Book Image

High-Performance Computing with Python 3.x [Video]

By : Mohammed Kashif
Book Image

High-Performance Computing with Python 3.x [Video]

By: Mohammed Kashif

Overview of this book

Python is a versatile programming language. Many industries are now using Python for high-performance computing projects. This course will teach you how to use Python on parallel architectures. You'll learn to use the power of NumPy, SciPy, and Cython to speed up computation. Then you will get to grips with optimizing critical parts of the kernel using various tools. You will also learn how to optimize your programmer using Numba. You'll learn how to perform large-scale computations using Dask and implement distributed applications in Python; finally, you'll construct robust and responsive apps using Reactive programming. By the end, you will have gained a solid knowledge of the most common tools to get you started on HPC with Python. All code files are located on GitHub at this link https://github.com/PacktPublishing/High-Performance-Computing-with-Python-3.x
Table of Contents (8 chapters)
Chapter 3
Using NumPy and SciPy to Speedup Computations
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Section 1
Introduction to NumPy
NumPy is the standard module for performing numerical operations on arrays in Python. We need to understand the basics of NumPy arrays and how they are faster than traditional lists. - Understand the idea behind NumPy module - Explore the concept of NumPy arrays - Explore how NumPy arrays are faster than traditional lists