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Table of contents
1.
Introduction
2.
A Brief About CuPy and NumPy
3.
Features of CuPy
4.
Features of NumPy
5.
Comparison Table between CuPy and NumPy
6.
Examples
7.
Frequently Asked Questions
7.1.
Write some limitations to Cupy.
7.2.
Is CuPy compatible with NumPy? 
7.3.
What are the pre-requisites for installing CuPy?
8.
Conclusions
Last Updated: Mar 27, 2024
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CuPy v/s NumPy

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Introduction

Welcome, Ninjas. Most of us have done programming in Python. We have used the most common library NumPy in python, to perform computations of 2-D arrays, or simply, matrices. But are you familiar with the CuPy library? Are you curious to learn more about it? 

CuPy vs NumPy

Well, let us get started!

A Brief About CuPy and NumPy

We will be looking into specific features of both libraries but before that, here's a brief introduction.

Numpy is an open-source Python language library that provides for large, multi-dimensional arrays and matrices, along with an extensive collection of high-level mathematical functions to operate on these arrays. It is a general-purpose array and matrices processing package for scientific computations.

CuPy is also an open-source library that implements Numpy arrays on Nvidia GPUs. With that implementation, the ideal parallel speedup can be achieved due to the many CUDA cores GPUs have.

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Features of CuPy

CuPy is now used very frequently. Some of its features include

  • With CuPy, we can write custom Python code that leverages CUDA and GPU speedups if you have something specific that isn’t yet supported. 
  • CuPy’s interface mirrors the Numpy 
  • It can be used as a direct replacement.
  • CuPy supports the array operations of Numpy, like indexing, broadcasting, math on arrays, and matrix transformations
cupy icon

Features of NumPy

NumPy is a powerful library for numerical computing in Python. Some of the main features of NumPy include the following:

  • NumPy supports for fast operations on arrays, including element-wise operations, mathematical functions, and linear algebra operations.
  • It supports creating and manipulating multidimensional arrays, also known as ndarrays. 
  • It provides functions for statistical analysis, such as mean, median, standard deviation, and linear algebra.
  • It supports many data types, including integers, floating-point, and complex numbers.
  • Provides Tools for reading and writing data from various file formats, such as CSV and binary files.
NumPy

Comparison Table between CuPy and NumPy

The interface of CuPy is designed to obey that of NumPy. However, there are some differences.

Basis CuPy NumPy
Execution model CuPy uses the GPU (through the CUDA programming model) to execute computations. NumPy executes all computations on the CPU
Compatibility CuPy is designed to be fully compatible with NumPy so that you can use it as a drop-in replacement for NumPy in most cases. Not all NumPy functions are compatible with CuPy, so you may need to use CuPy-specific functions for some operations.
Hardware requirements To use CuPy, you need a GPU and the CUDA toolkit installed on your system. NumPy, on the other hand, can be used on any system with a CPU
Performance CuPy generally provides faster performance than NumPy for certain types of operations that can be Parallelized and executed on the GPU

In some cases, NumPy may be faster than Cupy.

Examples

CuPy 

To use CuPy, you will first need to install it and ensure that you have a compatible GPU and the necessary CUDA drivers and tools. You can then import CuPy and use it to define and run functions on the GPU.

Here's an example of how you might define and run a simple CuPy function that computes the element-wise sum of two arrays:

import cupy as cp
def add_arrays(a, b):
    return a + b
# Create two arrays
a = cp.array([1, 2, 3, 4])
b = cp.array([5, 6, 7, 8])
# Run the function on the GPU
result = add_arrays(a, b)
print(result) # prints [6 8 10 12]

There are various other functions, such as cp.zeros() or cp.ones(), to create arrays on the GPU and use its math functions, such as cp.exp() or cp.sin(), to perform element-wise operations on GPU arrays.

It's important to note that CuPy functions can only operate on GPU arrays, so if you want to use a NumPy array as input to a CuPy function, you will need to convert it to a CuPy array using the cp.asarray() function. Similarly, if you want to convert a CuPy array to a NumPy array, you can use the cp.asnumpy() function.

NumPy

Here’s an example of array addition using the NumPy library.

import numpy as np
a = [2, 4, 9, 11] 
b = np.add(a, 1)
print(b)
#Using numpy add() function with two input arrays
a = np.array([1, 3, 7])
b = np.array([6, 9, 1])
c = np.add(a, b) 
print(c)
Output

Frequently Asked Questions

Write some limitations to Cupy.

Some limitations of using CuPy include the need for a GPU and the CUDA toolkit and not all NumPy functions are compatible with CuPy. Additionally, CuPy may not always provide significant performance improvements over NumPy, depending on the operations performed and the hardware used.

Is CuPy compatible with NumPy? 

CuPy is designed to be fully compatible with NumPy so that you can use it as a drop-in replacement for NumPy in most cases. This means that you can use CuPy arrays instead of NumPy arrays, and many NumPy functions will also work on CuPy arrays.

What are the pre-requisites for installing CuPy?

The prerequisites are NVIDIA CUDA GPU, the CUDA toolkit and Python.

Conclusions

We hope this blog helped you understand the difference between NumPy and CuPy. Firstly, We discussed the basic features of both, followed by their comparison under various fields.

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