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...