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 multi-threaded and multi-node strategies. There's a lack of good learning and reference material available if you want to l...
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 multi-threaded and multi-node strategies. There's a lack of good learning and reference material available if you want to learn Python for highly computational tasks. Because of it, fields from physics to biology and systems infrastructure to data science are hitting barriers. They need the fast prototyping nature of Python, but too few people know how to wield it. This book will put you ahead of the curve.
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
lisa
人民邮电出版社2017版
涅瓦纳
1 有用 pea1211 2015-06-27 22:01:08
干货！
0 有用 amadeoh 2015-05-11 16:58:53
The last chapter alone is worth five stars
0 有用 夏夜寂寞属壁虎 2022-06-12 18:38:24
大概《如何解决 Python 海量数据和海量请求下的 I/O 瓶颈和 CPU 瓶颈》这个标题更能概括这本书的内容，讲的是不限语言比较通用的性能优化思路。这本书更适合数据的细分方向，我不做这行就不打星了。性能分析的一些思路对我有帮助。
0 有用 安德 2020-04-27 17:53:55
你跟我说性能，我跟你说调包。
0 有用 france 2018-10-15 11:12:56
Python参考资料
0 有用 夏夜寂寞属壁虎 2022-06-12 18:38:24
大概《如何解决 Python 海量数据和海量请求下的 I/O 瓶颈和 CPU 瓶颈》这个标题更能概括这本书的内容，讲的是不限语言比较通用的性能优化思路。这本书更适合数据的细分方向，我不做这行就不打星了。性能分析的一些思路对我有帮助。
0 有用 安德 2020-04-27 17:53:55
你跟我说性能，我跟你说调包。
0 有用 催眠 2019-11-23 13:19:24
读得太慢，以致于书中后面的内容已经有些过时，但本书最重要的还是提供了一个框架：如何系统地比较不同的数据结构、算法或模型。
0 有用 仰望星空 2019-08-12 20:03:14
基于实践的性能优化。
1 有用 vuiBGmj 2018-12-04 21:40:13
vector instruction set # cache miss / memory locality # profile # compile # python 3.3 才变长 unicode 表示省内存，才知身在福中啊