I looked at the PRs yesterday, there are some overlap in the basic
functions. However, I have a longer road map about the JIT compilation. Let
me briefly list out it, so you all may think about if it is worthy or
suitable to put it in SymPy.
For the JIT compilation, it should provide the class or function interfaces
that wrap the details of llvmlite, the following functions are included:
1. Compile a single SymPy expression, a number is returned as the result
2. Compile a list of SymPy expresions, an array is returned as the result
(batch compile)
3. Compile a single SymPy expression, a array is returned when an array is
passed into the compiled function (vectorization)
- This is very useful since the over head of the function call can be
reduced when you want to do many times evaluation against the compiled
expressison
Advanced features:
4. The extension from 3, provide Cartisian product of parameters like the
Cartisian product in SQL
expr = x*y
f = jit_compile([x,y], expr)
f([2,3], [4,5,6]) # return [2*4, 2*5, 2*6, 3*4, 3*5, 3*6]
5. The extension from 4, vector symbol can be used instead of symbol of
double number
- This can be used in design algorithms which involve in a double loop
between two list of vectors
Even longer road map:
6. The compiled IR can be send to a remote server and the evaluation can be
performed on the server.
All the above has been implemented in my another project SymJava in the
experimental branch dist-snc. Actually, SymJava is motivated by SymPy, so I
believe is easy to implement the proposed features in SymPy.
-Yueming Liu
On Wednesday, April 20, 2016 at 1:43:11 PM UTC-7, Jason Moore 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]
> <javascript:>> 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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