Sturla Molden wrote:
> On 4/26/2007 2:19 PM, Steve Lianoglou wrote:
>
>>> Beside proper programing paradigm Python easily scales to large-
>>> scale number crunching: You can run large-matrices calculations
>>> with about 1/2 to 1/4 of memory consumption comparing to Matlab.
>>
>> Is that really true? (The large-matrix number crunching, not the
>> proper programming paradigm ;-)
>>
>> By no scientific means of evaluation, I was under the impression that
>> the opposite was true to a smaller degree.
>
> Matlab have pass-by-value semantics, so you have to copy your data in
> and copy your data out for every function call. You can achieve the same
> result in Python by pickling and unpickling arguments and return values,
> e.g. using this function decorator:
>
>
> import cPickle as pickle
>
> def Matlab_Semantics(f):
>
> '''
> Emulates Matlab's pass-by-value semantics,
> objects are serialized in and serialized out.
>
> Example: @Matlab_Semantics
> def foo(bar):
> pass
> '''
>
> func = f
> return wrapper
>
> def wrapper(*args,**kwargs):
> args_in = pickle.loads(pickle.dumps(args))
> kwargs_in = {}
> for k in kwargs:
> kwargs_in[k] = pickle.loads(pickle.dumps(kwargs[k]))
> args_out = func(*args_in,**kwargs_in)
> args_out = pickle.loads(pickle.dumps(args_out))
> return args_out
>
> Imagine using this horrible semantics in several layers of function
> calls. That is exactly what Matlab does. Granted, Matlab optimizes
> function calls by using copy-on-write, so it will be efficient in some
> cases, excessive cycles og copy-in and copy-out is usually what you get.
>
>
That's interesting. How did you find this information?
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