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My understanding is that it runs faster than native python and R. That said with Numba and other libraries, see no point.
by axpy906 5y ago
My understanding is that it runs faster than native python and R. That said with Numba and other libraries, see no point.
- NeutralForest 5y agoIt's easier to write Julia code than to deal with Numba tbh and the ecosystem around Julia makes the code composable which is often not the case if you write Numba code and have to deal with other libraries.
- tfehring 5y agoNumba is great for pure functions on primitive types but it breaks down when you need to pass objects around. PyPy is fantastic for single-threaded applications but doesn't play nicely with multiprocessing or distributed computing IME. Numpy helps for stuff you can vectorize, but there's a lot of stuff you can't (or can but shouldn't); it also brings lots of minor inconveniences by virtue of not being a native type - e.g., the code to JSON serialize a `Union[Sequence[float], np.ndarray]` isn't exactly Pythonic.
- oscardssmith 5y agoAlso, numpy has about a 100x overhead door small arrays (10 or fewer elements).
- danuker 5y ago> runs faster than native python and R That's a bit of an understatement. It's about as fast as C and Rust (ignoring JIT compilation time). https://julialang.org/benchmarks/ https://julialang.org/benchmarks/
- dklend122 5y agoComposability, speed, static analysis, type system, abstractions, user defined compiler passes, metaprogramming, ffi, soon static compilation, differentiability and more create an effect that far exceeds numba