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

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

Mohammed Kashif

Thursday, February 28, 2019

Build high-performance, distributed, and concurrent applications in Python

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Video Details

ISBN 13 9781789956252

Course Length 4 hours 12 minutes

Getting Started with Faster and Efficient Python Code

The Course Overview

Exploring Python Datatypes

Using Lambda Expressions

Comprehensions for Speedups

Generators and Iterators

Using Decorators for Time Analysis

Parallel Programming in Python

Introduction to the Threading Module

Using Threads with Locks

Global Interpreter Lock

Multiprocessing in Python

Using a Pool of Workers

Using NumPy and SciPy to Speedup Computations

Introduction to NumPy

Exploring NumPy Arrays

Indexing in NumPy Arrays

Operations and Broadcasting on NumPy Arrays

Performance Comparison of NumPy Arrays

Combining SciPy with NumPy

Optimizing Python Code Using Cython

Introduction to Cython

Implement a Program Using Cython

Time Analysis of a Cython Program

Cython Data Types

Using Cython Functions

Combining NumPy and Cython

Speeding Up Your Python Code Using Numba

Introduction to Numba

Setting Up Numba

Creating Your First Program with Numba

Digging Deeper into Numba

Threading Using Numba

Performance Comparison with Numba

Distributed Computing Using Python

Introduction to Synchronous Programming

Understanding Asynchronous Programming

Asynchronous Programming in Python

Distributed Systems Architecture

Distributed Programming Using Dask

Introduction to Dask

Setting Up Dask

Blocked Algorithms and Dask Arrays

Writing Your First Program Using Dask

Using @delayed to Parallelize Code

Performance Comparison with Dask

Reactive Programming Using Python

Introduction to Reactive Programming

Observables and Observers

Overview of Data Operators

Reactive Programming in Python Using RxPy

Using Data Operators with RxPy

Video Description

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

Style and Approach

This hands-on course covers all the important aspects of high-performance computing using Python 3.x. Throughout the course, we'll go over the various techniques, modules, frameworks, and architectures needed for high-performance computing. This course is designed with minimal theory and maximal practical implementation followed by step-by-step instructions to get you up-and-running.

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What You Will Learn

Use lambda expressions, generators, and iterators to speed up your code.
A solid understanding of multiprocessing and multithreading in Python.
Optimize performance and efficiency by leveraging NumPy, SciPy, and Cython for numerical computations.
Load large data using Dask in a distributed setting.
Leverage the power of Numba to make your Python programs run faster.
Build reactive applications using Python.

Authors

Mohammed Kashif

Mohammed Kashif works as a Data Scientist at Nineleaps, India, dealing mostly with graph data analysis. Prior to this, he was working as a Python developer at Qualcomm. He completed his Master's degree in computer science from IIIT Delhi, with specialization in data engineering. His areas of interest include recommender systems, NLP, and graph analytics. In his spare time, he likes to solve questions on StackOverflow and help debug other people out of their misery. He is also an experienced teaching assistant with a demonstrated history of working in the higher-education industry.

Getting Started with Faster and Efficient Python Code

The Course Overview

Exploring Python Datatypes

Using Lambda Expressions

Comprehensions for Speedups

Generators and Iterators

Using Decorators for Time Analysis

Parallel Programming in Python

Introduction to the Threading Module

Using Threads with Locks

Global Interpreter Lock

Multiprocessing in Python