[PATCH v3 00/15] mm/memory: optimize fork() with PTE-mapped THP

[David Hildenbrand]

>> Note that regarding NUMA effects, I mean when some memory access within the same
>> socket is faster/slower even with only a single node. On AMD EPYC that's
>> possible, depending on which core you are running and on which memory controller
>> the memory you want to access is located. If both are in different quadrants
>> IIUC, the access latency will be different.
> 
> I've configured the NUMA to only bring the RAM and CPUs for a single socket
> online, so I shouldn't be seeing any of these effects. Anyway, I've been using
> the Altra as a secondary because its so much slower than the M2. Let me move
> over to it and see if everything looks more straightforward there.

Better use a system where people will actually run Linux production 
workloads on, even if it is slower :)

[...]

>>>
>>> I'll continue to mess around with it until the end of the day. But I'm not
>>> making any headway, then I'll change tack; I'll just measure the performance of
>>> my contpte changes using your fork/zap stuff as the baseline and post based on
>>> that.
>>
>> You should likely not focus on M2 results. Just pick a representative bare metal
>> machine where you get consistent, explainable results.
>>
>> Nothing in the code is fine-tuned for a particular architecture so far, only
>> order-0 handling is kept separate.
>>
>> BTW: I see the exact same speedups for dontneed that I see for munmap. For
>> example, for order-9, it goes from 0.023412s -> 0.009785, so -58%. So I'm
>> curious why you see a speedup for munmap but not for dontneed.
> 
> Ugh... ok, coming up.

Hopefully you were just staring at the wrong numbers (e.g., only with 
fork patches). Because both (munmap/pte-dontneed) are using the exact 
same code path.

-- 
Cheers,

David / dhildenb