Can you explain the graphs you attached. What do all the labels mean? Why
would lambdify by faster than ufuncify?


Jason
moorepants.info
+01 530-601-9791

On Wed, Apr 20, 2016 at 1:42 PM, Jason Moore <[email protected]> wrote:

> This sounds great. Note that we have recently merged some code that uses
> llvm to automatically JIT sympy expressions. Check out the master branch
> and search for the relevant pull requests. Maybe there is some overlap with
> your project.
>
>
> Jason
> moorepants.info
> +01 530-601-9791
>
> On Wed, Apr 20, 2016 at 11:29 AM, yueming liu <[email protected]>
> wrote:
>
>> I am not sure if it is too late to contribute to SymPy and the paper.
>> I've been developing a private project called sympy-llvm which uses
>> just-in-time (JIT) compilation technique to compile SymPy expressions to
>> native machine code in order to speedup the numerical evaluation of the
>> expressions for numerical computation purpose. This is similar to the
>> existing functions in SymPy like subs/evalf, lambdify, ufuncify and Theano
>> (see http://docs.sympy.org/latest/modules/numeric-computation.html). The
>> advantage of sympy-llvm is that it is faster than all the existing methods
>> in the sense of compilation time and numerical evaluation time. Another
>> advantage is that no FORTRAN or C/C++ source code generation involved.
>> Runnable machine code is generated in memory using LLVM (Attached figures
>> show some comparison benchmarks. SMC_py stands for sympy-llvm
>> implementation).  Example applications are implemented such as the
>> numerical computation for modals in PyDy. I'd like to make sympy-llvm
>> public and integrate into SymPy as an optional component for numerical
>> computation if possible.
>>
>> As you all may know that the projects like Google TensorFlow and Theano
>> are both using symbolic-numerical way to provide human friendly language
>> interface and fast numerical computation. The CASs projects developed a
>> couple of decades ago like Maple, Maxima, Mathematica, Reduce etc (
>> https://en.wikipedia.org/wiki/List_of_computer_algebra_systems) none of
>> them have 'in-memory' JIT complication functions to bridge the gap between
>> symbolic and numeric computations. I believe sympy-llvm as a component of
>> SymPy will be a great enhancement of SymPy in numerical computation field.
>>
>> -Yueming Liu
>>
>>
>> On Tuesday, April 19, 2016 at 3:53:13 PM UTC-7, Ondřej Čertík wrote:
>>>
>>> Hi,
>>>
>>> I would like to invite anybody to contribute to our paper about SymPy
>>> and become an author. We use the authorship criteria that are written
>>> in our README:
>>>
>>>
>>> https://github.com/sympy/sympy-paper/blob/2a93d84a6f3447f8e15e24f02cedb6c27c299abd/README.md#authorship-criteria
>>>
>>> In other words, to satisfy 1), you must contribute to sympy in some
>>> way (e.g. some good patch that is more than, say, fixing a typo in
>>> documentation), to satisfy 2), get involved with the development of
>>> the sympy-paper repository: https://github.com/sympy/sympy-paper,
>>> submit a patch there, write a section, or just review PRs. Finally,
>>> you must also be willing to satisfy 3) and 4). Hopefully this should
>>> be pretty clear, but if you have any questions about authorship,
>>> please let me or Aaron know.
>>>
>>> Once this paper is accepted, we will probably put it into the SymPy's
>>> README for people to cite, so I encourage everyone to get involved.
>>>
>>> Ondrej
>>>
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