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.

まず、issue に記載されている Fedora、Ubuntu、Debian slim、Conda-Forge の各イメージで、Docker と Podman を使って報告された pystone の結果を再現します。これらの実行間で異なる Python ランタイムとイメージの条件を比較します。具体的かつ再現可能な性能差の原因を特定し、実行可能な改善策を文書化できれば完了です。