What I meant is that in typical use, producing hashes to use for inserting and/or looking things up in dictionaries is not a major performance-impact for Python.
I don't doubt that hashes can be produces quicker by dedicated hardware, but if you're using a small fraction of your time for that purpose to start with, then the gains from reducing it, are modest.
It'd be interesting benchmarking a variety of workloads in a variety of languages to see what fraction of time is used for hashing though, because I'm really just guessing here, and I could be wrong.
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