Supercharging NumPy with Numba

Supercharging NumPy with Numba

Numba makes Python/NumPy code runs faster. It achieves this by compiling your Python code into native machine code.

For the uninitiated Numba is an open-source JIT compiler that translates a subset of Python/NumPy code into an optimized machine code using the LLVM compiler library. In short Numba makes Python/NumPy code runs faster. It achieves this by compiling your Python code into native machine code. Before going into Numba details lets understand what are the problems with NumPy and how does Numba solves them. NumPy does not run in parallel. On the other hand Numba fully utilizes the parallel execution capabilities of your computer. NumPy functions are not going to use multiple CPU cores, never mind the GPU. You become dependent on NumPy functions as it is very difficult to write optimal custom NumPy ufuncs (universal functions). Other alternative is to write them in native Python but looping over individual array elements in Python is very slow.

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