Re: [PATCH] mm: limit THP alignment – performance gain observed in AI inference workloads

[siddhartha@kenip.in]

On 2025-07-01 18:58, Lorenzo Stoakes wrote:
> On Tue, Jul 01, 2025 at 06:53:47PM +0530, siddhartha@kenip.in wrote:
>> Hi Lorenzo,
>> 
>> Thanks for your clarification, and I appreciate your patience — 
>> especially
>> given your role in maintaining and designing the VMA merge logic.
>> 
>> I understand now that my earlier phrasing may have repeated your 
>> explanation
>> for VMA adjacency, and I regret unintentionally restating it.
>> 
>> I’ll make sure to be more careful and direct going forward.
> 
> Thanks, no problem. Mostly avoids confusion.
> 
>> 
>> As for the THP alignment condition now being `IS_ALIGNED(len, 
>> PMD_SIZE)`, I
>> agree this resolves the regressions by removing alignment for 
>> non-aligned
>> sizes, which was exactly what broke workloads like cactusBSSN or some 
>> AI
>> inference loads.
> 
> Ack - we're really happy to hear about workloads that this has helped 
> as this
> kind of input is very important as to getting insight into how 
> THP-related stuff
> impacts real users so we can best optimise especially for workloads 
> that are
> very important in the industry right now.
> 
>> 
>> Thanks again for the guidance — I’m learning a lot from this thread.
> 
> Glad to have helped, thanks again for reporting!
> 
>> 
>> Best Regards,
>> Siddhartha Sharma
>> 
> 
> Cheers, Lorenzo

Hi Lorenzo,

Thanks for the acknowledgement of my work, I really appreciate it. 
Please let me know if there is anything I can do here now moving 
forwards with integrating. Furthermore, once integrated and tested, I 
would like to see all performance metrics that have seen improvements if 
possible.

Best Regards,
Siddhartha Sharma