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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Concurrent NumPy in Python: Faster NumPy With BLAS, Python Threads, and Multiprocessing

Concurrent NumPy in Python: Faster NumPy With BLAS, Python Threads, and Multiprocessing

Brownlee, Jason

Paperback

Series: Python Concurrency by Example, Book 2

Programming

ISBN13: 9798862038057
Publisher: Independently Published
Published: Sep 21 2023
Pages: 476
Weight: 1.39
Height: 0.96 Width: 6.00 Depth: 9.00
Language: English
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.

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