Faster Python, beyond semantic interposition

#501 has a useful suggestion for speeding up Python by ~20%. After that's done, it's actually possible to do better.

Host is Fedora 33. All tests were run with Python 3.9.

On host:

• Fedora's Python gives 200K pystone/sec.
• Conda-Forge Python gives 240K pystone/sec.

Running inside Docker 20.04 (cgroups v2 enabled):

• fedora:33 gives 173K pystone/sec.
• python:3.9-slim-buster, I get 169K pystone/sec.
• ubuntu:20.04 (no shared library): 183K pystone/sec.
• continuumio/miniconda3 with Python from Conda-Forge: 189K/sec

I am mystified why things are so much slower inside Docker. Some of this is clearly not because of the image, but the runtime. But notice the Ubuntu image is definitely faster.

With podman:

• python:3.9-slim-buster: 204K/sec
• continuumio/miniconda3 with Python from Conda-Forge: 230K/sec

Note that the Anaconda (default Conda) Python 3.9 does not appear faster, it's specifically whatever Conda-Forge does. I am trying to figure that out.

首先，使用 Docker 和 Podman，在 issue 中列出的 Fedora、Ubuntu、Debian slim 和 Conda-Forge 镜像上复现报告的 pystone 结果。比较这些运行之间不同的 Python 运行时和镜像条件。确定性能差距的具体、可复现原因并记录一项可执行的改进，即视为完成。