We have a benchmark repository that is run periodically:
https://github.com/sympy/sympy_benchmarks

I recommend starting there. You can find a number of regressions that can
be investigated.

Jason
moorepants.info
+01 530-601-9791


On Wed, Mar 27, 2019 at 5:17 PM Aaron Meurer <[email protected]> wrote:

> I agree with Oscar. I would also add that it's usually not trivial to
> determine where the bottlenecks are in SymPy. So I would write more
> about how you intend to profile the code.
>
> Perhaps it would be useful to take an existing thing that is slow in
> SymPy (you can use the performance issue label as a guide, or find
> something yourself,
>
> https://github.com/sympy/sympy/issues?q=is%3Aopen+is%3Aissue+label%3APerformance
> ),
> and try to fix the performance, documenting how you went about finding
> the bottleneck and fixing it. This can be used as a case study in your
> application.
>
> Also I would note that currently the benchmarking infrastructure for
> SymPy is quite bad (basically nonexistent). See
>
> https://github.com/sympy/sympy/wiki/GSoC-2019-Ideas#benchmarks-and-performance
> .
> It's fine if you do not want to work on that specifically, but you
> should note that you will be running the benchmarks on your own
> computer to find performance regressions. Not all performance issues
> are regressions either (some things have always been slow), so you
> should consider absolute numbers as well as relative numbers.
>
> Aaron Meurer
>
> On Wed, Mar 27, 2019 at 5:11 PM Oscar Benjamin
> <[email protected]> wrote:
> >
> > This looks like good work to do. I don't know how these applications
> > are evaluated but my thought if I was reviewing this would be that it
> > seems quite vague. This would probably be a more enticing proposal if
> > it had some specific suggestions of changes that would speed things
> > up.
> >
> > I can tell you now what is slow in the ODE module: currently even for
> > the simplest ODEs all matching code is run for all the possible
> > methods even after a suitable method has been found. It would be much
> > better to identify the most immediately usable solver and then use
> > that without matching all the others. This needs a refactor of the
> > module though and a redesign of the basic approach used by dsolve. I
> > want that to happen as an ultimate goal but I would like to see better
> > test coverage first.
> >
> > On Wed, 27 Mar 2019 at 09:56, Shiksha Rawat <[email protected]>
> wrote:
> > >
> > >
> https://github.com/sympy/sympy/wiki/GSoC-2019-Application-SHIKSHA-RAWAT-:-Benchmarks-and-performance
> > >
> > > I have designed a proposal for Benchmarks and Perfromance idea, though
> it is not complete yet.
> > >
> > > Can Jason Moore, Aaron and Oscar please review that and suggest
> changes?
> > >
> > >
> > > On Tue, Mar 19, 2019 at 11:53 PM Shiksha Rawat <
> [email protected]> wrote:
> > >>
> > >> I did further digging on the idea mentioned by Jason Moore.
> > >>
> > >> Figuring out the main bottlenecks for sympy : The best way to figure
> out these bottlenecks would be to designing a typical problem for each
> module for example mass spring damper for physics and computing time taken
> by sympy to give the output.If it is greater then expected than or a
> predefined threshold than analyzing the codebase of that module for
> possible changes to decrease computation time. And the results of
> predefined benchmarks could also be used.
> > >>
> > >> Creating benchmarks :
> https://media.readthedocs.org/pdf/asv/v0.1.1/asv.pdf
> > >> I think this documentation could come in handy for creating the
> benchmarks. The requirement of a particular benchmark could be made on the
> basis of the bottlenecks which we will figure out.
> > >>
> > >> Improving performance:  I think the best way to improve performance
> would be cleaning up the codebase first and then making changes in the
> algorithms used according to the requirements.
> > >>
> > >> Future Scope: Figuring out a method by which each PR also has to give
> information about the time the modules related to that PR will take to give
> output of problems associated with that module. (those mentioned in
> figuring out the bottlenecks point).
> > >>
> > >> I might be wrong about the ideas mentioned above. So I want
> suggestions from the mentors.
> > >>
> > >> Thanks.
> > >>
> > >> On Fri, Mar 15, 2019 at 9:48 PM Shiksha Rawat <
> [email protected]> wrote:
> > >>>
> > >>> I am really interested in taking up that idea. Can you suggest where
> or how should I start from because up till now I was just focusing on the
> physics module and benchmarks related to it?
> > >>> I am still trying to find how could we optimize matrix operations.
> > >>>
> > >>>
> > >>> On Fri, Mar 15, 2019 at 8:46 PM Jason Moore <[email protected]>
> wrote:
> > >>>>
> > >>>> The mechanics speedup idea is really just a narrow version of the
> profiling and benchmarking idea (focuses on just a couple of packages).
> Maybe a proposal that focuses on figuring out the main bottlenecks for
> sympy, creating benchmarks for them, and then improving performance is a
> good proposal idea that will ultimately help all the packages. I'm happy to
> support and mentor on that idea if someone wants to submit.
> > >>>>
> > >>>> Jason
> > >>>> moorepants.info
> > >>>> +01 530-601-9791
> > >>>>
> > >>>>
> > >>>> On Thu, Mar 14, 2019 at 2:19 PM Aaron Meurer <[email protected]>
> wrote:
> > >>>>>
> > >>>>> I agree. The biggest challenge with symbolic matrices is expression
> > >>>>> blow up. In some cases it is unavoidable, for instance, symbolic
> > >>>>> eigenvalues/eigenvectors use the symbolic solutions to polynomials,
> > >>>>> which are complicated in the general case for n > 2.
> > >>>>>
> > >>>>> One thing I meant by "overhead" is that if the type of a matrix's
> > >>>>> entries is known to all be rational numbers, for instance, we can
> > >>>>> operate directly on those numbers, ideally using fast number types
> > >>>>> like gmpy.mpq. If they are all rational functions, we can use
> > >>>>> polynomial algorithms that operate on rational functions. These
> always
> > >>>>> keep rational functions in canonical form, and the zero equivalence
> > >>>>> testing becomes literally "expr == 0" (no simplification required).
> > >>>>> These can be more efficient than general symbolic manipulation.
> > >>>>>
> > >>>>> This is how the polys module is structured. See
> > >>>>> https://docs.sympy.org/latest/modules/polys/internals.html. It
> would
> > >>>>> be nice to have a similar structure in the matrices, where a matrix
> > >>>>> can have a ground domain (or type) associated with its underlying
> > >>>>> data.
> > >>>>>
> > >>>>> Aaron Meurer
> > >>>>>
> > >>>>> On Thu, Mar 14, 2019 at 2:52 PM Oscar Benjamin
> > >>>>> <[email protected]> wrote:
> > >>>>> >
> > >>>>> > (Replying on-list)
> > >>>>> >
> > >>>>> > On Thu, 14 Mar 2019 at 20:37, Alan Bromborsky <
> [email protected]> wrote:
> > >>>>> > >
> > >>>>> > > Since most pc these days have multiple cores and threads what
> not use
> > >>>>> > > parallel algorithyms.  For honesty I must state I have a
> vested interest
> > >>>>> > > since I have a pc with a threadripper cpu with 16 cores and 32
> threads.
> > >>>>> >
> > >>>>> > Parallel algorithms can offer improvement. Your 16 cores might
> amount
> > >>>>> > to a 10x speed up if used well for this kind of thing. The
> > >>>>> > double-threading probably can't be exploited in CPython.
> > >>>>> >
> > >>>>> > However I think that many of the things that SymPy is slow for
> have
> > >>>>> > *really* bad asymptotic performance: think O(N!) rather than
> O(N^2).
> > >>>>> > Many orders of magnitude improvements can be made by spotting
> these
> > >>>>> > where more efficient methods are possible. It's not hard in a
> CAS to
> > >>>>> > accidentally generate enormous expressions and end up
> simplifying them
> > >>>>> > down again. This leads to many situations where it would be
> vastly
> > >>>>> > more efficient to somehow take a more direct route.
> > >>>>> >
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