[radarsat1]

That's a silly explanation. A smart JIT would be able to take successive Python opcodes and optimise them together as one operation, so how much time it spends interpreting individual instructions has nothing to do with the "speed", in the sense of "current speed" vs "potential speed".

If you want to talk about speed in any meaningful sense, you have to talk about potential for optimisation. It's possible that Python (and its opcodes) are designed in such a way that there is little potential for optimisation. This has to do with the semantics of the language, not how much time it spends in one part of the code or the other.

I don't know ultimately how much potential for optimisation Python has, but clearly it's a very difficult problem, so we can say with some certainty that Python is "slow", in the concrete sense that there are no low-hanging fruit left for speeding it up.

Edit: I say this as an avid Python user by the way. Especially combined with ctypes, I find the Python interpreter to be an absolutely excellent way to "organize" a bunch of faster code written in C/C++. I actually don't have any problem with Python itself being slow, I kind of like it that way personally. It's easy to understand its execution model if the interpreter is kept fairly simple, and this makes it easy to reason with. But then again I am not writing web-scale backends with it, I am just, more or less, using it to batch calls to scientific C functions. So it really depends on your use case. While I've spent plenty of time tuning every little performance gain out of a tight computational loop in C, I can't think of a single time where I've struggled to figure out how to speed up my Python code -- I just am not using it in ways that that would be necessary.

[ankrgyl]

> That's a silly explanation. A smart JIT would be able to take successive Python opcodes and optimise them together as one operation, so how much time it spends interpreting individual instructions has nothing to do with the "speed", in the sense of "current speed" vs "potential speed".

I think you might actually be agreeing with the explanation. The point about big opcodes means that the opportunities to look at a sequence of opcodes (i.e. the Python part) are reduced because you're doing a lot of computation over a relatively small number of opcodes. So the challenge involves optimizing the "guts" of the opcodes and the sequence of "guys" across relatively few opcodes. Their approach to solving this is discussed in the original blog post (https://blog.pyston.org/2016/06/30/baseline-jit-and-inline-c...). This complication happens to make optimizing Python via JIT compilation a tough problem.