Accidentally quadratic: When Python is faster than C++

You can always beat c if c happens to implement a bad enough algorithm and your n is large enough. That's no guarantee that the performance ∆ will continue to hold once the two language implement the same algorithm.

You're right that flat datastructures are important, but C is far from the only language that can offer those.

I don't think C can ever be the answer; "comparable C code", i.e. code that was produced with the same amount of developer effort, is almost always undefined behaviour that happens to work most of the time on the developer's compiler. C doesn't have the kind of polymorphism that you need to write code that's compositional, testable, but also has good cache locality, at least not for code that also has to interact back and forth with the outside world. There is at least some work in the literature on making efficient use of cache in a Haskell/ML-like evaluation-by-reduction model, and I think that's where the long-term way forward lies (and FWIW in my experience compilers for ML-family languages already more than smart enough).

Sometimes. I know 2 reasons.

1. In some higher-level languages, some problems can be efficiently solved with code generation. For instance, you can take some of the input data, generate code from that, then run the generated code processing the rest of the input data. Examples of the mechanism include Lisp macros, or .NET shenanigans like System.Reflection.Emit or System.Linq.Expressions.

It’s hard to generate good machine code. Possible to do in C, but you gonna need to embed C compiler for that. They are extremely complex, and were not designed for the use case e.g. relatively slow (designed to run offline on fast developer’s computers, or on even faster build servers).

If your problem is a good fit for that approach, C# can actually be faster than C.

Parsing JSON is a good example. When your goal is not just parsing the syntax, but also populating structures with the data from the JSON, good C# JSON parsers gonna runtime-generate serialization code by reflecting the structure types being populated. For an isolated program, technically you can generate equivalent C code offline. But if you’re making a general-purpose JSON serialization library that’s borderline impossible to achieve in C, need reflection and code generation.

2. Modern computers are heterogenous, they have 2 chips computing stuff, CPU and GPU. Even when these pieces are on the same chip and using the same RAM like Intel’s UHD graphics or AMD’s APUs, GPU can be still faster for some problems. Not only because more GFlops (that’s not necessarily true for integrated graphics), also because GPUs have better strategy for dealing with RAM latency. They switch threads instead of waiting for the data to arrive. CPU cores only run 1-2 hardware threads each i.e. limited in that regard.

That’s how for some problems HLSL or OpenCL can actually be faster than C.

This title states "Python is Faster Than C++", which neither implies that this is just an opinion, nor that it isn't an absolute statement. You have to figure out yourself that it's probably hyperbolic and just referring to special cases.

OTOH, JIT runtimes have more input data than a C compiler. They can implement some runtime equivalent of C++ profile-guided optimization: measure what actually happens in runtime, assume the input data is going to stay roughly the same, and re-generate machine code with this new information into something more efficient.

Pretty sure modern Java does that sometimes.

> In Python, JavaScript, Ruby, you have a lot of pointers to objects.

In C# you can make data structures which don’t use pointers much, or at all. The language is strongly typed, has value types, it’s easy to place structures inside other structures. There’s a native stack just like C, and even stackalloc keyword which is an equivalent of alloca() in C.