My gripe with Numba is that you're still giving up on a huge suite of Python's language features. One could say "oh well, eventually with enough work Numba will make all of Python as fast as Julia" but I don't think that's true. Python has specific semantics core to it's design that make all sorts of optimizations impossible. Julia made a lot of core design decisions to make sure that these optimizations were possible. You could invent a new language that's python-like but doesn't have the same limitations but then you might as well just use Julia.
Now, if all you ever interact with is is floating point numbers then Numba may be enough for you unless you're in an application like differential equation solving where the context switch of going back and forth between interpreted Python code and compiled Numba code will kill your performance.
But Julia's compiler will work on custom types just as well as builtin types (in fact, Julia's builtin number types are written in pure julia and could have been implemented in a package with the same performance).
> In either case, you can see the difference between C++ and Python was around 13x when using Numba... not quite as bad as 100x, but I imagine Julia probably does better.
When I said the 100x that's mostly a statement about Python's looping performance and discounting Numpy vectorization Ie. comparing a Python for loop to a C or Julia for loop (arguably a strawman, I know). Numpy is just calling C routines afterall, so for the most part it can be really fast other than the fact that individual Numpy calls don't know about each-other which precludes some optimizations.
But yeah, claims about performance differences between languages is a really technical and highly context contingent debate which is why I wish people wouldn't lean so much on Julia's speed as it's main selling point. It will mostly just lead to disappointment or confusion when someone comes over to julia and finds out that matrix multiplication is the same speed in julia as their old language (or slower since we default to bundling OpenBLAS instead of MKL). Hell, for people just writing simple scripts and restarting julia often, especially plotting, Julia is probably significantly slower than Python since a non-trivial amount of time will be spent compiling functions before the first time they're run.
Julia's incredibly expressive type system, macros and the crazy composability of julia code are much more impactful benefits than it's runtime performance, but these are much more vauge squishy, qualitative concepts than quantitative concepts like runtime performance.
Julia's compiled code might be super fast, but developing (or, God forbid, doing any sort of analysis) is painful because of how brutally slow the REPL/interactive environment is. Pretty much every little snippet of code you'll want to test as you write Julia feels like it takes _forever_ to run. I don't know if there's a solution for this while retaining the compiled run-time performance. I'm new to Julia (from R and Python), but I find the slowness/sluggishness of REPL to be nearly a deal breaker for me. It feels like the web back in the 1990s when you'd click a button and wait, and then click another button (or link) and wait, etc.
My gripe with Numba is that you're still giving up on a huge suite of Python's language features. One could say "oh well, eventually with enough work Numba will make all of Python as fast as Julia" but I don't think that's true. Python has specific semantics core to it's design that make all sorts of optimizations impossible. Julia made a lot of core design decisions to make sure that these optimizations were possible. You could invent a new language that's python-like but doesn't have the same limitations but then you might as well just use Julia.
Now, if all you ever interact with is is floating point numbers then Numba may be enough for you unless you're in an application like differential equation solving where the context switch of going back and forth between interpreted Python code and compiled Numba code will kill your performance.
But Julia's compiler will work on custom types just as well as builtin types (in fact, Julia's builtin number types are written in pure julia and could have been implemented in a package with the same performance).
> In either case, you can see the difference between C++ and Python was around 13x when using Numba... not quite as bad as 100x, but I imagine Julia probably does better.
When I said the 100x that's mostly a statement about Python's looping performance and discounting Numpy vectorization Ie. comparing a Python for loop to a C or Julia for loop (arguably a strawman, I know). Numpy is just calling C routines afterall, so for the most part it can be really fast other than the fact that individual Numpy calls don't know about each-other which precludes some optimizations.
But yeah, claims about performance differences between languages is a really technical and highly context contingent debate which is why I wish people wouldn't lean so much on Julia's speed as it's main selling point. It will mostly just lead to disappointment or confusion when someone comes over to julia and finds out that matrix multiplication is the same speed in julia as their old language (or slower since we default to bundling OpenBLAS instead of MKL). Hell, for people just writing simple scripts and restarting julia often, especially plotting, Julia is probably significantly slower than Python since a non-trivial amount of time will be spent compiling functions before the first time they're run.
Julia's incredibly expressive type system, macros and the crazy composability of julia code are much more impactful benefits than it's runtime performance, but these are much more vauge squishy, qualitative concepts than quantitative concepts like runtime performance.