Large Performance Gap Between Internal IB and External Network Plugin about nccl HOT 12 OPEN

I don't have any explanation for the difference currently. I'd need to see full NCCL_DEBUG=INFO logs to diagnose it.
But the external plugin in NGC is this one: MLNX-SHARP-Plugin
The IB verbs code in the external plugin and NCCL internal plugin are kept pretty much in sync, so I cannot explain the difference in performance, unless you've enabled COLLNET and SHARP support in the IB network?

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Many thanks for you response. I'm sorry that I could not access the testing environment to have the detailed log message. But it looks the process to create the QP connections was no problem. Additionally, I'm sure that COLLNET and SHARP on switch side is not enabled because the problem can be reproduced in RoCE network. Anyway, this significant performance gap is a problem. And this issue can be reproduced in both IB and RoCE network.

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@AddyLaddy additionally, are you sure that the external plugin in NGC is this one: MLNX-SHARP-Plugin? It looks this one targets at the SHARP/COLLNET application scenario.

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That Nvidia developed plugin is provided with the Nvidia HPCX SW stack, which I assume is part of the NGC containers?
But there are only a handful of NCCL plugins available, and most of the others are for supporting Slingshot or AWS/EFA over libfabric. I don't know of any other external plugins that support IB Verbs apart from the one I linked above.
NB: That plugin supports both IB Verbs and SHARP/COLLNET and will identify itself with "IBext_vX" in the INFO logs.

RoCE doesn't support SHARP and unless you've explicitly requested COLLNET support then it will be disabled by default.

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Thanks for your information @AddyLaddy. I suspect Nvidia developed pugin in NGC has some kind of enhancement to boost the performance in NGC somehow. It's really tricky. Anyway, as far as my understanding, the bus bandwidh should reach 360+ GB/s in case of 4 H100 servers with 8x400Gbps network connection.

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We do try and keep both the external and internal IB plugin code lockstep, so I have no idea why there is a performance difference with the NVLSTree protocol. The NCCL_DEBUG=INFO logs may give some clues. Also adding NCCL_DEBUG_SUBSYS=INIT,ENV,GRAPH will give more information.

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OK. Let me check if we could have extra test window to have the detailed log messages. If you guys have chance to test this case, please try. It is much easy to reproduce this problem. Many thanks.

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Hello @AddyLaddy I'm not sure if the below information on this Nvidia page makes sense.

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The text above is not correct and I've asked the team responsible to update it.
The default P2P protocol for the SHARP/COLLNET plugin is still IB Verbs. You have to set an additional environment variable to switch to UCX.
All Nvidia sourced containers include HPCX so most Deep Learning training and testing is actually done with the IB Verbs code in the external plugin.
But we carry out extensive QA on each release, where we test both internal and external plugins on numerous platforms, for both correctness and performance.

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Hello @AddyLaddy FYI as attached - detailed log message in host system and NGC respectively.
all_reduce_in_host_system.txt
all_reduce_in_ngc.txt
Please let me know if any finding.
Thanks.

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Thanks for the log files. I've not seen any obvious reasons for the performance differences apart from the NCCL environment variables being set are different between the "host" and "ngc" runs.
With the "ngc" run I see the following extra env vars being set

NCCL_IB_PCI_RELAXED_ORDERING set by environment to 1.
NCCL_NVLS_ENABLE set by environment to 1.
NCCL_CROSS_NIC set by environment to 0.

But only NCCL_CROSS_NIC=0 is overriding the default NCCL library setting.
However, I am a bit concerned as to why these env variables are being set as they can cause sub-optimal behaviour:

NCCL_CROSS_NIC set by environment to 0.
NCCL_PXN_DISABLE set by environment to 1.
NCCL_NET_GDR_LEVEL set by environment to SYS

Also, we have found setting NCCL_IB_QPS_PER_CONNECTION=2 only helps on RoCE machines and should normally be used together with NCCL_IB_SPLIT_DATA_ON_QPS=0

Both runs don't appear to be using library binaries provided by Nvidia, so it's hard to tell what other changes may have been made to the source code. NCCL 2.19.44 is not an official version!

NCCL version 2.19.44+cuda12.2
Plugin Path : /workspace/infrawaves/nccl_2.19.44/build/lib/libnccl-net.so

Maybe as the next step you could unify the environment variable settings between "host" and "ngc" to see if that is the root cause of the performance differences?
But as you're not using official NCCL software versions, any further analysis may be difficult.

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Many thanks for your response and time, @AddyLaddy.

Will try by unifying the environment variables if I could have the test window. Additionally, we did update the nccl code. But I don't think that could cause the performance gap betweem host and ngc system. I suspect this problem could be reproduced by going with the original nccl code.

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