Many of the sympy algorithms could be parallelized using parallel
python. That could lead to major speed improvements. The computer I am
writing this on has a Ryzen 9 5900x cpu with 12 cores and 24 threads. I
think that 4 cores and 8 threads is very common these days.
On 12/19/22 8:58 AM, Sam Brockie wrote:
> Put another way, what would be your preferred computer language if you
were SymPy starting again? There would obviously need to be an interface
to Python, but would you write the rest in C++?
There's the SymEngine project
(https://github.com/symengine/symengine), which already does this :)
SymPy itself, however, is, and should remain, a pure-Python computer
algebra system.
I agree with what Aaron said above, SymPy does well in making things
syntactically nice. From a pure performance perspective, SymPy and its
trees of OOP objects is almost certainly the wrong data structure for
performant computer algebra. Oscar Benjamin and I (with Aaron and
Jason Moore also present for parts) have discussed the merits of
hashcons (https://en.wikipedia.org/wiki/Hash_consing), which gives
really neat inherent caching, smaller memory footprint, efficient
expression traversal and topological sorting, and more. If there were
to be a concerted effort to refactor the internals of SymPy in any
way, my vote would strongly go towards using a different data
structure for the implementation.
Sam
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