These are not the best benchmarks, but Python is indeed as slow as Perl, which I find insane considering that Python has 100-1000 more people working on the interpreter and performance has been a big emphasis the last few years.
Benchmarking 20 programming languages on N-queens and matrix multiplication
21–30 of 194 posts
Re: Benchmarking 20 programming languages on N-queens and matrix multiplication
#22Neat!
The Julia matmul implementation has its rows and columns flipped though - unlike C, Julia uses row-major matrices. This has large implications for speed.
Also, the code may be much faster if you enable SIMD in the function, which is disabled in the code because a) the code unnecessarily checks bounds at every index instead of at the top of the function, and b) float SIMD is opt-in since SIMD changes the rounding
Re: Benchmarking 20 programming languages on N-queens and matrix multiplication
#23What is this supposed to demonstrate? There appears to be roughly the same code structure, ported to every language, while for some languages, arbitrary optimizations are introduced (such as using `array` instead of `list` in Python). But nobody working in Python uses matrix multiplication code written in Python. They use NumPy, which is a de facto standard library for people working in the relevant fields. It's as m…
It demonstrates that Python needs libraries like NumPy. Few problems are more heavily optimized than matrix multiplication in practice, so comparing matrix multiplication benchmarks across languages with NumPy is not representative of real-world performance for most programming use cases. It also means that adding performance to an existing Python program requires dropping into a different language, which is not only…
> It demonstrates that Python needs libraries like NumPy.
People use matrix multiplication libraries (often written in Assembly) from every language if they really care about performance. That's because such libraries incorporate 100 PhD theses' worth of tricks that no individual can hope to reinvent in the course of solving another problem. There is absolutely nothing special about Python in this context.
> It also means that adding performance to an existing Python program requires dropping into a different language
As stated above, this applies to all languages. BLAS routines used for serious numerical work are hand-vectorized Assembly fine-tuned for each processor architecture, written by a few hyper-experts who do nothing else.
Nobody who needs performant matrix multiplication from C thinks "hey, let me just write two nested loops".
Re: Benchmarking 20 programming languages on N-queens and matrix multiplication
#24You should add a chart of the number of gzip'd bytes of source code.
IMO, uncompressed bytes is a better representation, because it can be used to compare relative expressive power for the particular problem. I'd bet Python cleans house here, but the write-only languages are a wild card.
"Expressive power" is a very subjective term, and uncompressed size is a bad proxy as it includes too many variables specific to coding conventions. Compressed size with a stupid enough algorithm (here gzip) is meant to reduce these variables. The true Kolmogorov complexity in comparison can't be computed, and too smart algorithms can start to infer enough about the language itself.
Re: Benchmarking 20 programming languages on N-queens and matrix multiplication
#25Aren’t JIT languages at a disadvantage since they are benchmarked through the CLI rather than using a benchmarking library to allow JIT to warmup?
What's CLI?
Re: Benchmarking 20 programming languages on N-queens and matrix multiplication
#26What is this supposed to demonstrate? There appears to be roughly the same code structure, ported to every language, while for some languages, arbitrary optimizations are introduced (such as using `array` instead of `list` in Python). But nobody working in Python uses matrix multiplication code written in Python. They use NumPy, which is a de facto standard library for people working in the relevant fields. It's as m…
It demonstrates that Python needs libraries like NumPy. Few problems are more heavily optimized than matrix multiplication in practice, so comparing matrix multiplication benchmarks across languages with NumPy is not representative of real-world performance for most programming use cases. It also means that adding performance to an existing Python program requires dropping into a different language, which is not only…
> It demonstrates that Python needs libraries like NumPy.
You need libraries to do _anything_ in Python. It's interpreted, so literally any call you make in Python will eventually make it back to something written in a compiled language (like a call to NumPy commands).
Re: Benchmarking 20 programming languages on N-queens and matrix multiplication
#27Why not run C# as AOT? It’s a one line change.
Do people usually run C# code AOT compiled?
Re: Benchmarking 20 programming languages on N-queens and matrix multiplication
#28Earlier quoted context omitted.
It demonstrates that Python needs libraries like NumPy. Few problems are more heavily optimized than matrix multiplication in practice, so comparing matrix multiplication benchmarks across languages with NumPy is not representative of real-world performance for most programming use cases. It also means that adding performance to an existing Python program requires dropping into a different language, which is not only…
You don't need any C knowledge to use numpy. In fact, its conceptual similarity with Matlab is possibly the single most important reason for its popularity. Many other problems do need specialized treatments that would indeed require other languages, but numpy is not a good counterexample.
You're missing the point. The point is that, for any application, Python needs an underlying C library to be fast. So if you need to solve problems where no such library exists, Python is slow.
In other words, Python IS slow, but it can call fast code written in other languages.
Re: Benchmarking 20 programming languages on N-queens and matrix multiplication
#29N-queens and matrix benchmark without Fortran and Wolfram Mathematica?
Re: Benchmarking 20 programming languages on N-queens and matrix multiplication
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