using a relatively straightforward compiler, provide low-level access to
memory, provide language constructs that map efficiently to machine
instructions, and require minimal run-time support". To me, that very
strongly suggests that it was meant to be compiled to native code. It's
called "portable assembly" for a reason. You *can* make it work in
another way, and I suppose that it *is* done, but those implementations
are far in the minority.
As for Python, until the advent of PyPy all implementations I known used
a virtual machine (CPython, Jython, IronPython). And PyPy is still
experimental as far as I know.
So yes, the transformation method from source code to something that the
CPU understands depends on your tools. But if you want to get work done,
the most common method by far for C is to use a toolchain that compiles
to native code and for Python a byte code compiler + virtual machine.
With possibly a JIT compiler, that's true.
>Python is
indeed compiled to byte code, but if you compare that byte code with
assembly code you'll see that there's a whole world of difference
between the two,
Obviously, yes - at least for all assembly language I've seen so far.
But whoever said otherwise ?
Whenever someone says that Python is interpreted, you respond saying
that that's not true, since it's compiled to byte code. Correct of
course, but somehow it appears to me that you imply that that makes
Python closer to a C-like language than to an interpreted language, and
that's not correct (IMO). If that's just a misinterpretati on by me, I
apologize.
> largely because of the dynamical nature of Python. Fact
is that Python was designed from the start to run on a virtual machine,
not on the native hardware.
Nope. The facts are that
1/ Python (the language) has *not* been designed with ease of
implementation of an optimizing native-code compiler in mind, and
2/ CPython (the first and reference implementation) has been designed
to use a byte-code + VM scheme
Isn't that more or less the same as what I said?
Maybe I don't make enough distinction between Python the language and
CPython the implementation, but Python development does happen on the
CPython implementation (Python 3.0 alpha releases are CPython releases,
for example).
>
>C OTOH was designed to be compiled to assembly code (or directly to
machine code)
Note quite. C has been designed to make it as easy as possible to
write either a C to assembly or C to native binary code compiler.
I find it hard to believe that during the development of C Dennis
Ritchie was considering any other mode of operation than compilation to
assembly or machine code. I might be wrong of course.
>and as a result there are no (or virtually) no
implementation s that interpret C or compile it to bytecode.
There's at least one (possibly incomplete) C interpreter.
I'd like to call that the exception that confirms the rule.
There's a very naive belief we saw every here and then here, which is
that "Python would be faster if it was compiled to native code". The
point is that, given Python's (as a language) extrem dynamism,
compiling it to native code wouldn't buy you much in terms of raw
performances. The problem is not with writing a native-code
compiler[1}, but with writing an *optimising* native-code compiler.
I admit I'm guilty of that belief. I know it's true what you say, but I
do have the more-or-less unconscious reflex 'compiled to native code
== fast'.
I'm just getting fed up with
this "Python is an interpreted and therefore slow language" non-
sense. Python is a language, and as such is neither slow nor fast nor
interpreted nor compiled nor <insert any implementation related stuff
here>. And while CPython is not blazingly fast for computation-heavy
stuff, it's not because it is "interprete d" - which it is not for a
strict definition of "interprete d", but anyway... - but because
*optimizing* execution of an highly dynamic language is nothing,
well, err, trivial.
So you are saying that CPython is relatively slow because Python is a
highly dynamic language. I know that CPython is not Python and Python is
not CPython, but there is a very strong association between the two and
therefore I think it's not really that much wrong to simplify that to
'Python is slow because it is a highly dynamic language (until proven
wrong by PyPy or another fast implementation' .
--
The saddest aspect of life right now is that science gathers knowledge
faster than society gathers wisdom.
-- Isaac Asimov
Roel Schroeven
In article <g0**********@s olaris.cc.vt.ed u>, brad <by*******@gmai l.comwrote:
>
Run time is not important until you are asked to scale to millions
or billions of users or computations or large data sets. I've seen
this first hand. Getting results back the same day or sooner may be
important. In cases such as this, I use C or C++... nothing else will
do. Nothing else is as fast. Although I always keep a py version around
for testing and for smaller tasks. Don't get me wrong, I love Python,
but there are times when nothing, but pure, hard speed will do.
YouTube?
--
Aahz (aa**@pythoncra ft.com) <* http://www.pythoncraft.com/
Need a book? Use your library!
On May 20, 3:37 pm, brad <byte8b...@gmai l.comwrote:
seen this first hand. Getting results back the same day or sooner may be
important. In cases such as this, I use C or C++... nothing else will
do. Nothing else is as fast.
Right. Tell that to the Fortran community. And by the way: http://latticeqcd.blogspot.com/2006/...nd-python.html
On 18 Mai, 00:32, Vicent Giner <vgi...@gmail.c omwrote:
Maybe this is not the right forum, but maybe you can give me some
hints or tips...
Thank you in advance.
If Python doesn't run in production systems execution speed doesn't
matter much. What actually matters when *developing* non-trivial algos
is to partition the dataflow because it might take just long to reach
a certain execution point that is of current interest - and it has to
be reached repeatedly. For each method call one might want to work
with pre-computed data that were serialized in a former run. Given
Pythons flexibility this shall be easy and so it is! The combination
of object pickling/unpickling and using decorators makes this a snap.
Just look at the following simple decorator:
def dump_data(func) :
def call(*args, **kwd):
res = func(*args, **kwd)
fl = open("dump_data .pkl", "w")
pickle.Pickler( fl, 0).dump(args[0])
return res
call.__name__ = func.__name__
call.__doc__ = func.__doc__
return call
and the corresponding loader:
def load_obj():
f = open("dump_data .pkl")
return pickle.Unpickle r(f).load()
One might decorate an arbitrary object method foo() with dump_data and
resume the activity of the object after foo has been called in another
run by restoring the object with load_obj.
One can further improve this by serializing the test driver when it is
implemented as a generator. This is non-standard though and requires
some work [1] of my own or Stackless Python [2] alternatively.
What I intended to make clear is that Python has many goodies as a
toolbox besides having lots of bindings and being well readable ( the
usual suspects when it comes to advocation in threads like these ).
[1] http://www.fiber-space.de/generator_...tor_tools.html
[2] http://www.stackless.com/
On May 19, 8:09 pm, Lou Pecora <pec...@anvil.n rl.navy.milwrot e:
In article <hifYj.43082$4B 6.9...@newsfe14 .ams2>,
Roel Schroeven <rschroev_nospa m...@fastmail.f mwrote:
Bruno Desthuilliers schreef:
1/ being interpreted or compiled (for whatever definition of these
terms) is not a property of a language, but a property of an
implementation of a language.
2/ actually, all known Python implementations compile to byte-code.
You keep saying that, and in theory you're right. But I'm still inclined
to disagree with it, since the practical reality is different. Python is
indeed compiled to byte code, but if you compare that byte code with
assembly code you'll see that there's a whole world of difference
between the two, largely because of the dynamical nature of Python. Fact
is that Python was designed from the start to run on a virtual machine,
not on the native hardware.
C OTOH was designed to be compiled to assembly code (or directly to
machine code) and as a result there are no (or virtually) no
implementations that interpret C or compile it to bytecode.
But how about this C/C++ interpreter. Dr. Dobbs article: http://www.ddj.com/cpp/184402054. Title and first two paragraphs:
Ch: A C/C++ Interpreter for Script Computing
Interactive computing in C
Ch is a complete C interpreter that supports all language features and
standard libraries of the ISO C90 Standard, but extends C with many
high-level features such as string type and computational arrays as
first-class objects.
If you still end up chasing pointers to implement your data structures
then its still hampered.
On May 17, 11:32 pm, Vicent Giner <vgi...@gmail.c omwrote:
Hello.
I am new to Python. It seems a very interesting language to me. Its
simplicity is very attractive.
However, it is usually said that Python is not a compiled but
interpreted programming language —I mean, it is not like C, in that
sense.
I am working on my PhD Thesis, which is about Operations Research,
heuristic algorithms, etc., and I am considering the possibility of
programming all my algorithms in Python.
The usual alternative is C, but I like Python more.
Using Python doesn't mean you give up on C! Many of the best
algorithms written in other languages such as C, C++ and Fortran are
pre-wrapped in Python or can be wrapped in a Python interface to
enhance usability, without having to pay Matlab-type prices. You then
make your resulting work easier to reproduce by lowering the cost to
other researchers.
Python is a scripting language. Despite what some may think, it is a
boon, as it means that Pythons designers value its ability to work
well with other languages and systems.
Nothing stops you from exploring algorithm space in Python then re-
implementing in a language closer to assembler - and this may well be
the quicker way to your goal.
- Paddy.
Roel Schroeven a écrit :
Wow this resulted in far more reactions than I had expected ...
br************* ****@gmail.com schreef:
>On 19 mai, 15:30, Roel Schroeven <rschroev_nospa m...@fastmail.f m>
wrote:
>>Bruno Desthuilliers schreef:
1/ being interpreted or compiled (for whatever definition of these
terms) is not a property of a language, but a property of an
implementati on of a language.
2/ actually, all known Python implementations compile to byte-code.
You keep saying that, and in theory you're right.
"In theory" ??? Heck, both points above are mere facts. Well, I may
accept that the 2nd one is a bit overgeneralized , since IIRC there's
an experimental Python to javascript "compiler" in Pypy, but...
>> But I'm still inclined to disagree with it, since the practical
reality is different.
Do you mean that how source code written in a language (that is : a
grammar + a syntax) finally become a set of instructions executed by
a CPU depends on the language (I repeat : a grammer + a syntax), and
not on a piece of software turning the source code into something that
can actually be executed by the CPU ?
No, that's not what I said; what I said is that some languages where
designed with in the back of the head the idea that they were going to
be compiled to native code, others to be interpreted, and others to be
compiled to byte code.
I'd put it more simply : some languages were designed with low-level
access and raw performances in mind, and some were'nt. Roel, I'm totally
aware of these issues - on which you're of course right -, but that
doesn't change the fact that a language and it's implementation *are*
distinct things.
(snip)
So yes, the transformation method from source code to something that the
CPU understands depends on your tools.
And you can have different tools using different solutions for a same
language.
But if you want to get work done,
the most common method by far for C is to use a toolchain that compiles
to native code and for Python a byte code compiler + virtual machine.
With possibly a JIT compiler, that's true.
>>Python is
indeed compiled to byte code, but if you compare that byte code with
assembly code you'll see that there's a whole world of difference
between the two,
Obviously, yes - at least for all assembly language I've seen so far.
But whoever said otherwise ?
Whenever someone says that Python is interpreted, you respond saying
that that's not true, since it's compiled to byte code.
Whenever someone says that Python is interpreted, I respond saying that
being interpeted or compiled is not a feature of a language, and that
CPython compiles to byte-code.
Correct of
course,
And that's the point : being correct.
but somehow it appears to me that you imply
I don't imply anything - except eventually that the person I'm
correcting should know better.
that that makes
Python closer to a C-like language than to an interpreted language,
Strange enough, no one calls Java or C# 'interpreted languages', while
they (or, to be more exact, their reference implementations ) both use
the same byte-code/VM scheme[1]. You know, the commonly accepted
definition of "byte-code" is "something that is going to be passed to a
virtual machine", not "native binary executable code", so I don't think
this could be really misleading.
Now what you don't seem to get is the difference between pure
interpretation - where each and every statement is parsed and
interpreted again and again - and intermediate byte-code compilation.
Believe me, *this* can make a huge difference wrt/ performances.
Also and FWIW, there are quite a lot of "C-like languages" that are - in
their only or reference implementation - interpreted or compiled to
byte-code. For a definition of "C-like" being "close to the C language's
syntax and grammar" !-)
[1] Oh, and before some nut-case jump in : no, this doesn't imply that
the CPython VM is 'equivalent' to Sun's Java VM or MS CLI/.NET VM.
and
that's not correct (IMO). If that's just a misinterpretati on by me, I
apologize.
>> largely because of the dynamical nature of Python. Fact
is that Python was designed from the start to run on a virtual machine,
not on the native hardware.
Nope. The facts are that
1/ Python (the language) has *not* been designed with ease of
implementati on of an optimizing native-code compiler in mind, and
2/ CPython (the first and reference implementation) has been designed
to use a byte-code + VM scheme
Isn't that more or less the same as what I said?
Can't you tell the difference ???
Maybe I don't make enough distinction between Python the language and
CPython the implementation, but Python development does happen on the
CPython implementation (Python 3.0 alpha releases are CPython releases,
for example).
>>
>>C OTOH was designed to be compiled to assembly code (or directly to
machine code)
Note quite. C has been designed to make it as easy as possible to
write either a C to assembly or C to native binary code compiler.
I find it hard to believe that during the development of C Dennis
Ritchie was considering any other mode of operation than compilation to
assembly or machine code. I might be wrong of course.
I'm not talking about "developmen t" (which implies implementation) , but
about the design of the *language*. Roel, can you define "language" ?
>>and as a result there are no (or virtually) no
implementatio ns that interpret C or compile it to bytecode.
There's at least one (possibly incomplete) C interpreter.
I'd like to call that the exception that confirms the rule.
Which rule ?
Oh, and yes - as a couple persons pointed out, there are actually more
than "one (possibly incomplete) C interpreter" - there are also the
llvm byte-code compiler+VM and the MS CLI C/C++ compiler.
>There's a very naive belief we saw every here and then here, which is
that "Python would be faster if it was compiled to native code". The
point is that, given Python's (as a language) extrem dynamism,
compiling it to native code wouldn't buy you much in terms of raw
performances . The problem is not with writing a native-code
compiler[1}, but with writing an *optimising* native-code compiler.
I admit I'm guilty of that belief. I know it's true what you say, but I
do have the more-or-less unconscious reflex 'compiled to native code ==
fast'.
So make a simple test : write a very Q&D cat-like program in Python, C
and Perl, and benchmark the three implementations . The results might
surprise you.
>I'm just getting fed up with
this "Python is an interpreted and therefore slow language" non-
sense. Python is a language, and as such is neither slow nor fast nor
interpreted nor compiled nor <insert any implementation related stuff
here>. And while CPython is not blazingly fast for computation-heavy
stuff, it's not because it is "interprete d" - which it is not for a
strict definition of "interprete d", but anyway... - but because
*optimizing* execution of an highly dynamic language is nothing,
well, err, trivial.
So you are saying that CPython is relatively slow because Python is a
highly dynamic language.
And therefore difficult to optimize.
I know that CPython is not Python and Python is
not CPython, but there is a very strong association between the two
Indeed. CPython is the reference implementation of Python. Like GCC is
the reference implementation of C on linux platforms. etc...
and
therefore I think it's not really that much wrong to simplify that to
'Python is slow because it is a highly dynamic language'
It is definitively wrong. How could a *language* be 'slow' or 'fast' ?
(until proven
wrong by PyPy or another fast implementation' .
You know, Common Lisp is also an highly dynamic language, and there are
now some optimizing native-code Common Lisp compilers that generate very
efficient binary code. It only tooks about 30 years and way more
ressources than CPython ever had to get there...
sturlamolden a écrit :
On May 19, 10:42 pm, "bruno.desthuil li...@gmail.com "
<bruno.desthuil li...@gmail.com wrote:
>Well... They do - they are called 'C compilers' !-) As Roel Schroven
mentioned - and he is at least partially right on this point - C has
been designed to make optimizing C compiler not to hairy to write.
C has proven very difficult to optimize, particularly because pointer
aliasing prevents efficient register allocation.
Does this compare to optimizing something like Python ? (serious
question, but I think I already know part of the answer).
By the way, is it possible (and easy) to call a C function from a
Python program??
Use SWIG. It's easy, smart and beautiful. After that, you can call C/C+
+ from a lot of scripting languages such as python, R, etc
A lot of open sources projects already use it. http://www.swig.org/tutorial.html
Boost.Python is also very known (but never tested by myself).
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