High performance python practical performant programming for humans

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...

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Detalles Bibliográficos
Otros Autores: Gorelick, Micha, author (author), Ozsvald, Ian, author (editor), Blanchette, Meghan, editor (cover designer), Roumeliotis, Rachel, editor (proofreader), Montgomery, Karen, cover designer (illustrator), Monaghan, Rachel, proofreader, Demarest, Rebecca, illustrator
Formato: Libro electrónico
Idioma:Inglés
Publicado: Sebastopol, California : O'Reilly 2014.
Edición:First edition
Materias:
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629507606719
Descripción
Sumario: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
Notas:Includes index.
Descripción Física:1 online resource (370 p.)
Bibliografía:Includes bibliographical references and index.
ISBN:9781449361747
9781449361778