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

More Information
  • 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.

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.

  • Master using NumPy, SciPy, and Cython to speed up your numerical computations.
  • Leverage the power of multiprocessing and multithreading in Python for parallelism.
  • Master using Dask to handle large data in a distributed setting and reactive applications in Python.
Course Length 4 hours 12 minutes
ISBN 9781789956252
Date Of Publication 28 Feb 2019


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.