Jason Brownlee's Blog, page 19
June 20, 2023
Performance Cost of Naive Parallelism in NumPy
You can add parallelism to a numpy program to improve its performance. Nevertheless, there is a danger in naively adding parallelism to a program that uses numpy. This is for many reasons, such as some numpy math functions that are already multithreaded, not knowing how to configure multithreaded numpy functions, using the wrong type of […]
June 18, 2023
Why Numpy Parallelism is Important
Parallelism is an important consideration when using numpy. Numpy is perhaps the most common Python library for working with arrays of numbers. It is popular when used directly, such as when performing mathematical and linear algebra operations, as well as a popular basis for many other scientific Python libraries. Parallelism allows Python programs to make […]
June 15, 2023
7 Ways to Share a Numpy Array Between Processes
You can share numpy arrays between processes in Python. There are many ways to share a numpy array between processes, such as as a function argument, as an inherited global variable, via a queue or a pipe, as a RawArray or SharedMemory backed array, or via a proxy object. In this tutorial, you will discover […]
June 13, 2023
How to Share Numpy Array Using SharedMemory
You can share a numpy array between processes by using multiprocessing SharedMemory. In this tutorial, you will discover how to share a numpy array between processes using multiprocessing SharedMemory. Let’s get started. Need to Share Numpy Array Between Processes Python offers process-based concurrency via the multiprocessing module. Process-based concurrency is appropriate for those tasks that […]
June 11, 2023
Share a Numpy Array Between Processes Using a Manager
You can share a numpy array between processes by hosting it in a manager server process and sharing proxy objects for working with the hosted array. In this tutorial, you will discover how to share an array between processes using a manager. Let’s get started. Need to Share Numpy Array Between Processes Python offers process-based […]
June 8, 2023
Share a Numpy Array Between Processes Backed By RawArray
You can share a numpy array between processes by first creating a shared ctype RawArray and then using the RawArray as a buffer for a new numpy array. In this tutorial, you will discover how to share a numpy array between processes that is backed by a shared ctype RawArray. Let’s get started. Need to […]
June 6, 2023
Share Numpy Array Between Processes With Shared ctypes
You can share a numpy array between processes by copying it into a shared ctype array. In this tutorial, you will discover how to share a numpy array between processes using a ctype array. Let’s get started. Need to Share Numpy Array Between Processes Python offers process-based concurrency via the multiprocessing module. Process-based concurrency is […]
June 4, 2023
Share Numpy Array Between Processes Using Global Variable
You can share numpy arrays with child processes by inheriting global variables. In this tutorial, you will discover how to share a numpy array with child processes by variable inheritance. Let’s get started. Need to Share Numpy Array Between Processes Python offers process-based concurrency via the multiprocessing module. Process-based concurrency is appropriate for those tasks […]
June 1, 2023
Share Numpy Array Between Processes Using Function Arguments and Return Values
You can share numpy arrays between processes using function arguments. Numpy arrays can be returned from processes using simulated return values via pipe and queue data structures. In this tutorial, you will discover how to share numpy arrays between processes using function arguments and simulated return values. Let’s get started. Need to Share Numpy Array […]
May 30, 2023
Share Numpy Array Between Processes Using a Queue
You can share numpy arrays between processes using a queue. In this tutorial, you will discover how to share numpy arrays between processes using a queue. Let’s get started. Need to Share Numpy Arrays Between Processes Python offers process-based concurrency via the multiprocessing module. Process-based concurrency is appropriate for those tasks that are CPU-bound, as […]
