> In the future the Python developers may decide to turn on further optimizations based on this argument, for example link-time optimizations (LTO), though they haven't worked out all the bugs for that one yet.
(Thanks mastax for finding the issue!)
Profile-guided optimization, in layman terms, means that you run you code under a profiler for a while, see what parts (branches, functions, data structures etc) are being used the most and use this information to make a build of your code that considers the profiler's findings when doing optimization.
So what does it mean? It means, basically, that revsys is publishing a python build that is optimized for their use-case. Which may or may not be your use-case. This is not good nor bad.
Still, the claim "up to 19% faster" is false in general (but true in a particular case -- their use case).
Just keep this in mind, because this python build might perform worse than a regular python build.
We did 10 runs of each on a large AWS instance and averaged the results.
The build info and benchmark results are up here https://github.com/revsys/optimized-python-docker if we’re wrong about something please let us know.
However, definitely agree that any optimization like this could have negative performance consequences for certain specific bits of code. But that’s likely true of any build to the next.
Here's the Dockerfiles and readme:
I have some trust concerns about basing my infrastructure on something opaque.
Another alternative for a fast speed up by changing your base image is trying out the pypy images.
