| Message ID | 20240729023532.1555-4-laoar.shao@gmail.com (mailing list archive) |
|---|---|
| State | New |
| Headers | show |
| Series | mm: Introduce a new sysctl knob vm.pcp_batch_scale_max | expand |
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, 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. [snip] -- Best Regards, Huang, Ying
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.
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. -- Best Regards, Huang, Ying
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. -- Regards Yafang
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. -- Best Regards, Huang, Ying
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?
Yafang Shao <laoar.shao@gmail.com> writes: > 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. IIUC, they will use the default value. Then, if there is any performance regression, they can report it. > I believe we should hear Andrew's suggestion. Andrew, what is your opinion? -- Best Regards, Huang, Ying
On Mon, Jul 29, 2024 at 2:04 PM Huang, Ying <ying.huang@intel.com> wrote: > > Yafang Shao <laoar.shao@gmail.com> writes: > > > 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. > > IIUC, they will use the default value. Then, if there is any > performance regression, they can report it. Now we report it. What is your replyment? "Keep it in your downstream kernel." Wow, PANIC again. > > > I believe we should hear Andrew's suggestion. Andrew, what is your opinion? > > -- > Best Regards, > Huang, Ying -- Regards Yafang
Yafang Shao <laoar.shao@gmail.com> writes: > On Mon, Jul 29, 2024 at 2:04 PM Huang, Ying <ying.huang@intel.com> wrote: >> >> Yafang Shao <laoar.shao@gmail.com> writes: >> >> > 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. >> >> IIUC, they will use the default value. Then, if there is any >> performance regression, they can report it. > > Now we report it. What is your replyment? "Keep it in your downstream > kernel." Wow, PANIC again. This is not all of my reply. I suggested you to change the default value too. > >> >> > I believe we should hear Andrew's suggestion. Andrew, what is your opinion? >> -- Best Regards, Huang, Ying
On Mon, Jul 29, 2024 at 2:18 PM Huang, Ying <ying.huang@intel.com> wrote: > > Yafang Shao <laoar.shao@gmail.com> writes: > > > On Mon, Jul 29, 2024 at 2:04 PM Huang, Ying <ying.huang@intel.com> wrote: > >> > >> Yafang Shao <laoar.shao@gmail.com> writes: > >> > >> > 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. > >> > >> IIUC, they will use the default value. Then, if there is any > >> performance regression, they can report it. > > > > Now we report it. What is your replyment? "Keep it in your downstream > > kernel." Wow, PANIC again. > > This is not all of my reply. I suggested you to change the default > value too. For the upstream kernel, I don't have a strong justification to change the default value from 5 to 0. That's why I'm proposing to introduce a sysctl. For our downstream kernel, some system administrators want us to keep this value the same as the upstream because it works fine with the old default value of 5. Therefore, we can't set the default value of our downstream kernel to 0. Let's wait for Andrew's suggestion.
diff --git a/Documentation/admin-guide/sysctl/vm.rst b/Documentation/admin-guide/sysctl/vm.rst index e86c968a7a0e..aa29f2fdad7c 100644 --- a/Documentation/admin-guide/sysctl/vm.rst +++ b/Documentation/admin-guide/sysctl/vm.rst @@ -65,6 +65,7 @@ Currently, these files are in /proc/sys/vm: - page-cluster - page_lock_unfairness - panic_on_oom +- pcp_batch_scale_max - percpu_pagelist_high_fraction - stat_interval - stat_refresh @@ -845,6 +846,22 @@ panic_on_oom=2+kdump gives you very strong tool to investigate why oom happens. You can get snapshot. +pcp_batch_scale_max +=================== + +In page allocator, PCP (Per-CPU pageset) is refilled and drained in +batches. The batch number is scaled automatically to improve page +allocation/free throughput. But too large scale factor may hurt +latency. This option sets the upper limit of scale factor to limit +the maximum latency. + +The range for this parameter spans from 0 to 6, with a default value of 5. +The value assigned to 'N' signifies that during each refilling or draining +process, a maximum of (batch << N) pages will be involved, where "batch" +represents the default batch size automatically computed by the kernel for +each zone. + + percpu_pagelist_high_fraction ============================= diff --git a/mm/Kconfig b/mm/Kconfig index b4cb45255a54..41fe4c13b7ac 100644 --- a/mm/Kconfig +++ b/mm/Kconfig @@ -663,17 +663,6 @@ config HUGETLB_PAGE_SIZE_VARIABLE config CONTIG_ALLOC def_bool (MEMORY_ISOLATION && COMPACTION) || CMA -config PCP_BATCH_SCALE_MAX - int "Maximum scale factor of PCP (Per-CPU pageset) batch allocate/free" - default 5 - range 0 6 - help - In page allocator, PCP (Per-CPU pageset) is refilled and drained in - batches. The batch number is scaled automatically to improve page - allocation/free throughput. But too large scale factor may hurt - latency. This option sets the upper limit of scale factor to limit - the maximum latency. - config PHYS_ADDR_T_64BIT def_bool 64BIT diff --git a/mm/page_alloc.c b/mm/page_alloc.c index bfd44b65777c..8d6f9dc99387 100644 --- a/mm/page_alloc.c +++ b/mm/page_alloc.c @@ -273,6 +273,8 @@ int min_free_kbytes = 1024; int user_min_free_kbytes = -1; static int watermark_boost_factor __read_mostly = 15000; static int watermark_scale_factor = 10; +static int pcp_batch_scale_max = 5; +static int sysctl_6 = 6; /* movable_zone is the "real" zone pages in ZONE_MOVABLE are taken from */ int movable_zone; @@ -2334,7 +2336,7 @@ static void drain_pages_zone(unsigned int cpu, struct zone *zone) int count = READ_ONCE(pcp->count); while (count) { - int to_drain = min(count, pcp->batch << CONFIG_PCP_BATCH_SCALE_MAX); + int to_drain = min(count, pcp->batch << pcp_batch_scale_max); count -= to_drain; spin_lock(&pcp->lock); @@ -2462,7 +2464,7 @@ static int nr_pcp_free(struct per_cpu_pages *pcp, int batch, int high, bool free /* Free as much as possible if batch freeing high-order pages. */ if (unlikely(free_high)) - return min(pcp->count, batch << CONFIG_PCP_BATCH_SCALE_MAX); + return min(pcp->count, batch << pcp_batch_scale_max); /* Check for PCP disabled or boot pageset */ if (unlikely(high < batch)) @@ -2494,7 +2496,7 @@ static int nr_pcp_high(struct per_cpu_pages *pcp, struct zone *zone, return 0; if (unlikely(free_high)) { - pcp->high = max(high - (batch << CONFIG_PCP_BATCH_SCALE_MAX), + pcp->high = max(high - (batch << pcp_batch_scale_max), high_min); return 0; } @@ -2564,9 +2566,9 @@ static void free_unref_page_commit(struct zone *zone, struct per_cpu_pages *pcp, } else if (pcp->flags & PCPF_PREV_FREE_HIGH_ORDER) { pcp->flags &= ~PCPF_PREV_FREE_HIGH_ORDER; } - if (pcp->free_count < (batch << CONFIG_PCP_BATCH_SCALE_MAX)) + if (pcp->free_count < (batch << pcp_batch_scale_max)) pcp->free_count = min(pcp->free_count + (1 << order), - batch << CONFIG_PCP_BATCH_SCALE_MAX); + batch << pcp_batch_scale_max); high = nr_pcp_high(pcp, zone, batch, free_high); if (pcp->count >= high) { free_pcppages_bulk(zone, nr_pcp_free(pcp, batch, high, free_high), @@ -2908,7 +2910,7 @@ static int nr_pcp_alloc(struct per_cpu_pages *pcp, struct zone *zone, int order) * subsequent allocation of order-0 pages without any freeing. */ if (batch <= max_nr_alloc && - pcp->alloc_factor < CONFIG_PCP_BATCH_SCALE_MAX) + pcp->alloc_factor < pcp_batch_scale_max) pcp->alloc_factor++; batch = min(batch, max_nr_alloc); } @@ -6275,6 +6277,15 @@ static struct ctl_table page_alloc_sysctl_table[] = { .proc_handler = percpu_pagelist_high_fraction_sysctl_handler, .extra1 = SYSCTL_ZERO, }, + { + .procname = "pcp_batch_scale_max", + .data = &pcp_batch_scale_max, + .maxlen = sizeof(pcp_batch_scale_max), + .mode = 0644, + .proc_handler = proc_dointvec_minmax, + .extra1 = SYSCTL_ZERO, + .extra2 = &sysctl_6, + }, { .procname = "lowmem_reserve_ratio", .data = &sysctl_lowmem_reserve_ratio,
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. 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. Future work =========== To ultimately mitigate the zone->lock contention issue, several suggestions have been proposed. One approach involves dividing large zones into multi smaller zones, as suggested by Matthew[2], while another entails splitting the zone->lock using a mechanism similar to memory arenas and shifting away from relying solely on zone_id to identify the range of free lists a particular page belongs to, as suggested by Mel[3]. However, implementing these solutions is likely to necessitate a more extended development effort. Link: https://lwn.net/Articles/981069/ [0] Link: https://github.com/iovisor/bcc/blob/master/tools/funclatency.py [1] Link: https://lore.kernel.org/linux-mm/ZnTrZ9mcAIRodnjx@casper.infradead.org/ [2] Link: https://lore.kernel.org/linux-mm/20240705130943.htsyhhhzbcptnkcu@techsingularity.net/ [3] Signed-off-by: Yafang Shao <laoar.shao@gmail.com> Cc: "Huang, Ying" <ying.huang@intel.com> Cc: Mel Gorman <mgorman@techsingularity.net> Cc: Matthew Wilcox <willy@infradead.org> Cc: David Rientjes <rientjes@google.com> --- Documentation/admin-guide/sysctl/vm.rst | 17 +++++++++++++++++ mm/Kconfig | 11 ----------- mm/page_alloc.c | 23 +++++++++++++++++------ 3 files changed, 34 insertions(+), 17 deletions(-)