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

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Message-ID: <19714cae-6b73-43ec-af7a-1455196561d1@arm.com>
Date: Sat, 28 Jun 2025 09:19:45 +0530
From: Dev Jain <dev.jain@....com>
To: Lorenzo Stoakes <lorenzo.stoakes@...cle.com>, siddhartha@...ip.in
Cc: linux-mm@...ck.org, linux-kernel@...r.kernel.org, mgorman@...e.de,
 Vlastimil Babka <vbabka@...e.cz>
Subject: Re: [PATCH] mm: limit THP alignment – performance gain observed in AI inference workloads


On 27/06/25 9:00 pm, Lorenzo Stoakes wrote:
> +cc Vlata
>
> On Fri, Jun 27, 2025 at 04:09:16PM +0530, siddhartha@...ip.in wrote:
>> Hi all,
>>
>> I wanted to share validation data from a Hugging Face-based AI inferencing
>> workload,
>> which was significantly impacted by the THP alignment logic introduced in
>> commit efa7df3e3bb5.
>>
>> Using transformer models with dynamic input lengths on Intel Xeon (Cooper
>> Lake),
>> we observed up to a 3200% throughput improvement after applying the patch
>> from Oct 2024:
>>
>>    mm: limit THP alignment of anonymous mappings to PMD-aligned sizes
> All congratulations are owed to Vlastimil Babka for doing this, cc'd :)
>
> I gather he enjoys novelty beer mugs as tokens of thanks ;)

I was wondering how the change can get us such a big optimization - the
alignment causes us to gain at most 1 extra PMD-THP mapping. Is there
something else I am missing?

I ask because when I was reading the code I was thinking whether a similar
change can be done for mTHPs.

>
>> Metrics:
>> - Model: BERT-base
>> - Inference engine: Transformers + ONNX Runtime
>> - Kernel: 6.6 vs patched 6.6.8
>> - Batch size: 8-32, input length: 64-512 tokens
>> - Metric: inference throughput (samples/sec)
>>
>> Thanks for the fix -- this change had real impact on a production-relevant
>> workload.
>>
>> Best Regards,
>> Siddhartha Sharma
>> ISV @ Kenip
>> Solution Link: https://www.intel.com/content/www/us/en/partner/showcase/offering/a5bHo00000045YUIAY/deadlock-clearance.html
>>