将 Python(可能通过中间 C 表示)编译成机器代码有多可行?
How feasible would it be to compile Python (possibly via an intermediate C representation) into machine code?
大概它需要链接到 Python 运行时库,并且 Python 标准库的任何部分(即 Python 本身)也需要编译(和链接).
Presumably it would need to link to a Python runtime library, and any parts of the Python standard library which were Python themselves would need to be compiled (and linked in) too.
此外,如果您想对表达式进行动态评估,则需要捆绑 Python 解释器,但也许不允许这样做的 Python 子集仍然有用.
Also, you would need to bundle the Python interpreter if you wanted to do dynamic evaluation of expressions, but perhaps a subset of Python that didn't allow this would still be useful.
它会提供任何速度和/或内存使用优势吗?推测 Python 解释器的启动时间将被消除(尽管共享库仍需要在启动时加载).
Would it provide any speed and/or memory usage advantages? Presumably the startup time of the Python interpreter would be eliminated (although shared libraries would still need loading at startup).
试试 ShedSkin Python-to-C++ 编译器,但它远非完美.如果只需要加速,还有 Psyco - Python JIT.但恕我直言,这是不值得的努力.对于代码的速度关键部分,最好的解决方案是将它们编写为 C/C++ 扩展.
Try ShedSkin Python-to-C++ compiler, but it is far from perfect. Also there is Psyco - Python JIT if only speedup is needed. But IMHO this is not worth the effort. For speed-critical parts of code best solution would be to write them as C/C++ extensions.
这篇关于将Python编译成机器码可行吗?的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持html5模板网!
如何在 Python 中将货币字符串转换为浮点数?How do I convert a currency string to a floating point number in Python?(如何在 Python 中将货币字符串转换为浮点数?)
在 Pandas 中解析多索引 Excel 文件Parsing a Multi-Index Excel File in Pandas(在 Pandas 中解析多索引 Excel 文件)
pandas 时间序列 between_datetime 函数?pandas timeseries between_datetime function?( pandas 时间序列 between_datetime 函数?)
pandas 重新采样到每月的特定工作日pandas resample to specific weekday in month( pandas 重新采样到每月的特定工作日)
Python - 如何标准化时间序列数据Python - how to normalize time-series data(Python - 如何标准化时间序列数据)
statsmodels 使用 ARMA 模型进行预测statsmodels forecasting using ARMA model(statsmodels 使用 ARMA 模型进行预测)