When NumPy is not fast enough

Introduction

Sometimes, even though NumPy is fast, it can be too slow for very large or complex tasks. Knowing when to look for faster options helps you save time and get results quicker.

You have a huge dataset and NumPy operations take too long.

You need to run many calculations repeatedly and want to speed them up.

Your code uses loops that slow down NumPy performance.

You want to use multiple CPU cores to do calculations faster.

You need to run your code on a GPU for better speed.

Syntax

NumPy

# No specific syntax, but common alternatives include:
# Using Numba to speed up functions
# Using multiprocessing for parallel tasks
# Using libraries like CuPy for GPU acceleration

NumPy is fast because it uses optimized C code under the hood.

When it is not enough, you can try other tools that work well with NumPy.

Examples

This example uses Numba to speed up a loop that sums squares of numbers.

NumPy

import numpy as np
from numba import njit

@njit
def sum_squares(arr):
    total = 0
    for x in arr:
        total += x * x
    return total

arr = np.arange(1000000)
print(sum_squares(arr))

This example uses CuPy to run the calculation on a GPU for faster results.

NumPy

import numpy as np
import cupy as cp

arr = np.arange(1000000)
cuda_arr = cp.asarray(arr)
result = cp.sum(cuda_arr ** 2)
print(result.get())

Sample Program

This program compares the time to sum squares of 10 million numbers using plain NumPy and Numba-accelerated function.

NumPy

import numpy as np
from numba import njit
import time

@njit
def sum_squares_numba(arr):
    total = 0
    for x in arr:
        total += x * x
    return total

arr = np.arange(10_000_000, dtype=np.float64)

start = time.time()
result_numpy = np.sum(arr ** 2)
end = time.time()
print(f"NumPy sum of squares: {result_numpy}")
print(f"NumPy time: {end - start:.4f} seconds")

start = time.time()
result_numba = sum_squares_numba(arr)
end = time.time()
print(f"Numba sum of squares: {result_numba}")
print(f"Numba time: {end - start:.4f} seconds")

Important Notes

Numba speeds up Python loops by compiling them to fast machine code.

GPU libraries like CuPy require a compatible graphics card.

Parallel processing can also help but needs careful coding.

Summary

NumPy is fast but can be slow for very large or complex tasks.

Use tools like Numba or CuPy to speed up calculations.

Always test and compare performance to choose the best method.