Check order status

Become part of a community of book lovers from all over the world and get access to a whole bunch of benefits. Create an account for free

Austrian Post 5.49 € DPD courier 3.99 € DPD point 2.99 €

Contact

How to shop

Help

My account

▸ Empty :-(

Concurrent NumPy in Python

Name: Concurrent NumPy in Python
Brand: Independently published
SKU: 51089320
Price: 35.99 EUR
Availability: InStock
Author: Jason Brownlee
ISBN: 9798862038057

Faster NumPy With BLAS, Python Threads, and Multiprocessing

Jason Brownlee

Language

English

Book Paperback

Libristo code: 51089320

Publishers Independently published, September 2023

Concurrency in NumPy is not an afterthoughtDiscover matrix multiplication that is 2.7x faster.Discov... Full description

Libristo code: 51089320

88 b

35.99 € VAT included

In stock at our supplier Shipping in 9-15 days

Delivery to Austria

30-day return policy

Customers also purchased

Wanderbuch für die Mark Brandenburg E. Albrecht

Paperback

33.90 €

El libro completo del yoga : el yoga de la respiración, de las posturas y de la meditación James Hewitt

Paperback

27.29 €

Perspektive Beruf und Beschäftigung Marco Böhss

Paperback

35.90 €

Concurrency in NumPy is not an afterthought

Discover matrix multiplication that is 2.7x faster.
Discover array initialization that is up to 3.2x faster.
Discover sharing copied arrays that is up to 516.91x faster.

NumPy is how we represent arrays of numbers in Python.

An entire ecosystem of third-party libraries has been developed around NumPy arrays, from machine learning and deep learning to image and computer vision and more.

Given the wide use of NumPy, it is essential we know how to get the most out of our system when using it.

We cannot afford to have CPU cores sit idle when performing mathematical operations on arrays.

Therefore we must know how to correctly harness concurrency in NumPy, such as:

NumPy has multithreaded algorithms and functions built-in (using BLAS).
NumPy will release the infamous GIL so Python threads can run in parallel.
NumPy arrays can be shared efficiently between Python processes using shared memory.

The problem is, no one is talking about how.

Introducing: "Concurrent NumPy in Python". A new book designed to teach you how to bring concurrency to your NumPy programs in Python, super fast!

You will get fast-paced tutorials showing you how to bring concurrency to the most common NumPy tasks.

Including:

Parallel array multiplication, common math functions, matrix solvers, and decompositions.
Parallel array filling and parallel creation of arrays of random numbers.
Parallel element-wise array arithmetic and common array math functions
Parallel programs for working with many NumPy arrays with thread and process pools.
Efficiently share arrays directly, and copies of arrays between Python processes.

Don't worry if you are new to NumPy programming or concurrency, you will also get primers on the background required to get the most out of this book, including:

The importance of concurrency when using NumPy and the cost of approaching it naively.
How to perform common NumPy operations and math functions.
How to install, query, and configure BLAS libraries for built-in multithreaded NumPy functions.
How to use Python concurrency APIs including threading, multiprocessing, and pools of workers.

Each tutorial is carefully designed to teach one critical aspect of how to bring concurrency to your NumPy projects.

Learn Python concurrency correctly, step-by-step.