IronPython vs Python vs for 循环的 C# 性能
IronPython vs Python vs C# performance of for loops
我对 Python 和 IronPython 比较陌生。我对我写的一个相对简单的代码的性能（我应该说慢）感到惊讶：
matrix_column_count=1000
matrix_row_count=2000
matrix=[[random.uniform(0,100) for i in range(matrix_column_count)] for j in range(matrix_row_count)]
运行 IronPython 2.7.10 需要大约 1 秒（有一些小的变化）！
运行 在 Python 3.8.6 上需要 0.55 秒
运行 C# 中类似的东西需要 0.03 秒！
Python 开发与 C# 开发相比，这些数字“正常”吗？还是我做错了什么？
是的，这些数字是正确的。由于我无法发表评论（堆栈溢出点不足），所以我将从堆栈溢出
复制粘贴另一个类似的答案
The answer is simply that Python deals with objects for everything and
that it doesn't have JIT by default. So rather than being very
efficient by modifying a few bytes on the stack and optimizing the hot
parts of the code (i.e., the iteration) – Python chugs along with rich
objects representing numbers and no on-the-fly optimizations.
If you tried this in a variant of Python that has JIT (for example,
PyPy) I guarantee you that you'll see a massive difference.
A general tip is to avoid standard Python for very computationally
expensive operations (especially if this is for a backend serving
requests from multiple clients). Java, C#, JavaScript, etc. with JIT
are incomparably more efficient.
在这里查看详细答案：
此外，您可能想查看 Python 基准测试与其他编程语言的详细比较，所以这里是链接，可能对您有用：
https://benchmarksgame-team.pages.debian.net/benchmarksgame/fastest/python.html
https://benchmarksgame-team.pages.debian.net/benchmarksgame/fastest/csharp.html
我对 Python 和 IronPython 比较陌生。我对我写的一个相对简单的代码的性能（我应该说慢）感到惊讶：
matrix_column_count=1000
matrix_row_count=2000
matrix=[[random.uniform(0,100) for i in range(matrix_column_count)] for j in range(matrix_row_count)]
运行 IronPython 2.7.10 需要大约 1 秒（有一些小的变化）！ 运行 在 Python 3.8.6 上需要 0.55 秒 运行 C# 中类似的东西需要 0.03 秒！
Python 开发与 C# 开发相比，这些数字“正常”吗？还是我做错了什么？
是的，这些数字是正确的。由于我无法发表评论（堆栈溢出点不足），所以我将从堆栈溢出
复制粘贴另一个类似的答案The answer is simply that Python deals with objects for everything and that it doesn't have JIT by default. So rather than being very efficient by modifying a few bytes on the stack and optimizing the hot parts of the code (i.e., the iteration) – Python chugs along with rich objects representing numbers and no on-the-fly optimizations.
If you tried this in a variant of Python that has JIT (for example, PyPy) I guarantee you that you'll see a massive difference.
A general tip is to avoid standard Python for very computationally expensive operations (especially if this is for a backend serving requests from multiple clients). Java, C#, JavaScript, etc. with JIT are incomparably more efficient.
在这里查看详细答案：
此外，您可能想查看 Python 基准测试与其他编程语言的详细比较，所以这里是链接，可能对您有用：
https://benchmarksgame-team.pages.debian.net/benchmarksgame/fastest/python.html
https://benchmarksgame-team.pages.debian.net/benchmarksgame/fastest/csharp.html