[aaronscott]

> I like to define my subroutines using a modern language like C++, which goes 47 gigaflops. This means C++ is three orders of a magnitude faster than Python. That's twenty years of progress per Moore's law.

This is great. I love the idea of measuring performance differences in “years of Moore’s law.”

Twenty years puts the delta in an easy to understand framework.

[JohnKemeny]

I doubt that you get Python to run faster than C++ at 2004 hardware.

[mrtranscendence]

Python on 2024 hardware vs C++ on 2004 hardware ... I don't think it's obvious that C++ always wins here, though it would depend on the use case, how much of the Python is underpinned by native libraries, and the specific hardware in question.

[JohnKemeny]

If we allow native libraries, it's not clear that C++ would win, even on modern hardware.

[michaelt]

I think we all know that, when someone writes "C++ is three orders of a magnitude faster than Python" they're not including native libraries.

[mrtranscendence]

You can't not include native libraries, at least if you want your benchmark to be realistic. Almost every Python library where performance matters is written (at least partially) in a compiled language.

[bornfreddy]

Yes, but many people like the sound of "X-times faster than Python" while conveniently forgetting that the same thing can be (and usually is) done in Python + numpy & co. even faster.

I have come to appreciate "slowness" of Python. It trades speed for legibility, which is a great compromise once you have really fast native libraries one import away. Best of both worlds.

[MichaelDickens]

C++ with well-optimized libraries should always outperform Python with well-optimized libraries, right? They should be ~identical in the highly optimized inner loops, but Python has more overhead. But naive hand-written C++ could easily perform worse than something like Numpy.

(I've only tested this once, and my naive hand-written C++ was still twice as fast as Numpy, but that was only on one specific task.)

[bevekspldnw]

Honestly depends on what you are doing. Most of my python work is data collection and analysis on top of Postgres.

Being smart in how I use Postgres indexing (and when to disable it outright) has more performance impact than the actual language doing the plumbing.