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 7
Distributed Programming Using Dask
Content Locked
Section 5
Using @delayed to Parallelize Code
Sometimes it is not possible to simply use Dask arrays to achieve parallelism. In such cases, we can use @delayed decorator to parallelize our custom code. - Explore how @delayed works - Implement a program with @delayed to speedup code - Visualize task graph for Parallelized code