From: Yafang Shao <laoar.shao@gmail.com>
To: "Huang, Ying" <ying.huang@intel.com>
Cc: akpm@linux-foundation.org, mgorman@techsingularity.net,
linux-mm@kvack.org, Matthew Wilcox <willy@infradead.org>,
David Rientjes <rientjes@google.com>
Subject: Re: [PATCH v2 3/3] mm/page_alloc: Introduce a new sysctl knob vm.pcp_batch_scale_max
Date: Mon, 29 Jul 2024 14:00:04 +0800 [thread overview]
Message-ID: <CALOAHbDF3veOjfLuoo8ufznvn2w1qxZR18iz3MASOZSiG3jB_A@mail.gmail.com> (raw)
In-Reply-To: <87zfq0wxub.fsf@yhuang6-desk2.ccr.corp.intel.com>
On Mon, Jul 29, 2024 at 1:54 PM Huang, Ying <ying.huang@intel.com> wrote:
>
> Yafang Shao <laoar.shao@gmail.com> writes:
>
> > On Mon, Jul 29, 2024 at 1:16 PM Huang, Ying <ying.huang@intel.com> wrote:
> >>
> >> Yafang Shao <laoar.shao@gmail.com> writes:
> >>
> >> > On Mon, Jul 29, 2024 at 11:22 AM Huang, Ying <ying.huang@intel.com> wrote:
> >> >>
> >> >> Hi, Yafang,
> >> >>
> >> >> Yafang Shao <laoar.shao@gmail.com> writes:
> >> >>
> >> >> > During my recent work to resolve latency spikes caused by zone->lock
> >> >> > contention[0], I found that CONFIG_PCP_BATCH_SCALE_MAX is difficult to use
> >> >> > in practice.
> >> >>
> >> >> As we discussed before [1], I still feel confusing about the description
> >> >> about zone->lock contention. How about change the description to
> >> >> something like,
> >> >
> >> > Sure, I will change it.
> >> >
> >> >>
> >> >> Larger page allocation/freeing batch number may cause longer run time of
> >> >> code holding zone->lock. If zone->lock is heavily contended at the same
> >> >> time, latency spikes may occur even for casual page allocation/freeing.
> >> >> Although reducing the batch number cannot make zone->lock contended
> >> >> lighter, it can reduce the latency spikes effectively.
> >> >>
> >> >> [1] https://lore.kernel.org/linux-mm/87ttgv8hlz.fsf@yhuang6-desk2.ccr.corp.intel.com/
> >> >>
> >> >> > To demonstrate this, I wrote a Python script:
> >> >> >
> >> >> > import mmap
> >> >> >
> >> >> > size = 6 * 1024**3
> >> >> >
> >> >> > while True:
> >> >> > mm = mmap.mmap(-1, size)
> >> >> > mm[:] = b'\xff' * size
> >> >> > mm.close()
> >> >> >
> >> >> > Run this script 10 times in parallel and measure the allocation latency by
> >> >> > measuring the duration of rmqueue_bulk() with the BCC tools
> >> >> > funclatency[1]:
> >> >> >
> >> >> > funclatency -T -i 600 rmqueue_bulk
> >> >> >
> >> >> > Here are the results for both AMD and Intel CPUs.
> >> >> >
> >> >> > AMD EPYC 7W83 64-Core Processor, single NUMA node, KVM virtual server
> >> >> > =====================================================================
> >> >> >
> >> >> > - Default value of 5
> >> >> >
> >> >> > nsecs : count distribution
> >> >> > 0 -> 1 : 0 | |
> >> >> > 2 -> 3 : 0 | |
> >> >> > 4 -> 7 : 0 | |
> >> >> > 8 -> 15 : 0 | |
> >> >> > 16 -> 31 : 0 | |
> >> >> > 32 -> 63 : 0 | |
> >> >> > 64 -> 127 : 0 | |
> >> >> > 128 -> 255 : 0 | |
> >> >> > 256 -> 511 : 0 | |
> >> >> > 512 -> 1023 : 12 | |
> >> >> > 1024 -> 2047 : 9116 | |
> >> >> > 2048 -> 4095 : 2004 | |
> >> >> > 4096 -> 8191 : 2497 | |
> >> >> > 8192 -> 16383 : 2127 | |
> >> >> > 16384 -> 32767 : 2483 | |
> >> >> > 32768 -> 65535 : 10102 | |
> >> >> > 65536 -> 131071 : 212730 |******************* |
> >> >> > 131072 -> 262143 : 314692 |***************************** |
> >> >> > 262144 -> 524287 : 430058 |****************************************|
> >> >> > 524288 -> 1048575 : 224032 |******************** |
> >> >> > 1048576 -> 2097151 : 73567 |****** |
> >> >> > 2097152 -> 4194303 : 17079 |* |
> >> >> > 4194304 -> 8388607 : 3900 | |
> >> >> > 8388608 -> 16777215 : 750 | |
> >> >> > 16777216 -> 33554431 : 88 | |
> >> >> > 33554432 -> 67108863 : 2 | |
> >> >> >
> >> >> > avg = 449775 nsecs, total: 587066511229 nsecs, count: 1305242
> >> >> >
> >> >> > The avg alloc latency can be 449us, and the max latency can be higher
> >> >> > than 30ms.
> >> >> >
> >> >> > - Value set to 0
> >> >> >
> >> >> > nsecs : count distribution
> >> >> > 0 -> 1 : 0 | |
> >> >> > 2 -> 3 : 0 | |
> >> >> > 4 -> 7 : 0 | |
> >> >> > 8 -> 15 : 0 | |
> >> >> > 16 -> 31 : 0 | |
> >> >> > 32 -> 63 : 0 | |
> >> >> > 64 -> 127 : 0 | |
> >> >> > 128 -> 255 : 0 | |
> >> >> > 256 -> 511 : 0 | |
> >> >> > 512 -> 1023 : 92 | |
> >> >> > 1024 -> 2047 : 8594 | |
> >> >> > 2048 -> 4095 : 2042818 |****** |
> >> >> > 4096 -> 8191 : 8737624 |************************** |
> >> >> > 8192 -> 16383 : 13147872 |****************************************|
> >> >> > 16384 -> 32767 : 8799951 |************************** |
> >> >> > 32768 -> 65535 : 2879715 |******** |
> >> >> > 65536 -> 131071 : 659600 |** |
> >> >> > 131072 -> 262143 : 204004 | |
> >> >> > 262144 -> 524287 : 78246 | |
> >> >> > 524288 -> 1048575 : 30800 | |
> >> >> > 1048576 -> 2097151 : 12251 | |
> >> >> > 2097152 -> 4194303 : 2950 | |
> >> >> > 4194304 -> 8388607 : 78 | |
> >> >> >
> >> >> > avg = 19359 nsecs, total: 708638369918 nsecs, count: 36604636
> >> >> >
> >> >> > The avg was reduced significantly to 19us, and the max latency is reduced
> >> >> > to less than 8ms.
> >> >> >
> >> >> > - Conclusion
> >> >> >
> >> >> > On this AMD CPU, reducing vm.pcp_batch_scale_max significantly helps reduce
> >> >> > latency. Latency-sensitive applications will benefit from this tuning.
> >> >> >
> >> >> > However, I don't have access to other types of AMD CPUs, so I was unable to
> >> >> > test it on different AMD models.
> >> >> >
> >> >> > Intel(R) Xeon(R) Platinum 8260 CPU @ 2.40GHz, two NUMA nodes
> >> >> > ============================================================
> >> >> >
> >> >> > - Default value of 5
> >> >> >
> >> >> > nsecs : count distribution
> >> >> > 0 -> 1 : 0 | |
> >> >> > 2 -> 3 : 0 | |
> >> >> > 4 -> 7 : 0 | |
> >> >> > 8 -> 15 : 0 | |
> >> >> > 16 -> 31 : 0 | |
> >> >> > 32 -> 63 : 0 | |
> >> >> > 64 -> 127 : 0 | |
> >> >> > 128 -> 255 : 0 | |
> >> >> > 256 -> 511 : 0 | |
> >> >> > 512 -> 1023 : 2419 | |
> >> >> > 1024 -> 2047 : 34499 |* |
> >> >> > 2048 -> 4095 : 4272 | |
> >> >> > 4096 -> 8191 : 9035 | |
> >> >> > 8192 -> 16383 : 4374 | |
> >> >> > 16384 -> 32767 : 2963 | |
> >> >> > 32768 -> 65535 : 6407 | |
> >> >> > 65536 -> 131071 : 884806 |****************************************|
> >> >> > 131072 -> 262143 : 145931 |****** |
> >> >> > 262144 -> 524287 : 13406 | |
> >> >> > 524288 -> 1048575 : 1874 | |
> >> >> > 1048576 -> 2097151 : 249 | |
> >> >> > 2097152 -> 4194303 : 28 | |
> >> >> >
> >> >> > avg = 96173 nsecs, total: 106778157925 nsecs, count: 1110263
> >> >> >
> >> >> > - Conclusion
> >> >> >
> >> >> > This Intel CPU works fine with the default setting.
> >> >> >
> >> >> > Intel(R) Xeon(R) Platinum 8260 CPU @ 2.40GHz, single NUMA node
> >> >> > ==============================================================
> >> >> >
> >> >> > Using the cpuset cgroup, we can restrict the test script to run on NUMA
> >> >> > node 0 only.
> >> >> >
> >> >> > - Default value of 5
> >> >> >
> >> >> > nsecs : count distribution
> >> >> > 0 -> 1 : 0 | |
> >> >> > 2 -> 3 : 0 | |
> >> >> > 4 -> 7 : 0 | |
> >> >> > 8 -> 15 : 0 | |
> >> >> > 16 -> 31 : 0 | |
> >> >> > 32 -> 63 : 0 | |
> >> >> > 64 -> 127 : 0 | |
> >> >> > 128 -> 255 : 0 | |
> >> >> > 256 -> 511 : 46 | |
> >> >> > 512 -> 1023 : 695 | |
> >> >> > 1024 -> 2047 : 19950 |* |
> >> >> > 2048 -> 4095 : 1788 | |
> >> >> > 4096 -> 8191 : 3392 | |
> >> >> > 8192 -> 16383 : 2569 | |
> >> >> > 16384 -> 32767 : 2619 | |
> >> >> > 32768 -> 65535 : 3809 | |
> >> >> > 65536 -> 131071 : 616182 |****************************************|
> >> >> > 131072 -> 262143 : 295587 |******************* |
> >> >> > 262144 -> 524287 : 75357 |**** |
> >> >> > 524288 -> 1048575 : 15471 |* |
> >> >> > 1048576 -> 2097151 : 2939 | |
> >> >> > 2097152 -> 4194303 : 243 | |
> >> >> > 4194304 -> 8388607 : 3 | |
> >> >> >
> >> >> > avg = 144410 nsecs, total: 150281196195 nsecs, count: 1040651
> >> >> >
> >> >> > The zone->lock contention becomes severe when there is only a single NUMA
> >> >> > node. The average latency is approximately 144us, with the maximum
> >> >> > latency exceeding 4ms.
> >> >> >
> >> >> > - Value set to 0
> >> >> >
> >> >> > nsecs : count distribution
> >> >> > 0 -> 1 : 0 | |
> >> >> > 2 -> 3 : 0 | |
> >> >> > 4 -> 7 : 0 | |
> >> >> > 8 -> 15 : 0 | |
> >> >> > 16 -> 31 : 0 | |
> >> >> > 32 -> 63 : 0 | |
> >> >> > 64 -> 127 : 0 | |
> >> >> > 128 -> 255 : 0 | |
> >> >> > 256 -> 511 : 24 | |
> >> >> > 512 -> 1023 : 2686 | |
> >> >> > 1024 -> 2047 : 10246 | |
> >> >> > 2048 -> 4095 : 4061529 |********* |
> >> >> > 4096 -> 8191 : 16894971 |****************************************|
> >> >> > 8192 -> 16383 : 6279310 |************** |
> >> >> > 16384 -> 32767 : 1658240 |*** |
> >> >> > 32768 -> 65535 : 445760 |* |
> >> >> > 65536 -> 131071 : 110817 | |
> >> >> > 131072 -> 262143 : 20279 | |
> >> >> > 262144 -> 524287 : 4176 | |
> >> >> > 524288 -> 1048575 : 436 | |
> >> >> > 1048576 -> 2097151 : 8 | |
> >> >> > 2097152 -> 4194303 : 2 | |
> >> >> >
> >> >> > avg = 8401 nsecs, total: 247739809022 nsecs, count: 29488508
> >> >> >
> >> >> > After setting it to 0, the avg latency is reduced to around 8us, and the
> >> >> > max latency is less than 4ms.
> >> >> >
> >> >> > - Conclusion
> >> >> >
> >> >> > On this Intel CPU, this tuning doesn't help much. Latency-sensitive
> >> >> > applications work well with the default setting.
> >> >> >
> >> >> > It is worth noting that all the above data were tested using the upstream
> >> >> > kernel.
> >> >> >
> >> >> > Why introduce a systl knob?
> >> >> > ===========================
> >> >> >
> >> >> > From the above data, it's clear that different CPU types have varying
> >> >> > allocation latencies concerning zone->lock contention. Typically, people
> >> >> > don't release individual kernel packages for each type of x86_64 CPU.
> >> >> >
> >> >> > Furthermore, for latency-insensitive applications, we can keep the default
> >> >> > setting for better throughput. In our production environment, we set this
> >> >> > value to 0 for applications running on Kubernetes servers while keeping it
> >> >> > at the default value of 5 for other applications like big data. It's not
> >> >> > common to release individual kernel packages for each application.
> >> >>
> >> >> Thanks for detailed performance data!
> >> >>
> >> >> Is there any downside observed to set CONFIG_PCP_BATCH_SCALE_MAX to 0 in
> >> >> your environment? If not, I suggest to use 0 as default for
> >> >> CONFIG_PCP_BATCH_SCALE_MAX. Because we have clear evidence that
> >> >> CONFIG_PCP_BATCH_SCALE_MAX hurts latency for some workloads. After
> >> >> that, if someone found some other workloads need larger
> >> >> CONFIG_PCP_BATCH_SCALE_MAX, we can make it tunable dynamically.
> >> >>
> >> >
> >> > The decision doesn’t rest with us, the kernel team at our company.
> >> > It’s made by the system administrators who manage a large number of
> >> > servers. The latency spikes only occur on the Kubernetes (k8s)
> >> > servers, not in other environments like big data servers. We have
> >> > informed other system administrators, such as those managing the big
> >> > data servers, about the latency spike issues, but they are unwilling
> >> > to make the change.
> >> >
> >> > No one wants to make changes unless there is evidence showing that the
> >> > old settings will negatively impact them. However, as you know,
> >> > latency is not a critical concern for big data; throughput is more
> >> > important. If we keep the current settings, we will have to release
> >> > different kernel packages for different environments, which is a
> >> > significant burden for us.
> >>
> >> Totally understand your requirements. And, I think that this is better
> >> to be resolved in your downstream kernel. If there are clear evidences
> >> to prove small batch number hurts throughput for some workloads, we can
> >> make the change in the upstream kernel.
> >>
> >
> > Please don't make this more complicated. We are at an impasse.
> >
> > The key issue here is that the upstream kernel has a default value of
> > 5, not 0. If you can change it to 0, we can persuade our users to
> > follow the upstream changes. They currently set it to 5, not because
> > you, the author, chose this value, but because it is the default in
> > Linus's tree. Since it's in Linus's tree, kernel developers worldwide
> > support it. It's not just your decision as the author, but the entire
> > community supports this default.
> >
> > If, in the future, we find that the value of 0 is not suitable, you'll
> > tell us, "It is an issue in your downstream kernel, not in the
> > upstream kernel, so we won't accept it." PANIC.
>
> I don't think so. I suggest you to change the default value to 0. If
> someone reported that his workloads need some other value, then we have
> evidence that different workloads need different value. At that time,
> we can suggest to add an user tunable knob.
>
The problem is that others are unaware we've set it to 0, and I can't
constantly monitor the linux-mm mailing list. Additionally, it's
possible that you can't always keep an eye on it either.
I believe we should hear Andrew's suggestion. Andrew, what is your opinion?
--
Regards
Yafang
next prev parent reply other threads:[~2024-07-29 6:00 UTC|newest]
Thread overview: 14+ messages / expand[flat|nested] mbox.gz Atom feed top
2024-07-29 2:35 [PATCH v2 0/3] mm: " Yafang Shao
2024-07-29 2:35 ` [PATCH v2 1/3] mm/page_alloc: A minor fix to the calculation of pcp->free_count Yafang Shao
2024-07-29 2:35 ` [PATCH v2 2/3] mm/page_alloc: Avoid changing pcp->high decaying when adjusting CONFIG_PCP_BATCH_SCALE_MAX Yafang Shao
2024-07-29 2:35 ` [PATCH v2 3/3] mm/page_alloc: Introduce a new sysctl knob vm.pcp_batch_scale_max Yafang Shao
2024-07-29 3:18 ` Huang, Ying
2024-07-29 3:40 ` Yafang Shao
2024-07-29 5:12 ` Huang, Ying
2024-07-29 5:45 ` Yafang Shao
2024-07-29 5:50 ` Huang, Ying
2024-07-29 6:00 ` Yafang Shao [this message]
2024-07-29 6:00 ` Huang, Ying
2024-07-29 6:13 ` Yafang Shao
2024-07-29 6:14 ` Huang, Ying
2024-07-29 7:50 ` Yafang Shao
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