Assumedly if it doesn't, then you would be able to tell just by applying the 
sympy cse and seeing if it makes a significant difference in the run time. 

Aaron Meurer 

> On Nov 5, 2013, at 6:45 PM, Matthew Rocklin <[email protected]> wrote:
> 
> Sadly I'm not an expert in C compilers and I no longer have an awesome 
> compilers-post-doc-office-mate.  I don't know what they'll do in this 
> situation.  His no-CSE statement would still hold if the input pointers point 
> to dynamically allocated memory.  I don't recall what he said if they point 
> to statically allocated memory; I think it was a big nebulous "it depends."
> 
> 
>> On Tue, Nov 5, 2013 at 5:35 PM, Jason Moore <[email protected]> wrote:
>> Got it.
>> 
>> SymPy's code generators in codegen and autowrap don't support evaluating a 
>> SymPy matrix as the expression. I'd like to improve those so that you can 
>> pass in a sequences of Matrices and it returns a function that evaluates all 
>> of the matrices in a single C/Fortran/etc file.
>> 
>> Here is a simple example of the c file I'm generating from two SymPy 
>> matrices: 
>> https://github.com/PythonDynamics/pydy-code-gen/blob/master/pydy_code_gen/tests/expected_cython/desired_mass_forcing_c.c
>> 
>> 
>> Jason
>> moorepants.info
>> +01 530-601-9791
>> 
>> 
>>> On Tue, Nov 5, 2013 at 5:41 PM, Matthew Rocklin <[email protected]> wrote:
>>> My previous statement was that Theano won over autowrap.ufuncify because 
>>> theano did CSE internally (theano's basic data structure is a DAG).
>>> 
>>> My understanding is that your C friend is correct IF the code is written in 
>>> the standard way, declaring each variable individually.  I remember looking 
>>> at older code of your groups in which all of the variables were stored in a 
>>> dynamically allocated array.  My C friend mentioned that most compilers 
>>> won't dare touch such a structure because it's verify that transformations 
>>> are safe.  I have no idea what your various code generators are doing under 
>>> the hood. 
>>> 
>>> By the way I did not intend to raise the issue of CSE.  I'm mostly curious 
>>> about the poor performance of Theano and I don't think this is that issue.
>>> 
>>> 
>>>> On Tue, Nov 5, 2013 at 10:48 AM, Aaron Meurer <[email protected]> wrote:
>>>> I would still benchmark running cse and not. If cse() is fast enough,
>>>> it might out-balance the overhead of the code generation, especially
>>>> if it significantly reduces the resulting code size.
>>>> 
>>>> Aaron Meurer
>>>> 
>>>> On Tue, Nov 5, 2013 at 11:36 AM, Jason Moore <[email protected]> wrote:
>>>> > My roommate who's a C guy suggested that CSE was a waste of time if you 
>>>> > have
>>>> > a good compiler. He claimed that the compiler will do CSE anyways, so 
>>>> > doing
>>>> > it in the C code was not useful. I need to compare my Cython code output
>>>> > with and without CSE (it currently uses CSE).
>>>> >
>>>> > How would I use CSE with theano_function? I'm not doing that currently 
>>>> > and
>>>> > it wasn't apparent to me how to go about that.
>>>> >
>>>> >
>>>> > Jason
>>>> > moorepants.info
>>>> > +01 530-601-9791
>>>> >
>>>> >
>>>> > On Tue, Nov 5, 2013 at 1:30 PM, Matthew Rocklin <[email protected]> 
>>>> > wrote:
>>>> >>
>>>> >> In my experiments with sympy-theano earlier this year I found that it 
>>>> >> was
>>>> >> competitive with our Fortran code generation (autowrap.ufuncify) on 
>>>> >> single
>>>> >> array outputs and outperformed it when CSE were involved.  I don't have
>>>> >> anything recorded that profiles scalar operations.
>>>> >>
>>>> >> Frederic might have some intuition on Theano's performance on large 
>>>> >> scalar
>>>> >> computations.  I've brought this up in the past and don't actually 
>>>> >> remember
>>>> >> the answer.
>>>> >>
>>>> >>
>>>> >> On Tue, Nov 5, 2013 at 9:51 AM, Jason Moore <[email protected]> 
>>>> >> wrote:
>>>> >>>
>>>> >>> Ok, so I think I'm comparing things correctly then. It is surprising 
>>>> >>> that
>>>> >>> the compiled Theano code doesn't seem to execute that fast. The 
>>>> >>> functions
>>>> >>> I'm generating are essentially evaluating very long sympy expressions, 
>>>> >>> maybe
>>>> >>> Theano isn't great at that...
>>>> >>>
>>>> >>>
>>>> >>> Jason
>>>> >>> moorepants.info
>>>> >>> +01 530-601-9791
>>>> >>>
>>>> >>>
>>>> >>> On Tue, Nov 5, 2013 at 12:48 PM, Matthew Rocklin <[email protected]>
>>>> >>> wrote:
>>>> >>>>
>>>> >>>> `sympy.printing.theanocode.theano_function` calls `theano.function`
>>>> >>>>
>>>> >>>> The result of `theano_function` is a compiled theano function.
>>>> >>>>
>>>> >>>>
>>>> >>>>
>>>> >>>> On Tue, Nov 5, 2013 at 9:27 AM, Jason Moore <[email protected]>
>>>> >>>> wrote:
>>>> >>>>>
>>>> >>>>>
>>>> >>>>>
>>>> >>>>>
>>>> >>>>> Jason
>>>> >>>>> moorepants.info
>>>> >>>>> +01 530-601-9791
>>>> >>>>>
>>>> >>>>>
>>>> >>>>> On Tue, Nov 5, 2013 at 8:46 AM, Frédéric Bastien <[email protected]>
>>>> >>>>> wrote:
>>>> >>>>>>
>>>> >>>>>> Hi,
>>>> >>>>>>
>>>> >>>>>> Just to know, witch version of Theano did you used? I have speed up 
>>>> >>>>>> a
>>>> >>>>>> little the optimization phase of the compilation and I have a PR 
>>>> >>>>>> that
>>>> >>>>>> will optimize this more.
>>>> >>>>>
>>>> >>>>>
>>>> >>>>> I'm using the master branch of Theano as of last week.
>>>> >>>>>
>>>> >>>>>>
>>>> >>>>>>
>>>> >>>>>> Also, Theano cache the compilation of the c code. Where this timming
>>>> >>>>>> done with a empty theano cache or a filled Theano cache? If you ran
>>>> >>>>>> the same benchmark multiple time on the same computer, only the 
>>>> >>>>>> first
>>>> >>>>>> time Theano will compile the c code, the other time it will reuse 
>>>> >>>>>> what
>>>> >>>>>> is in the cache.
>>>> >>>>>
>>>> >>>>>
>>>> >>>>> I time two things wrt to Theano:
>>>> >>>>>
>>>> >>>>> 1. The time it takes to call 
>>>> >>>>> sympy.printing.theanocode.theano_function
>>>> >>>>>
>>>> >>>>> and
>>>> >>>>>
>>>> >>>>> 2. The time it takes to call the function generated from 1.
>>>> >>>>>
>>>> >>>>> When does the Theano compilation happen? Is it in the call to
>>>> >>>>> theano_function or the first time I use the generated function?
>>>> >>>>>
>>>> >>>>> If it is the latter then my timing comparisons aren't really 
>>>> >>>>> comparing
>>>> >>>>> apples to apples.
>>>> >>>>>
>>>> >>>>>>
>>>> >>>>>>
>>>> >>>>>> thanks
>>>> >>>>>>
>>>> >>>>>> Frédéric
>>>> >>>>>>
>>>> >>>>>> On Sun, Nov 3, 2013 at 3:22 PM, Ronan Lamy <[email protected]>
>>>> >>>>>> wrote:
>>>> >>>>>> > Le 03/11/13 14:19, Jason Moore a écrit :
>>>> >>>>>> >
>>>> >>>>>> >> Ronan,
>>>> >>>>>> >>
>>>> >>>>>> >> Thanks for looking at the derivation code. We haven't ever really
>>>> >>>>>> >> had
>>>> >>>>>> >> any review of it outside of our mechanical engineer group, so 
>>>> >>>>>> >> this
>>>> >>>>>> >> very
>>>> >>>>>> >> helpful.
>>>> >>>>>> >>
>>>> >>>>>> >> I'll review your PR and look into the second two items.
>>>> >>>>>> >>
>>>> >>>>>> >> Why do you think the dictionary representation will be so much
>>>> >>>>>> >> faster?
>>>> >>>>>> >
>>>> >>>>>> >
>>>> >>>>>> > Using a dict would make Vector.__eq__ a lot less expensive, and 
>>>> >>>>>> > make
>>>> >>>>>> > canonicalisation (as currently done in Vector.__init__) 
>>>> >>>>>> > O(len(args))
>>>> >>>>>> > instead
>>>> >>>>>> > of O(len(args)**2).
>>>> >>>>>> >
>>>> >>>>>> > However, it seems that the main performance issue is that
>>>> >>>>>> > instantiating
>>>> >>>>>> > Matrix objects is expensive. Using a dict isn't strictly required 
>>>> >>>>>> > to
>>>> >>>>>> > solve
>>>> >>>>>> > this, but the structure I suggest would allow e.g. dot products of
>>>> >>>>>> > vectors
>>>> >>>>>> > to be decomposed as combinations of multiplications of components
>>>> >>>>>> > and dot
>>>> >>>>>> > products of base vectors, which can be optimised or cached
>>>> >>>>>> > separately. There
>>>> >>>>>> > would be no need for intermediate Matrix objects.
>>>> >>>>>> >
>>>> >>>>>> >
>>>> >>>>>> >>
>>>> >>>>>> >> On Sat, Nov 2, 2013 at 10:11 PM, Ronan Lamy <[email protected]
>>>> >>>>>> >> <mailto:[email protected]>> wrote:
>>>> >>>>>> >>
>>>> >>>>>> >>     Le 01/11/13 11:54, Jason Moore a écrit :
>>>> >>>>>> >>
>>>> >>>>>> >>         I've been tinkering with code generation for ODE's that
>>>> >>>>>> >>         sympy.physics.mechanics spits out and have some results:
>>>> >>>>>> >>
>>>> >>>>>> >>         http://www.moorepants.info/__blog/pydy-code-gen.html
>>>> >>>>>> >>
>>>> >>>>>> >>         <http://www.moorepants.info/blog/pydy-code-gen.html>
>>>> >>>>>> >>
>>>> >>>>>> >>         Several people have posted topics on this recently. We 
>>>> >>>>>> >> need
>>>> >>>>>> >> to
>>>> >>>>>> >>         build in
>>>> >>>>>> >>         a code generator for solving ODE's into SymPy that would
>>>> >>>>>> >> play
>>>> >>>>>> >>         well with
>>>> >>>>>> >>         the codegen and autowrap modules. I think I can use this
>>>> >>>>>> >> code I
>>>> >>>>>> >>         wrote as
>>>> >>>>>> >>         a base to start working on that but would need some help
>>>> >>>>>> >>         generalizing it
>>>> >>>>>> >>         beyond our systems. Feedback is welcome.
>>>> >>>>>> >>
>>>> >>>>>> >>
>>>> >>>>>> >>     I've only looked at the derivation part, it's an interesting
>>>> >>>>>> >>     real-world(ish) benchmark for expression manipulation. 
>>>> >>>>>> >> However,
>>>> >>>>>> >> it
>>>> >>>>>> >>     incurs a lot of avoidable overhead:
>>>> >>>>>> >>     * Extracting some loop constants out of their loops cuts down
>>>> >>>>>> >> the
>>>> >>>>>> >>     run-time by 40%, cf. 
>>>> >>>>>> >> https://github.com/sympy/__sympy/pull/2570
>>>> >>>>>> >>
>>>> >>>>>> >>     <https://github.com/sympy/sympy/pull/2570>
>>>> >>>>>> >>     * The creation of temporary objects by the Vector class takes
>>>> >>>>>> >> up
>>>> >>>>>> >>     most of the run-time.
>>>> >>>>>> >>     * The internal representation of Vector objects seems
>>>> >>>>>> >> inefficient. I
>>>> >>>>>> >>     think it should be switched to a dict-based representation 
>>>> >>>>>> >> as a
>>>> >>>>>> >>     linear combination of base vectors (e.g. using {I.x: l0, B.y:
>>>> >>>>>> >> -l1}
>>>> >>>>>> >>     for l0 * I.x - l1 * B.y).
>>>> >>>>>> >>
>>>> >>>>>> >>
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