Your Python code may run correctly, but you need it to run faster. Updated for Python 3, this expanded edition shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. By exploring the fundamental theory behind design choices, High Performance Python helps you gain a deeper understanding of Python’s implementation.
Your Python code may run correctly, but you need it to run faster. Updated for Python 3, this expanded edition shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. By exploring the fundamental theory behind design choices, High Performance Python helps you gain a deeper understanding of Python’s implementation.
How do you take advantage of multicore architectures or clusters? Or build a system that scales up and down without losing reliability? Experienced Python programmers will learn concrete solutions to many issues, along with war stories from companies that use high-performance Python for social media analytics, productionized machine learning, and more.
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 local or remote clusters
Deploy code faster using tools like Docker
作者简介
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Micha Gorelick cofounded Fast Forward Labs as resident mad scientist. The company was acquired by Cloudera in 2017. He works on many issues from machine learning to performant stream algorithms.
Ian is a chief data scientist and coach. He co-organizes the annualPyDataLondon conference with 700+ attendees and the associated 10,000+ member monthly meetup. He runs the established ...
Micha Gorelick cofounded Fast Forward Labs as resident mad scientist. The company was acquired by Cloudera in 2017. He works on many issues from machine learning to performant stream algorithms.
Ian is a chief data scientist and coach. He co-organizes the annualPyDataLondon conference with 700+ attendees and the associated 10,000+ member monthly meetup. He runs the established Mor Consulting Data Science consultancy in London and gives conference talks internationally, often as keynote speaker. He has 17 years ofexperience as a senior data science leader, trainer and team coach.For fun he's walked by his high-energy Springer Spaniel, surfs theCornish coast and drinks fine coffee. Past talks and articles can befound at: https://ianozsvald.com/
The main properties of interest in a computing unit are the number of operations it can do in one cycle and how many cycles it can do in one second. The first value is measured by its instructions per cycle (IPC),1 while the latter value is measured by its clock speed. These two measures are always competing with each other when new computing units are being made.
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Furthermore, while increasing clock speed almost immediately speeds up all programs running on that computational unit (because they are able to do more calculations per second), having a higher IPC can also drastically affect computing by changing the level of vectorization that is possible. Vectorization is when a CPU is provided with multiple pieces of data at a time and is able to operate on all of them at once. This s... (查看原文)
Simply adding more cores to a CPU does not always speed up a program’s execution time. This is because of something known as Amdahl’s law. Simply stated, Amdahl’s law says that if a program designed to run on multiple cores has some routines that must run on one core, this will be the bottleneck for the final speedup that can be achieved by allocating more cores.
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Furthermore, a major hurdle with utilizing multiple cores in Python is Python’s use of a global interpreter lock (GIL). The GIL makes sure that a Python process can only run one instruction at a time, regardless of the number of cores it is currently using. This means that even though some Python code has access to multiple cores at a time, only one core is running a Python instruction at any given time.
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While this may... (查看原文)
If you're an experienced Python programmer, High Performance Python will guide you through the various routes of code optimization. You'll learn how to use smarter algorithms and leverage peripheral technologies, such as numpy, cython, cpython, and various ...
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云破月
人民邮电出版社2017版
夏嘉莫
O'Reilly Media2014版
lisa
人民邮电出版社2017版
涅瓦纳
O'Reilly Media2014版
0 有用 csyangchen 2023-06-05 22:23:33 中国香港
例子感觉离实际太远, 感觉翻来覆去就是几个实际数据挖掘上不太会涉及到的玩具例子. 第一章讲的还行, 后面每章感觉讲的很糙, 然后很多又想讲, 但是又没展开.
0 有用 csyangchen 2023-06-05 22:23:33 中国香港
例子感觉离实际太远, 感觉翻来覆去就是几个实际数据挖掘上不太会涉及到的玩具例子. 第一章讲的还行, 后面每章感觉讲的很糙, 然后很多又想讲, 但是又没展开.