High Performance Python: Practical Performant Programming for Humans (Paperback)
Micha Gorelick, Ian Ozsvald
- 出版商: O'Reilly|英文2書85折
- 出版日期: 2014-09-20
- 售價: $1,840
- 貴賓價: 9.5 折 $1,748
- 語言: 英文
- 頁數: 370
- 裝訂: Paperback
- ISBN: 1449361595
- ISBN-13: 9781449361594
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相關分類:
Python、程式語言
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相關翻譯:
高效能 Python 程式設計 (High Performance Python: Practical Performant Programming for Humans) (繁中版)
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其他版本:
High Performance Python: Practical Performant Programming for Humans, 2/e (Paperback)
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商品描述
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
商品描述(中文翻譯)
您的Python程式碼可能能正確運行,但您需要讓它運行得更快。透過探索設計選擇背後的基本理論,這本實用指南將幫助您更深入地了解Python的實現。您將學習如何找到性能瓶頸並在高數據量程序中顯著加快代碼速度。
您如何利用多核架構或叢集?或者構建一個可以在不損失可靠性的情況下進行擴展和縮小的系統?有經驗的Python程序員將學習到這些和其他問題的具體解決方案,以及使用高性能Python進行社交媒體分析、生產化機器學習等情況的公司的實戰故事。
- 更好地掌握numpy、Cython和性能分析工具
- 學習Python如何抽象底層計算機架構
- 使用性能分析工具找到CPU時間和內存使用的瓶頸
- 通過選擇適當的數據結構來編寫高效的程序
- 加快矩陣和向量計算速度
- 使用工具將Python編譯為機器碼
- 同時管理多個I/O和計算操作
- 將多進程代碼轉換為在本地或遠程叢集上運行
- 在使用更少內存的情況下解決大型問題