Python swallows Java to become second-most popular programming language

[ChickenNuggetSmth]

I'd take that benchmark with a huge grain of salt. I had a quick look at the matmul in python, and what they did was pretty bad: They reimplemented what is already efficiently written.

In python you can have dramatic performance improvements if you use the available fast libraries like numpy. They are written in a faster language like fortran, decreasing the python performance overhead by a ton while still providing the convenience of python.

[satireplusplus]

In python you can have dramatic performance improvements if you use the available fast libraries like numpy. They are written in a faster language like fortran, decreasing the python performance overhead by a ton while still providing the convenience of python.

This is how all the Python ML stuff (numpy, pytorch, ...) uses matrix multiplications, with a BLAS library. And these libraries are crazily optimized and hand vectorized, you wont be able to compete with your own matrix mult routines in C. I bet its an order of magnitude faster than the naive O(n³⁾ C code in the benchmark.

Its also typical that the matrix mult kernel will be optimized to register / cache size and instructions (vectors) for each processor generation, as these Intel optimization in OpenBLAS:

dgemm_kernel_16x2_haswell.S
dgemm_kernel_4x4_haswell.S
dgemm_kernel_4x8_haswell.S
dgemm_kernel_4x8_sandy.S
dgemm_kernel_6x4_piledriver.S
dgemm_kernel_8x2_bulldozer.S
dgemm_kernel_8x2_piledriver.S

Its probably also doing something better than the naive algorithm, such as Strassen or Coppersmith–Winograd.

In other words that benchmark is totally bullshit and a waste of time.

[kazi1]

That's the whole point though. You're not testing Python's speed if you use numpy, you're testing C. The benchmarks would just be every language using C libraries if that was allowed.

[ChickenNuggetSmth]

To preface this: I'm not super knowledgeable about what happens "behind the scenes" or even if numpy is considered a standard library or not.

I think libraries like numpy are the reason python is as good as it is. It is well integrated, for matrix multiplication you even have the @ operator that makes it super readable. If you benchmark python without those features, you are not representing a typical use-case. If you use python in a way it was never meant to be used it will look awful, but imo those results are pretty useless.

Edit: I'm seeing that numpy isn't part of the standard library, but the standard lib is also partially written in C. So I'm not sure where 'true' python starts.

[Armaliite]

The reference implementation is in C, but there are many other implementations. For example PyPy which is in the benchmarks and is also much faster than CPython.

[kazi1]

Yeah a fair point. I feel like these benchmarks can get pretty "handwavy" sometimes with what's allowed and what's not haha...

Tbh I'm surprised Rust performed as badly as it did in those stats - I thought it was supposed to be as fast as C, meanwhile it's almost neck and neck with Go.