speed-comparison VS NumPy

For example, Java runs on the JVM (Java Virtual Machine) instead of running directly on the hardware, and it also has a garbage collector to handle memory management. Running on a virtual machine means your code is more abstracted: you only have to worry about the JVM and not about the platform you’re running on (since the JVM is the platform), and it’s more portable since your code can go on anything that runs the JVM. But running the JVM as an intermediate layer takes more computing power and so does running garbage collection, meaning that you experience a performance penalty. Here’s one benchmark I could find comparing the use of different programming languages to compute pi, in which Java took about 3x as long as C to complete the same task

Or you've got the speed comparisons (https://github.com/niklas-heer/speed-comparison) -- Python is probably something like 10% the speed of C/C++ (although, like I said, 99% of the time that's comparable to premature optimization).

Looks like a PR was merged yesterday to make the code more simd friendly https://github.com/niklas-heer/speed-comparison/pull/52

Sure, they could have chosen Python. But I doubt the language differences account for even a noticeable percentage of the slowness of Brew.

In NumPy with @, dot() or matmul():

In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.

Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:

numpy: A library for scientific computing in Python

Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.

I was looking at all the big projects like numpy, pytorch, flask, etc.

I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]