Comment by coldtea | Hacker News Reader

Comment by coldtea | Hacker News Reader

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Python is a scripting language, so its strong point is being used as a glue, and half of its standard library is implemented in C anyway.

Plus, the "C-implementations" he mentions are available as readily usable modules (numpy), or semi-transparent jit/aot compilers only needing a few annotations (Cython, numba), not actual C you have to write.

Besides, isn't the whole point: finish your project fast with the language you know using whatever it makes available to easily speed your code up?

As opposed to: "be a purist and not use wrapped libs written in another language".

Who cares for that? Even if it comes up, it's to avoid the hassle of having to deal with an additional language, setup etc -- which for numpy, Cython etc is almost none-existent (as you don't need to actually deal with C).

And of course, despite the purity of Julia's "single language", the hassle of moving to a totally different language, which few use, is not yet stable in syntax, compiler etc, and has fewer libs, should also be considered...

The specific purpose of the benchmark, though, is to compare implementations of the same algorithm natively in the language itself, as explained explicitly on the Julia website just under the table of benchmark results (see quote below).

As such, I do think the article misses the point somewhat. Of course, if there's a numpy function that does what you want, you'd use it in real life. But what if there isn't? The nice thing about Julia is that the function can be written in Julia itself, and fast.

> It is important to note that these benchmark implementations are not written for absolute maximal performance (the fastest code to compute fib(20) is the constant literal 6765). Rather, all of the benchmarks are written to test the performance of specific algorithms implemented in each language. In particular, all languages use the same algorithm: the Fibonacci benchmarks are all recursive while the pi summation benchmarks are all iterative; the “algorithm” for random matrix multiplication is to call the most obvious built-in/standard random-number and matmul routines (or to directly call BLAS if the language does not provide a high-level matmul), except where a matmul/BLAS call is not possible (such as in JavaScript). The point of these benchmarks is to compare the performance of specific algorithms across language implementations, not to compare the fastest means of computing a result, which in most high-level languages relies on calling C code.

https://julialang.org

> Of course, if there's a numpy function that does what you want, you'd use it in real life. But what if there isn't?

I have been in this exact situation, a numerical algorithm that was missing from Numpy but the rest of the project is in Python.

The solution is:

1. Write a Python function that operates on numpy arrays,

2. Add a few Cython type declarations to loop variables,

3. Mark the source file as "compile with Cython at runtime", which seamlessly turns the Python function into a C library.

The end result was a 1000x speedup compared to pure Python, very close to numpy built-in functions working on similarly sized arrays. And it needed only about 5 lines of setup code and type declarations for a few variables - all the code could still be Python and use all of Python even in the compiled files.

>The specific purpose of the benchmark, though, is to compare implementations of the same algorithm natively in the language itself, as explained explicitly on the Julia website just under the table of benchmark results (see quote below).

But then they go and write their own sort instead of using the language provided ones when offered. All these show is that julia is apparently faster than incredibly unidiomatic python written by someone who clearly doesn't write python. Okay. That's neat.

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