If I write Python and my code is fast, to me that sounds like Python is fast, I couldn't care less whether it's because the implementation is in another language or for some other reason.
When you see an interpreted language faster than a compiled one, it's worth looking at why, because most the time it's because there's some hidden issue causing the other to be slow (which could just be a different and much worse implementation).
Put another way, you can do a lot to make a Honda Civic very fast, but when you hear one goes up against a Ferrari and wins your first thoughts should be about what the test was, how the Civic was modified, and if the Ferrari had problems or the test wasn't to its strengths at all. If you just think "yeah, I love Civics, that's awesome" then you're not thinking critically enough about it.
The C components initiate the system call and manage the file pointer, which loads the data from the disk into a pyobj string.
Therefore, it isn't so much Python itself that is being tested, but rather python underlying C runtime.
> When you see an interpreted language faster than a compiled one, it's worth looking at why, because most the time it's because there's some hidden issue causing the other to be slow (which could just be a different and much worse implementation).
On the contrary, the compiled languages tend to only be faster in trivial benchmarks. In real-world systems the Python-based systems tends to be faster because they haven't had to spend so long twiddling which integers they're using and debugging crashes and memory leaks, and got to spend more time on the problem.
So, like in most things, the details can sometimes matter quite a bit.
Code that has lots of attention is different, certainly, but it's also the exception rather than the rule; the last figure I saw was that 90% of code is internal business applications that are never even made publicly available in any form, much less subject to outside code review or contributions.
> As time spent on the project increases, I suspect that any gain an interpreted language has over an (efficient) compiled one not only gets smaller, but eventually reverses in most cases.
In terms of the limit of an efficient implementation (which certainly something like Python is nowhere near), I've seen it argued both ways; with something like K the argument is that a tiny interpreter that sits in L1 and takes its instructions in a very compact form ends up saving you more memory bandwidth (compared to what you'd have to compile those tiny interpreter instructions into if you wanted them to execute "directly") than it costs.
There's a paper on this you might like. https://www.researchgate.net/publication/2749121_When_are_By...
I think there's something to the idea of keeping the program in the instruction cache by deliberately executing parts of it via interpreted bytecode. There should be an optimum around zero instruction cache misses, either from keeping everything resident, or from deliberately paging instructions in and out as control flow in the program changes which parts are live.
There are complicated tradeoffs between code specialisation and size. Translating some back and forth between machine code and bytecode adds another dimension to that.
I fear it's either the domain of extremely specialised handwritten code - luajit's interpreter is the canonical example - of the the sufficiently smart compiler. In this case a very smart compiler.
This is an interesting premise.
Python in particular gets an absolute kicking for being slow. Hence all the libraries written in C or C++ then wrapped in a python interface. Also why "python was faster than rust at anything" is headline worthy.
I note your claim is that python systems in general tend to be faster (outside of trivial benchmarks, whatever the scope of that is). Can you cite any single example where this is the case?
Plenty of line-of-business systems I've seen, but systems big enough to matter tend not to be public. Bitbucket's cloud and on-prem version are the only case I can think of where you can directly compare something substantial between an implementation known to be written in Python and an implementation that's known to be written in C/C++ (and even then I'm not 100% that that's what they use).
For me, coding is almost exclusively using python libraries like numpy to call out to other languages like c or FORTRAN. It feels silly to say I'm not coding in Python to me.
On the other hand, if you're writing those libraries, coding to you is mostly writing FORTRAN and c optimizations. It probably feels silly to say you're coding in Python just because that's where your code is called from.
It's actually the opposite, a Python programmer should know how to offload most, or use the libraries that do so, out of Python into C. He should not be oblivious to the fact that any decent Python performance is due to shrinking down the ratio of actual Python instructions vs native instructions.
I noticed that you're pretty hard in the "basic isn't fast, the thing it transpiles to is fast" camp, but still accidentally said "there is a version of BASIC [...] that is lightning fast" which I'm not sure you think? Highlights just how tricky it is to talk about where speed lives
There is clear distinction between original language design (an interpreter) and a project aiming to recreate a sub-standard of that language and support its legacy codebase via a transpiler.
It's completely fair to say that's not python because it isn't - any language out there can FFI to C and it has the same problems mentioned above.
Pretty much any language can wrap C/Rust code.
Why does it matter?
1. Having to split your code across 2 languages via FFI is a huge pain.
2. You are still writing some Python. There's plenty of code that is pure Python. That code is slow.
It's pure Python in that there's no cffi, no ctypes, no Cython, no C extensions of any kind.
Conversely, you can have pure C code just using PyObjects (this is effectively what Cython does), with the Python bytecode interpreter completely out of the picture. But the perf improvement is nowhere near what people naively expect from compiled code, usually.
The only thing that makes sense to compare when talking about pythons performance is how many instructions it needs to compute something, versus the instructions needed to compute the same thing in C. Those are probably a few orders of magnitude apart.
But the Zen3/4 were developed far, far after the PyObject header...
Just like in this article. The author measured, wondered, investigated, experimented, and finally, after a lot of hard work, made the C/Rust programs faster. You wouldn't call that luck, would you? If there had been a similar performance regression in CPython, then a benchmark could have picked up on it, and the CPython developers would then have done the same.
Also: If you're going to prove that changes informed by performance measurements are absent from the commit logs, then you'll need to look in the logs for all the relevant places, which means also looking at I/O and bytes and allocator code.
Also, when we talk about "faster" and "slower," it's not clear the order of magnitude.
Maybe an analysis of actual code execution would shed more light than a simplistic explanation that the Python interpreter is written in C. I don't think the BASIC interpreter in my first computer was written in BASIC.
What's there to understand? When it's fast it's not really Python, it's C. C is fast. Python can call out to C. You don't have to care that the implementation is in another language, but it is.
99% of my use cases are easily, maintainably solved with good, modern Python. The Python execution is almost never the bottleneck in my workflows. It’s disk or network I/O.
I’m not against building better languages and ecosystems, and compiled languages are clearly appropriate/required in many workflows, but the language parochialism gets old. I just want to build shit that works and get stuff done.