Improving GC collections: dynamic thresholds, single generation gc and time barriers

In the pursuit of trying to optimize GC runs it has been observed that the weak generational hypothesis may not apply that well to Python. This is because, according to this argument, in the presence of a mixel cycle GC + refcount GC strategy, young objects are mostly cleaned up by reference count, not by the GC. Is important as well that there is no segregation between the GC strategies and that the cycle GC needs to deal with objects that according to this will be mainly cleaned up by reference count alone.

This questions the utility of segregating GC by generations and indeed there is some evidence of this. I have been benchmarking the percentage of success of different generations in some programs (such as blach and mypy and a bunch of HTTP servers) and the success rate of the lower generations is generally small. Here is an example of running black over all the standard library:

Statistics for generation 0

Statistics for generation 1

Statistics for generation 2

I am currently investigating if having a single generation with a dynamic threshold that is similar to the strategy that we use currently for the last generation would be generally better to get better performance.

What do you think?

Linked PRs

• gh-100958

Start by reviewing the benchmark observations in this issue and the linked PR gh-100958. The proposed work concerns comparing the current generational strategy with a single generation and dynamic thresholds; completion would require a decided approach backed by performance results, but no specific files or tests are named here.