[nishnik]

To add more info they have been building a C++ code-base for the most important features: https://github.com/symengine/symengine SymEngine can be used from C++ or from the wrappers in Python, Julia, Haskell - and gives a performance boost over vanilla SymPy.

[ChrisRackauckas]

With DifferentialEquations.jl and the SciML ecosystem we used SymEngine for a long time and it was great, but we found we could have much better performance with a pure Julia ecosystem, and so we built out the ModelingToolkit ecosystem. It's been turning out great. One of the nice things is that rule simplification uses Julia's task-parallelism, so you get a lot of parallelism for free. It's really focused on symbolic derivation of high performance numerical code, like in https://mtk.sciml.ai/dev/tutorials/symbolic_functions/