# CAS Best Practices

**URL:** <https://discourse.julialang.org/t/cas-best-practices/58092>\
**Category:** Machine Learning\
**Tags:** symbolic\
**Created:** [March 27, 2021, 8:51pm UTC](https://discourse.julialang.org/t/cas-best-practices/58092 "2021-03-27T20:51:11Z")\
**Posts on this page:** 1\
**Showing post:** 36

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**Author:** ![stevengj](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/stevengj/32/71_2.png) [@stevengj](https://discourse.julialang.org/u/stevengj)\
**Post date:** [March 31, 2021, 8:19pm UTC](https://discourse.julialang.org/t/cas-best-practices/58092/36 "2021-03-31T20:19:26Z")

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> [@rfateman](#):
>
> Given the goal as you interpret it, it seems that time might be better spent on writing a program STJ , a SymPy to Julia translator

This is [a FAQ](https://docs.julialang.org/en/v1/manual/faq/#Why-don't-you-compile-Matlab/Python/R/%E2%80%A6-code-to-Julia?) — you don’t get any performance boost by “transpiling” from another language, because the whole performance benefit of Julia derives from the front-end, not the back-end.

> [@rfateman](#):
>
> What if the SymPy code was only, say 2X slower than running an equivalent algorithm in Julia, in the same problem-solving context, same computer system, … .

SymEngine is reportedly [hundreds of times faster than SymPy](https://github.com/symengine/symengine/pull/579) for many tasks, and Symbolics.jl is [reportedly](https://discourse.julialang.org/t/ann-symbolics-jl-a-modern-computer-algebra-system-for-a-modern-language/56251/51) matching SymEngine already on many benchmarks (but seems [easier to extend and integrate](https://discourse.julialang.org/t/ann-symbolics-jl-a-modern-computer-algebra-system-for-a-modern-language/56251/93) with other Julia projects than SymEngine’s C++ codebase).

To [quote one of the authors of SymPy and SymEngine](https://discourse.julialang.org/t/ann-symbolics-jl-a-modern-computer-algebra-system-for-a-modern-language/56251/92), _Python is indeed too slow, so it is hard for SymPy to compete in terms of speed_. The idea that a new implementation in a compiled language can do much better hardly seems outlandish.

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