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Message-ID: <20241202175459.2005526-1-sj@kernel.org> Date: Mon, 2 Dec 2024 09:54:58 -0800 From: SeongJae Park <sj@...nel.org> To: SeongJae Park <sj@...nel.org> Cc: damon@...ts.linux.dev, linux-mm@...ck.org, linux-kernel@...r.kernel.org, kernel-team@...a.com Subject: Re: A guide to DAMON tuning and results interpretation for hot pages On Fri, 8 Nov 2024 15:25:36 -0800 SeongJae Park <sj@...nel.org> wrote: > Hello DAMON community, > > > One of common issues that I received from DAMON users is that DAMON's > monitoring results show hot regions much less than expected. Specifically, the > users find regions of only zero or low 'nr_accesses' value from the > DAMON-generated access pattern snapshots. > > In some cases, it turned out the problem can be alleviated by tuning DAMON > parameters or changing the way to interpret the results. I'd like to share the > details and possible future improvements here. > > Note that I'm not saying this is users' tuning fault. I admit that the real > root cause of the issue is the poor interface and lack of guides that makes > correct tuning difficult, and the suboptimality of DAMON's mechanisms. We will > continue working on advancing it in long term. Sharing some of the plans and > status at the end of this email. > > TL; DR > ------ > > Users show only low or zero nr_accesses regions mainly because they set > 'aggregation intrval' too short compared to the workload's memory access > intensiveness. Please increase the aggregation interval, or treat > 'nr_accesses' zero regions of short 'age' as hot regions. > > Now let's walk through more details. The below sections assume you're familiar > with DAMON's monitoring mechanisms including 'Access Frequency Monitoring', > 'Regions Based Sampling', 'Adaptive Regions Adjustment', and 'Age Tracking'. > You should particularly be familiar with terms including 'sampling interval', > 'aggregation interval', 'nr_accesses', and 'age'. If you're not familiar with > those, you can refer to the document > (https://www.kernel.org/doc/html/next/mm/damon/design.html#monitoring). [...] > > Tuning Guide > ------------ > > Based on above root cause theories, I suggest to try below tuning guides. > > If you show DAMON is not working well at finding hot pages, > > 1. Ensure your workload is making meaningfully intensive data accesses. > 2. Gradually increase aggregation interval and show if it makes change. > 3. Try using 'age' information even if 'nr_accesses' is zero. > 4. If nothing works, report the problem to sj@...nel.org, damon@...ts.linux.dev > and/or linux-mm@...ck.org. > > If increasing aggregation interval alleviates your problem, you can further > consider increasing 'sampling interval'. If it doesn't harm the quality of the > access pattern snapshots, having low 'sampling interval' will only increase > DAMON's CPU usage. > > For using 'age' information of zero 'nr_accesses' regions, different approaches > could be used for profiling use case and DAMOS use case. For profiling use > case, users can try reading recency or access temperature based histograms > (https://github.com/damonitor/damo/blob/v2.5.4/USAGE.md#access-report-styles) > of snapshots from record, or live-captured snapshots. > > If the use case is for DAMOS, applying the 'age' information on DAMOS target > access pattern would be straightforward. Using DAMOS Quotas together can be > useful, since it provides its own under-quota-prioritization logic that > utilizes 'age' information for zero 'nr_accesses' regions, and further provides > auto-tuning of the quota for given target metric/value. I tried monitoring the access patterns on the physical address space of a system running a real-world server workload. I was able to reproduce the reported poor quality of hot pages detection using default parameter. And I was also able to improve the quality following the above tuning guide. I'm sharing the details as an example. 5ms/100ms intervals: Reproduce Problem -------------------------------------- Initially, I captured the access pattern snapshot on the physical address space using DAMON, with the default interval parameters (5 milliseconds and 100 milliseconds for the sampling and the aggregation intervals, respectively). I wait ten minutes after starting DAMON, to show a meaningful time-wise access patterns. ``` # damo start # sleep 600 # damo record --snapshot 0 1 # damo stop ``` Then, I listed the DAMON-found regions of different access patterns, sorted by the access temperature. Access temperature is calculated (https://github.com/damonitor/damo/blob/v2.5.8/src/damo_report_access.py#L643) as a weighted sum of the access frequency and the age of the region. If the access frequency is 0 %, the temperature is multipled by minus one. That is, if a region is not accessed, it gets minus temperature and it gets lower as not accessed for longer time. The sorting is in temperature-ascendint order, so the region at the top of the list is the coldest, and the one at the bottom is the hottest one. ``` # damo report access --sort_regions_by temperature 0 addr 16.052 GiB size 5.985 GiB access 0 % age 5.900 s # coldest 1 addr 22.037 GiB size 6.029 GiB access 0 % age 5.300 s 2 addr 28.065 GiB size 6.045 GiB access 0 % age 5.200 s 3 addr 10.069 GiB size 5.983 GiB access 0 % age 4.500 s 4 addr 4.000 GiB size 6.069 GiB access 0 % age 4.400 s 5 addr 62.008 GiB size 3.992 GiB access 0 % age 3.700 s 6 addr 56.795 GiB size 5.213 GiB access 0 % age 3.300 s 7 addr 39.393 GiB size 6.096 GiB access 0 % age 2.800 s 8 addr 50.782 GiB size 6.012 GiB access 0 % age 2.800 s 9 addr 34.111 GiB size 5.282 GiB access 0 % age 2.300 s 10 addr 45.489 GiB size 5.293 GiB access 0 % age 1.800 s # hottest total size: 62.000 GiB ``` The list shows not seemingly hot regions, and only minimum access pattern diversity. Every region has zero access frequency. The number of region is 10, which is the default `min_nr_regions` value. Size of each region is also nearly idential. We can suspect this is because "adaptive regions adjustment" mechanism was not well working. As the guide suggested, we can get relative hotness of regions using 'age' as the recency information. That would be better than nothing, but given the fact that the longest age is only about 6 seconds while we waited about ten minuts, it is unclear how useful this will be. The temperature ranges to total size of regions of each range histogram visualization (https://github.com/damonitor/damo/blob/v2.5.7/USAGE.md#access-report-styles) of the results also shows no interesting distribution pattern. ``` # damo report access --style temperature-sz-hist <temperature> <total size> [-,590,000,000, -,549,000,000) 5.985 GiB |********** | [-,549,000,000, -,508,000,000) 12.074 GiB |********************| [-,508,000,000, -,467,000,000) 0 B | | [-,467,000,000, -,426,000,000) 12.052 GiB |********************| [-,426,000,000, -,385,000,000) 0 B | | [-,385,000,000, -,344,000,000) 3.992 GiB |******* | [-,344,000,000, -,303,000,000) 5.213 GiB |********* | [-,303,000,000, -,262,000,000) 12.109 GiB |********************| [-,262,000,000, -,221,000,000) 5.282 GiB |********* | [-,221,000,000, -,180,000,000) 0 B | | [-,180,000,000, -,139,000,000) 5.293 GiB |********* | total size: 62.000 GiB ``` In short, the result is very similar to the reported problems: poor quality monitoring results for hot regions detection. According to the above guide, this is due to the too short aggregation interval. 100ms/2s intervals: Starts Showing Small Hot Regions ---------------------------------------------------- Following the guide, I increased the interval 20 times (100 milliseocnds and 2 seconds for sampling and aggregation intervals, respectively). ``` # damo start -s 100ms -a 2s # sleep 600 # damo record --snapshot 0 1 # damo stop # damo report access --sort_regions_by temperature 0 addr 10.180 GiB size 6.117 GiB access 0 % age 7 m 8 s # coldest 1 addr 49.275 GiB size 6.195 GiB access 0 % age 6 m 14 s 2 addr 62.421 GiB size 3.579 GiB access 0 % age 6 m 4 s 3 addr 40.154 GiB size 6.127 GiB access 0 % age 5 m 40 s 4 addr 16.296 GiB size 6.182 GiB access 0 % age 5 m 32 s 5 addr 34.254 GiB size 5.899 GiB access 0 % age 5 m 24 s 6 addr 46.281 GiB size 2.995 GiB access 0 % age 5 m 20 s 7 addr 28.420 GiB size 5.835 GiB access 0 % age 5 m 6 s 8 addr 4.000 GiB size 6.180 GiB access 0 % age 4 m 16 s 9 addr 22.478 GiB size 5.942 GiB access 0 % age 3 m 58 s 10 addr 55.470 GiB size 915.645 MiB access 0 % age 3 m 6 s 11 addr 56.364 GiB size 6.056 GiB access 0 % age 2 m 8 s 12 addr 56.364 GiB size 4.000 KiB access 95 % age 16 s 13 addr 49.275 GiB size 4.000 KiB access 100 % age 8 m 24 s # hottest total size: 62.000 GiB # damo report access --style temperature-sz-hist <temperature> <total size> [-42,800,000,000, -33,479,999,000) 22.018 GiB |***************** | [-33,479,999,000, -24,159,998,000) 27.090 GiB |********************| [-24,159,998,000, -14,839,997,000) 6.836 GiB |****** | [-14,839,997,000, -5,519,996,000) 6.056 GiB |***** | [-5,519,996,000, 3,800,005,000) 4.000 KiB |* | [3,800,005,000, 13,120,006,000) 0 B | | [13,120,006,000, 22,440,007,000) 0 B | | [22,440,007,000, 31,760,008,000) 0 B | | [31,760,008,000, 41,080,009,000) 0 B | | [41,080,009,000, 50,400,010,000) 0 B | | [50,400,010,000, 59,720,011,000) 4.000 KiB |* | total size: 62.000 GiB ``` DAMON found two distinct 4 KiB regions that pretty hot. The regions are also well aged. The hottest 4 KiB region was keeping the access frequency for about 8 minutes, and the coldest region was keeping no access for about 7 minutes. The distribution on the histogram also looks like having a pattern. Especially, the finding of the 4 KiB regions shows DAMON's adaptive regions adjustment is working as designed. Still the number of regions is close to the `min_nr_regions`, and sizes of cold regions are similar, though. Apparently it is improved, but it still has rooms to improve. 400ms/8s intervals: Pretty Improved Results ------------------------------------------- I further increased the intervals four times (400 milliseconds and 8 seconds for sampling and aggregation intervals, respectively). ``` # damo start -s 400ms -a 8s # sleep 600 # damo record --snapshot 0 1 # damo stop # damo report access --sort_regions_by temperature 0 addr 64.492 GiB size 1.508 GiB access 0 % age 6 m 48 s # coldest 1 addr 21.749 GiB size 5.674 GiB access 0 % age 6 m 8 s 2 addr 27.422 GiB size 5.801 GiB access 0 % age 6 m 3 addr 49.431 GiB size 8.675 GiB access 0 % age 5 m 28 s 4 addr 33.223 GiB size 5.645 GiB access 0 % age 5 m 12 s 5 addr 58.321 GiB size 6.170 GiB access 0 % age 5 m 4 s [...] 25 addr 6.615 GiB size 297.531 MiB access 15 % age 0 ns 26 addr 9.513 GiB size 12.000 KiB access 20 % age 0 ns 27 addr 9.511 GiB size 108.000 KiB access 25 % age 0 ns 28 addr 9.513 GiB size 20.000 KiB access 25 % age 0 ns 29 addr 9.511 GiB size 12.000 KiB access 30 % age 0 ns 30 addr 9.520 GiB size 4.000 KiB access 40 % age 0 ns [...] 41 addr 9.520 GiB size 4.000 KiB access 80 % age 56 s 42 addr 9.511 GiB size 12.000 KiB access 100 % age 6 m 16 s 43 addr 58.321 GiB size 4.000 KiB access 100 % age 6 m 24 s 44 addr 9.512 GiB size 4.000 KiB access 100 % age 6 m 48 s 45 addr 58.106 GiB size 4.000 KiB access 100 % age 6 m 48 s # hottest total size: 62.000 GiB # damo report access --style temperature-sz-hist <temperature> <total size> [-40,800,000,000, -32,639,999,000) 21.657 GiB |********************| [-32,639,999,000, -24,479,998,000) 17.938 GiB |***************** | [-24,479,998,000, -16,319,997,000) 16.885 GiB |**************** | [-16,319,997,000, -8,159,996,000) 586.879 MiB |* | [-8,159,996,000, 5,000) 4.946 GiB |***** | [5,000, 8,160,006,000) 260.000 KiB |* | [8,160,006,000, 16,320,007,000) 0 B | | [16,320,007,000, 24,480,008,000) 0 B | | [24,480,008,000, 32,640,009,000) 0 B | | [32,640,009,000, 40,800,010,000) 16.000 KiB |* | [40,800,010,000, 48,960,011,000) 8.000 KiB |* | total size: 62.000 GiB ``` The number of regions having different access patterns has significantly increased. Size of each region is also more varied. Total size of non-zero access frequency regions is also significantly increased. Maybe this is already good enough to make some meaningful memory management efficieny changes. 800ms/16s intervals: Another bias --------------------------------- Further doubling the intervals (800 milliseconds and 16 seconds for sampling and aggregation intervals, respectively) mor improved the hot regions detection, but starts looking degrading cold regions detection. ``` # damo start -s 800ms -a 16s # sleep 600 # damo record --snapshot 0 1 # damo stop # damo report access --sort_regions_by temperature 0 addr 64.781 GiB size 1.219 GiB access 0 % age 4 m 48 s 1 addr 24.505 GiB size 2.475 GiB access 0 % age 4 m 16 s 2 addr 26.980 GiB size 504.273 MiB access 0 % age 4 m 3 addr 29.443 GiB size 2.462 GiB access 0 % age 4 m 4 addr 37.264 GiB size 5.645 GiB access 0 % age 4 m 5 addr 31.905 GiB size 5.359 GiB access 0 % age 3 m 44 s [...] 20 addr 8.711 GiB size 40.000 KiB access 5 % age 2 m 40 s 21 addr 27.473 GiB size 1.970 GiB access 5 % age 4 m 22 addr 48.185 GiB size 4.625 GiB access 5 % age 4 m 23 addr 47.304 GiB size 902.117 MiB access 10 % age 4 m 24 addr 8.711 GiB size 4.000 KiB access 100 % age 4 m 25 addr 20.793 GiB size 3.713 GiB access 5 % age 4 m 16 s 26 addr 8.773 GiB size 4.000 KiB access 100 % age 4 m 16 s total size: 62.000 GiB # damo report access --style temperature-sz-hist <temperature> <total size> [-28,800,000,000, -23,359,999,000) 12.294 GiB |***************** | [-23,359,999,000, -17,919,998,000) 9.753 GiB |************* | [-17,919,998,000, -12,479,997,000) 15.131 GiB |********************| [-12,479,997,000, -7,039,996,000) 0 B | | [-7,039,996,000, -1,599,995,000) 7.506 GiB |********** | [-1,599,995,000, 3,840,006,000) 6.127 GiB |********* | [3,840,006,000, 9,280,007,000) 0 B | | [9,280,007,000, 14,720,008,000) 136.000 KiB |* | [14,720,008,000, 20,160,009,000) 40.000 KiB |* | [20,160,009,000, 25,600,010,000) 11.188 GiB |*************** | [25,600,010,000, 31,040,011,000) 4.000 KiB |* | total size: 62.000 GiB ``` It found more non-zero access frequency regions. The number of regions is still much higher than the `min_nr_regions`, but it is reduced from that of the previous setup. And apparently the distribution seems bit biased to hot regions. Conclusion ---------- Because the workload is live, the above results are not always consistent. But, the tendency of the quality for the interval changes was consistent. With the above experimental tuning results, I conclude the theory and the guide makes sense to at least this workload, and could be applied to similar cases. This also gives us an idea for automated tuning of the intervals. If the interval is too short, results are biased to cold regions. If the interval is too long, results are biased to hot regions. Maybe DAMON can moitor to which direction the current snapshot is biased, and adjust the intervals. I will develop the idea more. Thanks, SJ [...]
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