@chandlerc1024 By any chance is the source code for your "RNG" random number generator used in your 2017 CPPcon talk available anywhere?pic.twitter.com/ZwYHbOox3H
I'm worried that the baby thinks people can't change.
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@chandlerc1024 By any chance is the source code for your "RNG" random number generator used in your 2017 CPPcon talk available anywhere?pic.twitter.com/ZwYHbOox3H
:blink: I probably have it ... somewhere ... lemme go spelunking...pic.twitter.com/lz8vZGsJ5B
Sorry for the deep pull, but your L1 cache miss rate for the 8mb case in this talk looked way lower than what we expected. So some of us were trying to figure out what actually happened. One (wild) guess was that RNG(count) produces highly coherent indices.
Possibly. I found the code. I can try to get it into a gist or something if useful. But to understand what went wrong w/ this, may be easier: its just a silly slide-code wrapper around `std::mt19937`, seeded the usual way and pushed through `std::uniform_int_distribution`.
Not sure exactly which standard library I was using, but my memory is that there have been a few bugs in the uniform distribution implementations in some. And MT isn't the best these days. But I didn't apply deep rigor to this, and so entirely possible there are other factors.
Super interested in what you end up with?
Will report back if we figure it out! Our only theories so far are a) bad RNG and b) the EPYC perf reading was new and flaky then. We haven't come up with anything else, except the absurd c) EPYC's prefetch predictor learned the whole random sequence (since it is repeated)
But we're pretty sure it has to be _something_ because we can't think of any plausible theory as to how a 64k L1 can handle 4mb of sequential reads and 4mb of random reads with a ~1% miss rate! It should have been in the high 40%, right?
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