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Learning Concurrency in Python

You're reading from   Learning Concurrency in Python Build highly efficient, robust, and concurrent applications

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781787285378
Length 360 pages
Edition 1st Edition
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Concepts
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Author (1):
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Elliot Forbes Elliot Forbes
Author Profile Icon Elliot Forbes
Elliot Forbes
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Table of Contents (13) Chapters Close

Preface 1. Speed It Up! FREE CHAPTER 2. Parallelize It 3. Life of a Thread 4. Synchronization between Threads 5. Communication between Threads 6. Debug and Benchmark 7. Executors and Pools 8. Multiprocessing 9. Event-Driven Programming 10. Reactive Programming 11. Using the GPU 12. Choosing a Solution

Accelerate

Anaconda’s Accelerate package is a package that enables us to leverage the exceptional performance of Intel CPUs as well as Nvidia’s GPUs. It's an add-on to the anaconda package that from the documentation features: 

  • Binding to several CUDA libraries such as cuBLAS, cuFFT, cuSPARSE, cuRAND, and CUDA Sorting
  • Speed-boosted linear algebra operations in NumPy, SciPy, scikit-learn, and NumExpr libraries using Intel's Math Kernel Library
  • Accelerated variants of NumPy's built-in UFuncs
  • Increased-speed Fast Fourier Transformations (FFT) in NumPy

Much like the other libraries in this chapter, it's designed specifically to cope with huge datasets.

The official documentation of Anaconda’s Accelerate can be found at https://docs.continuum.io/accelerate/# It's unfortunately quite a sparse documentation at the time...

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