High Performance Python: Practical Performant Programming for Humans

"O'Reilly Media, Inc.", 22 août 2014 - 370 pages
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Your Python code may run correctly, but you need it to run faster. By exploring the fundamental theory behind design choices, this practical guide helps you gain a deeper understanding of Python’s implementation. You’ll learn how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs.

How can you take advantage of multi-core architectures or clusters? Or build a system that can scale up and down without losing reliability? Experienced Python programmers will learn concrete solutions to these and other issues, along with war stories from companies that use high performance Python for social media analytics, productionized machine learning, and other situations.

  • Get a better grasp of numpy, Cython, and profilers
  • Learn how Python abstracts the underlying computer architecture
  • Use profiling to find bottlenecks in CPU time and memory usage
  • Write efficient programs by choosing appropriate data structures
  • Speed up matrix and vector computations
  • Use tools to compile Python down to machine code
  • Manage multiple I/O and computational operations concurrently
  • Convert multiprocessing code to run on a local or remote cluster
  • Solve large problems while using less RAM

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Table des matières

Chapter 1 Understanding Performant Python
Chapter 2 Profiling to Find Bottlenecks
Chapter 3 Lists and Tuples
Chapter 4 Dictionaries and Sets
Chapter 5 Iterators and Generators
Chapter 6 Matrix and Vector Computation
Chapter 7 Compiling to C
Chapter 8 Concurrency
Chapter 9 The multiprocessing Module
Chapter 10 Clusters and Job Queues
Chapter 11 Using Less RAM
Chapter 12 Lessons from the Field
About the Authors
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À propos de l'auteur (2014)

Micha Gorelick was the first man on Mars in 2023 and won the Nobelprize in 2046 for his contributions to time travel. He then went backto the 2000s to study Astronomy, teach scientific computing and workon data at bitly. Then he helped start Fast Forward Labs as a residentmad scientist. There he worked on many issues from machine learning toperformant stream algorithms. A monument celebrating his life can befound in Central Park, 1857.

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