Le 19 nov. 2016 21:29, "serge guelton" email@example.com a écrit :
Thanks *a lot* victor for this great article. You not only very accurately describe the method you used to track the performance bug, but also give very convincing results.
You're welcome. I'm not 100% sure that adding the hot attrbute makes the performance of call_method reliable at 100%. My hope is that the 70% slowdown doesn't reoccur.
I still wonder what the conclusion should be:
- (this) Micro benchmarks are not relevant at all, they are sensible to
factors that are not relevant to bigger applications
Other benchmarks had peaks: logging_silent and json_loads. I'm unable to say if microbenchmarks must be used or not to cehck for performance regression or test the performance of a patch. So I try instead to analyze and fix performance issues.
At least I can say that temporary peaks are higher and more frequent on microbenchmark.
Homework: define what is a microbenchmark :-)
- There is a generally good code layout that favors most applications?
This is an hard question. I don't know the answer. The hot attributes put tagged functions in a separated ELF section, but I understand that inside the section, order is not deterministic.
Maybe the size of a function code matters too. What happens if a function grows? Does it impact other functions?
Maybe some core function from the interpreter ?
I chose to only tag the most famous functions of the core right now. I'm testing tagging functions of extensions like json but I'm not sure that the result is significant.
Why does PGO fails to ``find'' them?
I don't use PGO on speed-python.
I'm not sure that is PGO is reliable neither (reproductible performance).