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> Everything you do in Python involves chasing down pointers and method tables, looking up strings in dictionaries, and that sort of thing. One of the things t

> Everything you do in Python involves chasing down pointers and method tables, looking up strings in dictionaries, and that sort of thing. One of the things that a Cpp Compiler can do is De-Virtualize Function Calls. That is; solve those function pointer tables at Compile Time. Why could not a good compiler for Python do the same?

nine_k 2y ago
C++ has an explicit compilation phase after which things like classes remain stable. Python only has a byte-compilation phase that turns the parse tree into executable format. Everything past that, including the creation if classes, imports, etc is runtime. You can pick a class and patch it. You can create classes and functions at runtime; in fact, this happens all the time, and not only for lambdas, but this is how decorators work. A JIT compiler could detect actual usage patterns and replace the code with more efficient versions, until a counter-example is found, and the original "de-optimized" code is run. This is how JavaScript JITs generally work.
klodolph 2y ago
It’s a lot easier in C++ or Java. In C++, you make the virtual calls to some base class, and all you need to demonstrate is that only one concrete class is being used at that place. For example, you might see a List<T> in Java, and then the JIT compiler figures out that the field is always assigned = new ArrayList<T>(), which allows it to devirtualize. In Python, the types are way more general. Basically, every method call is being made to “object”. Every field has a value of type “object”. This makes it much more difficult for the compiler to devirtualize anything. It might be much harder to track which code assigns values to a particular field, because objects can easily escape to obscure parts of your codebase and be modified without knowing their type at all. This happens even if you write very boring, Java-like code in Python.

nine_k 2y ago

C++ has an explicit compilation phase after which things like classes remain stable. Python only has a byte-compilation phase that turns the parse tree into executable format. Everything past that, including the creation if classes, imports, etc is runtime. You can pick a class and patch it. You can create classes and functions at runtime; in fact, this happens all the time, and not only for lambdas, but this is how decorators work. A JIT compiler could detect actual usage patterns and replace the code with more efficient versions, until a counter-example is found, and the original "de-optimized" code is run. This is how JavaScript JITs generally work.

klodolph 2y ago

It’s a lot easier in C++ or Java. In C++, you make the virtual calls to some base class, and all you need to demonstrate is that only one concrete class is being used at that place. For example, you might see a List<T> in Java, and then the JIT compiler figures out that the field is always assigned = new ArrayList<T>(), which allows it to devirtualize. In Python, the types are way more general. Basically, every method call is being made to “object”. Every field has a value of type “object”. This makes it much more difficult for the compiler to devirtualize anything. It might be much harder to track which code assigns values to a particular field, because objects can easily escape to obscure parts of your codebase and be modified without knowing their type at all. This happens even if you write very boring, Java-like code in Python.