Book Image

Advanced Python Programming

By : Dr. Gabriele Lanaro, Quan Nguyen, Sakis Kasampalis
Book Image

Advanced Python Programming

By: Dr. Gabriele Lanaro, Quan Nguyen, Sakis Kasampalis

Overview of this book

This Learning Path shows you how to leverage the power of both native and third-party Python libraries for building robust and responsive applications. You will learn about profilers and reactive programming, concurrency and parallelism, as well as tools for making your apps quick and efficient. You will discover how to write code for parallel architectures using TensorFlow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark. With the knowledge of how Python design patterns work, you will be able to clone objects, secure interfaces, dynamically choose algorithms, and accomplish much more in high performance computing. By the end of this Learning Path, you will have the skills and confidence to build engaging models that quickly offer efficient solutions to your problems. This Learning Path includes content from the following Packt products: • Python High Performance - Second Edition by Gabriele Lanaro • Mastering Concurrency in Python by Quan Nguyen • Mastering Python Design Patterns by Sakis Kasampalis
Table of Contents (41 chapters)
Title Page
About Packt

How to simulate in Python

In this section, we will look at the results of Amdahl's Law through a Python program. Still considering the task of determining whether an integer is a prime number, as discussed in Chapter 8, Advanced Introduction to Concurrent and Parallel Programming, we will see what actual speedup is achieved through concurrency. If you already have the code for the book downloaded from the GitHub page, we are looking at the Chapter09/ file.

As a refresher, the function that checks for prime numbers is as follows:

# Chapter09/

from math import sqrt

def is_prime(x):
    if x < 2:
        return False

    if x == 2:
        return x

    if x % 2 == 0:
        return False

    limit = int(sqrt(x)) + 1
    for i in range(3, limit, 2):
        if x % i == 0:
            return False

    return x

The next part of the code is a function that takes in an integer that indicates the number of processors (workers) that we will be utilizing to concurrently...