How computing in CuPy works on Python

The basics of GPU computing with CuPy can be very easily understood with a side-by-side comparison with the traditional use of NumPy code on Python.

Once we explore the simple terminologies, we will shift our focus towards actual GPU-accelerated computations for solving specific computational problems with CuPy.

If you recall our traditional NumPy program that was first described in the PyCUDA chapter, we implemented a function to multiply two array elements through numpy. The syntax we used to import numpy was the following:

import numpy as np

As you can see, numpy is abbreviated as np for convenience of use throughout the program code.

In case of CuPy, too, we can use a similar syntax, as shown here:

import cupy as cp

In our NumPy code, we used the following syntax to initialize two arrays of the double data type for N elements with zero...