Also saying the effort of PyPy is purely around speed is misleading. After all, another huge goal of the project was to implement a python interpreter in python, which they succeeded at.
It's in geometric mean, not average (see http://ece.uprm.edu/~nayda/Courses/Icom5047F06/Papers/paper4...). The same principle is applied to all testees. It's normal that certain benchmarks run faster than others. That's why we compare geometric means.
> Also comparing a volunteer project to an interpreter that has the resources of google behind it is IMO pretty unfair.
Didn't the project run for nearly twenty years with seveal rounds of EU funding? I think it's rather the approach than the team size or corporate support. See e.g. LuaJIT which was implemented by a single person in a shorter time frame and achieves similar performance like Node.js.
> Also saying the effort of PyPy is purely around speed is misleading
Didn's say that. But unfortunately also the other RPython based implementations also don't seem to be faster.
https://doc.pypy.org/en/release-1.9/index-report.html
https://ieeexplore.ieee.org/document/1667583
https://mail.python.org/pipermail/pypy-dev/2004-December/001...