[sva_]

It really depends on what you're doing, but I don't think it is generally accurate.

What slows Python down is generally the "everything is an object" attitude of the interpreter. I.e. you call a function, the interpreter has to first create an object of the thing you're calling.

In C++, due to zero-cost abstractions, this usually just boils down to a CALL instruction preceded by a bunch of PUSH instructions in assembly, based on the number of parameters (and call convention). This is of course a lot faster than running through the abstractions of creating some Python object.

[kaba0]

> What slows Python down is generally the "everything is an object" attitude of the interpreter

Nah, it’s the interpreter itself. Due to it not having JIT compilation there is a very high ceiling it can not even in theory surpass (as opposed to things like pypy, or graal python).

[codethief]

I don't think this is true: Other Python runtimes and compilers (e.g. Nuitka) won't magically speed up your code to the level of C++.

Python is primarily slowed down because of the fact that each attribute and method access results in multiple CALL instructions since it's dictionaries and magic methods all the way down.

[kaba0]

Which can be inlined/speculated away easily. It won’t be as fast as well-optimized C++ (mostly due to memory layout), but there is no reason why it couldn’t get arbitrarily close to that.

[codethief]

> Which can be inlined/speculated away easily.

How so? Python is dynamically typed after all and even type annotations are merely bolted on – they don't tell you anything about the "actual" type of an object, they merely restrict your view on that object (i.e. what operations you can do on the variable without causing a type error). For instance, if you add additional properties to an object of type A via monkey-patching, you can still pass it around as object of type A.

[kaba0]

A function/part of code is performed say a thousand times, the runtime collects statistics that object ‘a’ was always an integer, so it might be worthwhile to compile this code block to native code with a guard on whether ‘a’ really is an integer (that’s very cheap). The speedup comes from not doing interpretation, but taking the common case and making it natively fast and in the slow branch the complex case of “+ operator has been redefined” for example can be handled simply by the interpreter. Python is not more dynamic than Javascript (hell, python is strongly typed even), which hovers around the impressive 2x native performance mark.

Also, if you are interested, “shapes” are the primitives of both Javascript and python jit compilers instead of regular types.

[ale42]

Other than this, dynamic typing is a big culprit. I can't find back the article with the numbers, but its performance overhead is enormous.