From: SeongJae Park <[email protected]>
DAMON[1] can be used as a primitive for data access awared memory management
optimizations. That said, users who want such optimizations should run DAMON,
read the monitoring results, analyze it, plan a new memory management scheme,
and apply the new scheme by themselves. Such efforts will be inevitable for
some complicated optimizations.
However, in many other cases, the users could simply want the system to apply a
memory management action to a memory region of a specific size having a
specific access frequency for a specific time. For example, "page out a memory
region larger than 100 MiB keeping only rare accesses more than 2 minutes", or
"Do not use THP for a memory region larger than 2 MiB rarely accessed for more
than 1 seconds".
This RFC patchset makes DAMON to handle such data access monitoring-based
operation schemes. With this change, users can do the data access awared
optimizations by simply specifying their schemes to DAMON.
Evaluations
===========
Setup
-----
On my personal QEMU/KVM based virtual machine on an Intel i7 host machine
running Ubuntu 18.04, I measure runtime and consumed system memory while
running various realistic workloads with several configurations. I use 13 and
12 workloads in PARSEC3[3] and SPLASH-2X[4] benchmark suites, respectively. I
personally use another wrapper scripts[5] for setup and run of the workloads.
On top of this patchset, we also applied the DAMON-based operation schemes
patchset[6] for this evaluation.
Measurement
~~~~~~~~~~~
For the measurement of the amount of consumed memory in system global scope, I
drop caches before starting each of the workloads and monitor 'MemFree' in the
'/proc/meminfo' file. To make results more stable, I repeat the runs 5 times
and average results. You can get stdev, min, and max of the numbers among the
repeated runs in appendix below.
Configurations
~~~~~~~~~~~~~~
The configurations I use are as below.
orig: Linux v5.5 with 'madvise' THP policy
rec: 'orig' plus DAMON running with record feature
thp: same with 'orig', but use 'always' THP policy
ethp: 'orig' plus a DAMON operation scheme[6], 'efficient THP'
prcl: 'orig' plus a DAMON operation scheme, 'proactive reclaim[7]'
I use 'rec' for measurement of DAMON overheads to target workloads and system
memory. The remaining configs including 'thp', 'ethp', and 'prcl' are for
measurement of DAMON monitoring accuracy.
'ethp' and 'prcl' is simple DAMON-based operation schemes developed for
proof of concepts of DAMON. 'ethp' reduces memory space waste of THP by using
DAMON for decision of promotions and demotion for huge pages, while 'prcl' is
as similar as the original work. Those are implemented as below:
# format: <min/max size> <min/max frequency (0-100)> <min/max age> <action>
# ethp: Use huge pages if a region >2MB shows >5% access rate, use regular
# pages if a region >2MB shows <5% access rate for >1 second
2M null 5 null null null hugepage
2M null null 5 1s null nohugepage
# prcl: If a region >4KB shows <5% access rate for >5 seconds, page out.
4K null null 5 5s null pageout
Note that both 'ethp' and 'prcl' are designed with my only straightforward
intuition, because those are for only proof of concepts and monitoring accuracy
of DAMON. In other words, those are not for production. For production use,
those should be tuned more.
[1] "Redis latency problems troubleshooting", https://redis.io/topics/latency
[2] "Disable Transparent Huge Pages (THP)",
https://docs.mongodb.com/manual/tutorial/transparent-huge-pages/
[3] "The PARSEC Becnhmark Suite", https://parsec.cs.princeton.edu/index.htm
[4] "SPLASH-2x", https://parsec.cs.princeton.edu/parsec3-doc.htm#splash2x
[5] "parsec3_on_ubuntu", https://github.com/sjp38/parsec3_on_ubuntu
[6] "[RFC v4 0/7] Implement Data Access Monitoring-based Memory Operation
Schemes",
https://lore.kernel.org/linux-mm/[email protected]/
[7] "Proactively reclaiming idle memory", https://lwn.net/Articles/787611/
Results
-------
Below two tables show the measurement results. The runtimes are in seconds
while the memory usages are in KiB. Each configurations except 'orig' shows
its overhead relative to 'orig' in percent within parenthesises.
runtime orig rec (overhead) thp (overhead) ethp (overhead) prcl (overhead)
parsec3/blackscholes 107.594 107.956 (0.34) 106.750 (-0.78) 107.672 (0.07) 111.916 (4.02)
parsec3/bodytrack 79.230 79.368 (0.17) 78.908 (-0.41) 79.705 (0.60) 80.423 (1.50)
parsec3/canneal 142.831 143.810 (0.69) 123.530 (-13.51) 133.778 (-6.34) 144.998 (1.52)
parsec3/dedup 11.986 11.959 (-0.23) 11.762 (-1.87) 12.028 (0.35) 13.313 (11.07)
parsec3/facesim 210.125 209.007 (-0.53) 205.226 (-2.33) 207.766 (-1.12) 209.815 (-0.15)
parsec3/ferret 191.601 191.177 (-0.22) 190.420 (-0.62) 191.775 (0.09) 192.638 (0.54)
parsec3/fluidanimate 212.735 212.970 (0.11) 209.151 (-1.68) 211.904 (-0.39) 218.573 (2.74)
parsec3/freqmine 291.225 290.873 (-0.12) 289.258 (-0.68) 289.884 (-0.46) 298.373 (2.45)
parsec3/raytrace 118.289 119.586 (1.10) 119.045 (0.64) 119.064 (0.66) 137.919 (16.60)
parsec3/streamcluster 323.565 328.168 (1.42) 279.565 (-13.60) 287.452 (-11.16) 333.244 (2.99)
parsec3/swaptions 155.140 155.473 (0.21) 153.816 (-0.85) 156.423 (0.83) 156.237 (0.71)
parsec3/vips 58.979 59.311 (0.56) 58.733 (-0.42) 59.005 (0.04) 61.062 (3.53)
parsec3/x264 70.539 68.413 (-3.01) 64.760 (-8.19) 67.180 (-4.76) 68.103 (-3.45)
splash2x/barnes 80.414 81.751 (1.66) 73.585 (-8.49) 80.232 (-0.23) 115.753 (43.95)
splash2x/fft 33.902 34.111 (0.62) 24.228 (-28.53) 29.926 (-11.73) 44.438 (31.08)
splash2x/lu_cb 85.556 86.001 (0.52) 84.538 (-1.19) 86.000 (0.52) 91.447 (6.89)
splash2x/lu_ncb 93.399 93.652 (0.27) 90.463 (-3.14) 94.008 (0.65) 93.901 (0.54)
splash2x/ocean_cp 45.253 45.191 (-0.14) 43.049 (-4.87) 44.022 (-2.72) 46.588 (2.95)
splash2x/ocean_ncp 86.927 87.065 (0.16) 50.747 (-41.62) 86.855 (-0.08) 199.553 (129.57)
splash2x/radiosity 91.433 91.511 (0.09) 90.626 (-0.88) 91.865 (0.47) 104.524 (14.32)
splash2x/radix 31.923 32.023 (0.31) 25.194 (-21.08) 32.035 (0.35) 39.231 (22.89)
splash2x/raytrace 84.367 84.677 (0.37) 82.417 (-2.31) 83.505 (-1.02) 84.857 (0.58)
splash2x/volrend 87.499 87.495 (-0.00) 86.775 (-0.83) 87.311 (-0.21) 87.511 (0.01)
splash2x/water_nsquared 236.397 236.759 (0.15) 219.902 (-6.98) 224.228 (-5.15) 238.562 (0.92)
splash2x/water_spatial 89.646 89.767 (0.14) 89.735 (0.10) 90.347 (0.78) 103.585 (15.55)
total 3020.570 3028.080 (0.25) 2852.190 (-5.57) 2953.960 (-2.21) 3276.550 (8.47)
memused.avg orig rec (overhead) thp (overhead) ethp (overhead) prcl (overhead)
parsec3/blackscholes 1785916.600 1834201.400 (2.70) 1826249.200 (2.26) 1828079.200 (2.36) 1712210.600 (-4.13)
parsec3/bodytrack 1415049.400 1434317.600 (1.36) 1423715.000 (0.61) 1430392.600 (1.08) 1435136.000 (1.42)
parsec3/canneal 1043489.800 1058617.600 (1.45) 1040484.600 (-0.29) 1048664.800 (0.50) 1050280.000 (0.65)
parsec3/dedup 2414453.200 2458493.200 (1.82) 2411379.400 (-0.13) 2400516.000 (-0.58) 2461120.800 (1.93)
parsec3/facesim 541597.200 550097.400 (1.57) 544364.600 (0.51) 553240.000 (2.15) 552316.400 (1.98)
parsec3/ferret 317986.600 332346.000 (4.52) 320218.000 (0.70) 331085.000 (4.12) 330895.200 (4.06)
parsec3/fluidanimate 576183.400 585442.000 (1.61) 577780.200 (0.28) 587703.400 (2.00) 506501.000 (-12.09)
parsec3/freqmine 990869.200 997817.000 (0.70) 990350.400 (-0.05) 997669.000 (0.69) 763325.800 (-22.96)
parsec3/raytrace 1748370.800 1757109.200 (0.50) 1746153.800 (-0.13) 1757830.400 (0.54) 1581455.800 (-9.55)
parsec3/streamcluster 121521.800 140452.400 (15.58) 129725.400 (6.75) 132266.000 (8.84) 130558.200 (7.44)
parsec3/swaptions 15592.400 29018.800 (86.11) 14765.800 (-5.30) 27260.200 (74.83) 26631.600 (70.80)
parsec3/vips 2957567.600 2967993.800 (0.35) 2956623.200 (-0.03) 2973062.600 (0.52) 2951402.000 (-0.21)
parsec3/x264 3169012.400 3175048.800 (0.19) 3190345.400 (0.67) 3189353.000 (0.64) 3172924.200 (0.12)
splash2x/barnes 1209066.000 1213125.400 (0.34) 1217261.400 (0.68) 1209661.600 (0.05) 921041.800 (-23.82)
splash2x/fft 9359313.200 9195213.000 (-1.75) 9377562.400 (0.19) 9050957.600 (-3.29) 9517977.000 (1.70)
splash2x/lu_cb 514966.200 522939.400 (1.55) 520870.400 (1.15) 522635.000 (1.49) 329933.600 (-35.93)
splash2x/lu_ncb 514180.400 525974.800 (2.29) 521420.200 (1.41) 521063.600 (1.34) 523557.000 (1.82)
splash2x/ocean_cp 3346493.400 3288078.000 (-1.75) 3382253.800 (1.07) 3289477.600 (-1.70) 3260810.400 (-2.56)
splash2x/ocean_ncp 3909966.400 3882968.800 (-0.69) 7037196.000 (79.98) 4046363.400 (3.49) 3471452.400 (-11.22)
splash2x/radiosity 1471119.400 1470626.800 (-0.03) 1482604.200 (0.78) 1472718.400 (0.11) 546893.600 (-62.82)
splash2x/radix 1748360.800 1729163.400 (-1.10) 1371463.200 (-21.56) 1701993.600 (-2.65) 1817519.600 (3.96)
splash2x/raytrace 46670.000 60172.200 (28.93) 51901.600 (11.21) 60782.600 (30.24) 52644.800 (12.80)
splash2x/volrend 150666.600 167444.200 (11.14) 151335.200 (0.44) 163345.000 (8.41) 162760.000 (8.03)
splash2x/water_nsquared 45720.200 59422.400 (29.97) 46031.000 (0.68) 61801.400 (35.17) 62627.000 (36.98)
splash2x/water_spatial 663052.200 672855.800 (1.48) 665787.600 (0.41) 674696.200 (1.76) 471052.600 (-28.96)
total 40077300.000 40108900.000 (0.08) 42997900.000 (7.29) 40032700.000 (-0.11) 37813000.000 (-5.65)
DAMON Overheads
~~~~~~~~~~~~~~~
In total, DAMON recording feature incurs 0.25% runtime overhead (up to 1.66% in
worst case with 'splash2x/barnes') and 0.08% memory space overhead.
For convenience test run of 'rec', I use a Python wrapper. The wrapper
constantly consumes about 10-15MB of memory. This becomes high memory overhead
if the target workload has small memory footprint. In detail, 16%, 86%, 29%,
11%, and 30% overheads shown for parsec3/streamcluster (125 MiB),
parsec3/swaptions (15 MiB), splash2x/raytrace (45 MiB), splash2x/volrend (151
MiB), and splash2x/water_nsquared (46 MiB)). Nonetheless, the overheads are
not from DAMON, but from the wrapper, and thus should be ignored. This fake
memory overhead continues in 'ethp' and 'prcl', as those configurations are
also using the Python wrapper.
Efficient THP
~~~~~~~~~~~~~
THP 'always' enabled policy achieves 5.57% speedup but incurs 7.29% memory
overhead. It achieves 41.62% speedup in best case, but 79.98% memory overhead
in worst case. Interestingly, both the best and worst case are with
'splash2x/ocean_ncp').
The 2-lines implementation of data access monitoring based THP version ('ethp')
shows 2.21% speedup and -0.11% memory overhead. In other words, 'ethp' removes
100% of THP memory waste while preserving 39.67% of THP speedup in total.
Proactive Reclamation
~~~~~~~~~~~~~~~~~~~~
As same to the original work, I use 'zram' swap device for this configuration.
In total, our 1 line implementation of Proactive Reclamation, 'prcl', incurred
8.47% runtime overhead in total while achieving 5.65% system memory usage
reduction.
Nonetheless, as the memory usage is calculated with 'MemFree' in
'/proc/meminfo', it contains the SwapCached pages. As the swapcached pages can
be easily evicted, I also measured the residential set size of the workloads:
rss.avg orig rec (overhead) thp (overhead) ethp (overhead) prcl (overhead)
parsec3/blackscholes 592502.000 589764.400 (-0.46) 592132.600 (-0.06) 593702.000 (0.20) 406639.400 (-31.37)
parsec3/bodytrack 32365.400 32195.000 (-0.53) 32210.800 (-0.48) 32114.600 (-0.77) 21537.600 (-33.45)
parsec3/canneal 839904.200 840292.200 (0.05) 836866.400 (-0.36) 838263.200 (-0.20) 837895.800 (-0.24)
parsec3/dedup 1208337.200 1218465.600 (0.84) 1233278.600 (2.06) 1200490.200 (-0.65) 882911.400 (-26.93)
parsec3/facesim 311380.800 311363.600 (-0.01) 315642.600 (1.37) 312573.400 (0.38) 310257.400 (-0.36)
parsec3/ferret 99514.800 99542.000 (0.03) 100454.200 (0.94) 99879.800 (0.37) 89679.200 (-9.88)
parsec3/fluidanimate 531760.800 531735.200 (-0.00) 531865.400 (0.02) 531940.800 (0.03) 440781.000 (-17.11)
parsec3/freqmine 552455.400 552882.600 (0.08) 555793.600 (0.60) 553019.800 (0.10) 58067.000 (-89.49)
parsec3/raytrace 894798.400 894953.400 (0.02) 892223.400 (-0.29) 893012.400 (-0.20) 315259.800 (-64.77)
parsec3/streamcluster 110780.400 110856.800 (0.07) 110954.000 (0.16) 111310.800 (0.48) 108066.800 (-2.45)
parsec3/swaptions 5614.600 5645.600 (0.55) 5553.200 (-1.09) 5552.600 (-1.10) 3251.800 (-42.08)
parsec3/vips 31942.200 31752.800 (-0.59) 32042.600 (0.31) 32226.600 (0.89) 29012.200 (-9.17)
parsec3/x264 81770.800 81609.200 (-0.20) 82800.800 (1.26) 82612.200 (1.03) 81805.800 (0.04)
splash2x/barnes 1216515.600 1217113.800 (0.05) 1225605.600 (0.75) 1217325.000 (0.07) 540108.400 (-55.60)
splash2x/fft 9668660.600 9751350.800 (0.86) 9773806.400 (1.09) 9613555.400 (-0.57) 7951241.800 (-17.76)
splash2x/lu_cb 510368.800 510095.800 (-0.05) 514350.600 (0.78) 510276.000 (-0.02) 311584.800 (-38.95)
splash2x/lu_ncb 509904.800 510001.600 (0.02) 513847.000 (0.77) 510073.400 (0.03) 509905.600 (0.00)
splash2x/ocean_cp 3389550.600 3404466.000 (0.44) 3443363.600 (1.59) 3410388.000 (0.61) 3330608.600 (-1.74)
splash2x/ocean_ncp 3923723.200 3911148.200 (-0.32) 7175800.400 (82.88) 4104482.400 (4.61) 2030525.000 (-48.25)
splash2x/radiosity 1472994.600 1475946.400 (0.20) 1485636.800 (0.86) 1476193.000 (0.22) 262161.400 (-82.20)
splash2x/radix 1750329.800 1765697.000 (0.88) 1413304.000 (-19.25) 1754154.400 (0.22) 1516142.600 (-13.38)
splash2x/raytrace 23149.600 23208.000 (0.25) 28574.400 (23.43) 26694.600 (15.31) 16257.800 (-29.77)
splash2x/volrend 43968.800 43919.000 (-0.11) 44087.600 (0.27) 44224.000 (0.58) 32484.400 (-26.12)
splash2x/water_nsquared 29348.000 29338.400 (-0.03) 29604.600 (0.87) 29779.400 (1.47) 23644.800 (-19.43)
splash2x/water_spatial 655263.600 655097.800 (-0.03) 655199.200 (-0.01) 656282.400 (0.16) 379816.800 (-42.04)
total 28486900.000 28598400.000 (0.39) 31625000.000 (11.02) 28640100.000 (0.54) 20489600.000 (-28.07)
In total, 28.07% of residential sets were reduced.
With parsec3/freqmine, 'prcl' reduced 22.96% of system memory usage and 89.49%
of residential sets while incurring only 2.45% runtime overhead.
Sequence Of Patches
===================
The patches are based on the v5.6 plus v7 DAMON patchset[1] and Minchan's
``do_madvise()`` patch[2]. Minchan's patch was necessary for reuse of
``madvise()`` code in DAMON. You can also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damos/rfc/v5
The web is also available:
https://github.com/sjp38/linux/releases/tag/damos/rfc/v5
[1] https://lore.kernel.org/linux-mm/[email protected]/
[2] https://lore.kernel.org/linux-mm/[email protected]/
The first patch allows DAMON to reuse ``madvise()`` code for the actions. The
second patch accounts age of each region. The third patch implements the
handling of the schemes in DAMON and exports a kernel space programming
interface for it. The fourth patch implements a debugfs interface for
privileged people and programs. The fifth and sixth patches each adds
kunittests and selftests for these changes, and finally the seventhe patch
modifies the user space tool for DAMON to support description and applying of
schemes in human freiendly way.
Patch History
=============
Changes from RFC v4
(https://lore.kernel.org/linux-mm/[email protected]/)
- Handle CONFIG_ADVISE_SYSCALL
- Clean up code (Jonathan Cameron)
- Update test results
- Rebase on v5.6 + DAMON v7
Changes from RFC v3
(https://lore.kernel.org/linux-mm/[email protected]/)
- Add Reviewed-by from Brendan Higgins
- Code cleanup: Modularize madvise() call
- Fix a trivial bug in the wrapper python script
- Add more stable and detailed evaluation results with updated ETHP scheme
Changes from RFC v2
(https://lore.kernel.org/linux-mm/[email protected]/)
- Fix aging mechanism for more better 'old region' selection
- Add more kunittests and kselftests for this patchset
- Support more human friedly description and application of 'schemes'
Changes from RFC v1
(https://lore.kernel.org/linux-mm/[email protected]/)
- Properly adjust age accounting related properties after splitting, merging,
and action applying
SeongJae Park (7):
mm/madvise: Export do_madvise() to external GPL modules
mm/damon: Account age of target regions
mm/damon: Implement data access monitoring-based operation schemes
mm/damon/schemes: Implement a debugfs interface
mm/damon-test: Add kunit test case for regions age accounting
mm/damon/selftests: Add 'schemes' debugfs tests
damon/tools: Support more human friendly 'schemes' control
include/linux/damon.h | 29 ++
mm/damon-test.h | 5 +
mm/damon.c | 428 +++++++++++++++++-
mm/madvise.c | 1 +
tools/damon/_convert_damos.py | 125 +++++
tools/damon/_damon.py | 143 ++++++
tools/damon/damo | 7 +
tools/damon/record.py | 135 +-----
tools/damon/schemes.py | 105 +++++
.../testing/selftests/damon/debugfs_attrs.sh | 29 ++
10 files changed, 878 insertions(+), 129 deletions(-)
create mode 100755 tools/damon/_convert_damos.py
create mode 100644 tools/damon/_damon.py
create mode 100644 tools/damon/schemes.py
--
2.17.1
==================================== >8 =======================================
Appendix: Stdev / min / max numbers among the repeated runs
===========================================================
Below are stdev/min/max of each number in the 5 repeated runs.
runtime_avg orig rec thp ethp prcl
parsec3/blackscholes 107.594 107.956 106.750 107.672 111.916
parsec3/bodytrack 79.230 79.368 78.908 79.705 80.423
parsec3/canneal 142.831 143.810 123.530 133.778 144.998
parsec3/dedup 11.986 11.959 11.762 12.028 13.313
parsec3/facesim 210.125 209.007 205.226 207.766 209.815
parsec3/ferret 191.601 191.177 190.420 191.775 192.638
parsec3/fluidanimate 212.735 212.970 209.151 211.904 218.573
parsec3/freqmine 291.225 290.873 289.258 289.884 298.373
parsec3/raytrace 118.289 119.586 119.045 119.064 137.919
parsec3/streamcluster 323.565 328.168 279.565 287.452 333.244
parsec3/swaptions 155.140 155.473 153.816 156.423 156.237
parsec3/vips 58.979 59.311 58.733 59.005 61.062
parsec3/x264 70.539 68.413 64.760 67.180 68.103
splash2x/barnes 80.414 81.751 73.585 80.232 115.753
splash2x/fft 33.902 34.111 24.228 29.926 44.438
splash2x/lu_cb 85.556 86.001 84.538 86.000 91.447
splash2x/lu_ncb 93.399 93.652 90.463 94.008 93.901
splash2x/ocean_cp 45.253 45.191 43.049 44.022 46.588
splash2x/ocean_ncp 86.927 87.065 50.747 86.855 199.553
splash2x/radiosity 91.433 91.511 90.626 91.865 104.524
splash2x/radix 31.923 32.023 25.194 32.035 39.231
splash2x/raytrace 84.367 84.677 82.417 83.505 84.857
splash2x/volrend 87.499 87.495 86.775 87.311 87.511
splash2x/water_nsquared 236.397 236.759 219.902 224.228 238.562
splash2x/water_spatial 89.646 89.767 89.735 90.347 103.585
memused.avg_avg orig rec thp ethp prcl
parsec3/blackscholes 1785916.600 1834201.400 1826249.200 1828079.200 1712210.600
parsec3/bodytrack 1415049.400 1434317.600 1423715.000 1430392.600 1435136.000
parsec3/canneal 1043489.800 1058617.600 1040484.600 1048664.800 1050280.000
parsec3/dedup 2414453.200 2458493.200 2411379.400 2400516.000 2461120.800
parsec3/facesim 541597.200 550097.400 544364.600 553240.000 552316.400
parsec3/ferret 317986.600 332346.000 320218.000 331085.000 330895.200
parsec3/fluidanimate 576183.400 585442.000 577780.200 587703.400 506501.000
parsec3/freqmine 990869.200 997817.000 990350.400 997669.000 763325.800
parsec3/raytrace 1748370.800 1757109.200 1746153.800 1757830.400 1581455.800
parsec3/streamcluster 121521.800 140452.400 129725.400 132266.000 130558.200
parsec3/swaptions 15592.400 29018.800 14765.800 27260.200 26631.600
parsec3/vips 2957567.600 2967993.800 2956623.200 2973062.600 2951402.000
parsec3/x264 3169012.400 3175048.800 3190345.400 3189353.000 3172924.200
splash2x/barnes 1209066.000 1213125.400 1217261.400 1209661.600 921041.800
splash2x/fft 9359313.200 9195213.000 9377562.400 9050957.600 9517977.000
splash2x/lu_cb 514966.200 522939.400 520870.400 522635.000 329933.600
splash2x/lu_ncb 514180.400 525974.800 521420.200 521063.600 523557.000
splash2x/ocean_cp 3346493.400 3288078.000 3382253.800 3289477.600 3260810.400
splash2x/ocean_ncp 3909966.400 3882968.800 7037196.000 4046363.400 3471452.400
splash2x/radiosity 1471119.400 1470626.800 1482604.200 1472718.400 546893.600
splash2x/radix 1748360.800 1729163.400 1371463.200 1701993.600 1817519.600
splash2x/raytrace 46670.000 60172.200 51901.600 60782.600 52644.800
splash2x/volrend 150666.600 167444.200 151335.200 163345.000 162760.000
splash2x/water_nsquared 45720.200 59422.400 46031.000 61801.400 62627.000
splash2x/water_spatial 663052.200 672855.800 665787.600 674696.200 471052.600
rss.avg_avg orig rec thp ethp prcl
parsec3/blackscholes 592502.000 589764.400 592132.600 593702.000 406639.400
parsec3/bodytrack 32365.400 32195.000 32210.800 32114.600 21537.600
parsec3/canneal 839904.200 840292.200 836866.400 838263.200 837895.800
parsec3/dedup 1208337.200 1218465.600 1233278.600 1200490.200 882911.400
parsec3/facesim 311380.800 311363.600 315642.600 312573.400 310257.400
parsec3/ferret 99514.800 99542.000 100454.200 99879.800 89679.200
parsec3/fluidanimate 531760.800 531735.200 531865.400 531940.800 440781.000
parsec3/freqmine 552455.400 552882.600 555793.600 553019.800 58067.000
parsec3/raytrace 894798.400 894953.400 892223.400 893012.400 315259.800
parsec3/streamcluster 110780.400 110856.800 110954.000 111310.800 108066.800
parsec3/swaptions 5614.600 5645.600 5553.200 5552.600 3251.800
parsec3/vips 31942.200 31752.800 32042.600 32226.600 29012.200
parsec3/x264 81770.800 81609.200 82800.800 82612.200 81805.800
splash2x/barnes 1216515.600 1217113.800 1225605.600 1217325.000 540108.400
splash2x/fft 9668660.600 9751350.800 9773806.400 9613555.400 7951241.800
splash2x/lu_cb 510368.800 510095.800 514350.600 510276.000 311584.800
splash2x/lu_ncb 509904.800 510001.600 513847.000 510073.400 509905.600
splash2x/ocean_cp 3389550.600 3404466.000 3443363.600 3410388.000 3330608.600
splash2x/ocean_ncp 3923723.200 3911148.200 7175800.400 4104482.400 2030525.000
splash2x/radiosity 1472994.600 1475946.400 1485636.800 1476193.000 262161.400
splash2x/radix 1750329.800 1765697.000 1413304.000 1754154.400 1516142.600
splash2x/raytrace 23149.600 23208.000 28574.400 26694.600 16257.800
splash2x/volrend 43968.800 43919.000 44087.600 44224.000 32484.400
splash2x/water_nsquared 29348.000 29338.400 29604.600 29779.400 23644.800
splash2x/water_spatial 655263.600 655097.800 655199.200 656282.400 379816.800
runtime_stdev orig rec thp ethp prcl
parsec3/blackscholes 0.954 1.173 1.344 0.728 3.731
parsec3/bodytrack 0.723 0.463 0.465 0.686 0.266
parsec3/canneal 2.915 1.248 3.627 5.427 1.000
parsec3/dedup 0.047 0.052 0.037 0.062 0.180
parsec3/facesim 2.724 0.890 1.848 2.472 1.137
parsec3/ferret 1.818 0.552 1.288 1.397 0.826
parsec3/fluidanimate 2.157 1.082 1.695 1.456 4.954
parsec3/freqmine 5.016 2.417 2.256 2.066 2.007
parsec3/raytrace 0.246 0.601 0.825 0.522 1.462
parsec3/streamcluster 1.529 1.678 1.069 1.549 1.074
parsec3/swaptions 1.488 0.840 0.509 1.488 1.567
parsec3/vips 0.280 0.496 0.202 0.330 1.153
parsec3/x264 8.605 5.975 4.042 4.928 4.645
splash2x/barnes 0.802 0.741 0.317 0.745 6.725
splash2x/fft 0.440 0.458 0.126 3.501 5.331
splash2x/lu_cb 0.714 0.593 0.159 0.458 1.386
splash2x/lu_ncb 0.715 0.798 0.600 0.854 0.791
splash2x/ocean_cp 0.331 0.288 0.182 0.164 2.340
splash2x/ocean_ncp 0.540 0.893 0.276 3.448 2.089
splash2x/radiosity 0.715 0.665 0.511 0.625 0.986
splash2x/radix 0.240 0.285 0.202 0.135 6.516
splash2x/raytrace 0.343 0.466 0.360 0.757 0.333
splash2x/volrend 0.998 0.403 0.975 1.025 0.314
splash2x/water_nsquared 2.549 1.586 4.201 2.752 0.851
splash2x/water_spatial 0.823 0.116 0.832 0.481 1.289
memused.avg_stdev orig rec thp ethp prcl
parsec3/blackscholes 79952.135 4432.811 1789.970 5626.223 84879.091
parsec3/bodytrack 3175.309 4849.375 1831.190 6191.900 3540.004
parsec3/canneal 4619.856 2412.794 3416.723 3025.273 3987.361
parsec3/dedup 68506.421 21577.238 45877.701 66721.358 9016.778
parsec3/facesim 1307.756 2381.476 2262.856 1393.396 1937.146
parsec3/ferret 3084.143 2264.331 2634.383 2864.003 2768.815
parsec3/fluidanimate 4193.918 3755.019 709.357 1354.922 36248.397
parsec3/freqmine 7624.951 2670.721 1056.584 1956.573 3489.113
parsec3/raytrace 3040.764 3314.882 3858.548 1242.293 14261.074
parsec3/streamcluster 1785.329 3278.962 12647.075 1323.469 2359.912
parsec3/swaptions 1427.746 1651.129 2211.373 2154.608 1513.124
parsec3/vips 5348.300 28619.077 3698.960 11801.584 31044.042
parsec3/x264 70352.471 44934.346 35477.481 38664.730 34684.496
splash2x/barnes 6327.141 5998.215 7956.925 3549.169 27271.518
splash2x/fft 112761.299 40965.232 23288.890 133070.766 299831.272
splash2x/lu_cb 1959.648 1071.290 1661.184 2213.133 7278.891
splash2x/lu_ncb 2737.766 1908.123 2225.306 1866.099 2965.527
splash2x/ocean_cp 5714.993 3305.800 4475.152 9238.080 46523.579
splash2x/ocean_ncp 5369.359 12261.740 47468.551 331852.556 14039.346
splash2x/radiosity 6635.861 1847.946 2504.261 4999.619 85263.423
splash2x/radix 26109.082 30050.707 23734.126 29847.749 97323.261
splash2x/raytrace 1244.431 1634.057 431.736 2477.034 1469.019
splash2x/volrend 1695.845 1276.892 2141.189 1853.123 2398.085
splash2x/water_nsquared 4092.775 4390.012 3535.825 685.058 16884.309
splash2x/water_spatial 3320.770 1779.806 2895.820 3684.654 31197.246
rss.avg_stdev orig rec thp ethp prcl
parsec3/blackscholes 2099.400 1825.929 1783.531 141.050 129348.367
parsec3/bodytrack 84.540 101.865 91.202 152.135 230.240
parsec3/canneal 1236.533 245.109 1079.198 1395.976 634.364
parsec3/dedup 24015.016 33732.398 15235.190 36140.568 134488.735
parsec3/facesim 443.203 284.799 1199.896 681.779 2127.226
parsec3/ferret 197.466 176.918 1035.756 233.280 2282.755
parsec3/fluidanimate 79.776 48.093 83.121 296.200 43330.391
parsec3/freqmine 942.668 701.068 1078.358 1413.236 7531.130
parsec3/raytrace 944.799 1250.995 769.676 1469.261 18296.783
parsec3/streamcluster 102.866 53.790 470.192 548.025 57.252
parsec3/swaptions 75.298 98.636 79.550 62.021 413.196
parsec3/vips 106.916 220.912 479.852 263.794 732.441
parsec3/x264 688.836 542.557 691.062 586.514 252.573
splash2x/barnes 1196.302 4882.971 2004.136 1382.603 73413.752
splash2x/fft 130840.897 11731.094 34936.745 139407.193 731486.599
splash2x/lu_cb 328.545 827.521 30.618 689.734 8641.051
splash2x/lu_ncb 466.633 375.314 8.695 413.171 789.276
splash2x/ocean_cp 32004.956 4180.580 4846.877 11128.713 148750.827
splash2x/ocean_ncp 15405.607 20291.836 3580.843 343615.906 57957.024
splash2x/radiosity 7370.873 1947.166 403.421 1640.537 109300.546
splash2x/radix 36493.663 26592.717 48293.202 28593.957 210257.478
splash2x/raytrace 52.861 101.382 440.840 799.744 331.045
splash2x/volrend 88.635 287.634 58.387 219.078 28.542
splash2x/water_nsquared 70.063 29.350 455.723 549.829 507.052
splash2x/water_spatial 965.860 584.120 862.899 625.247 36590.592
runtime_min orig rec thp ethp prcl
parsec3/blackscholes 106.493 107.131 105.819 106.780 107.954
parsec3/bodytrack 78.504 78.835 78.445 79.124 80.031
parsec3/canneal 137.573 142.360 118.803 129.343 143.642
parsec3/dedup 11.930 11.880 11.720 11.972 13.075
parsec3/facesim 207.181 208.001 203.330 204.972 208.496
parsec3/ferret 189.513 190.432 188.831 190.556 191.685
parsec3/fluidanimate 210.622 211.693 207.265 210.418 212.564
parsec3/freqmine 287.914 288.292 286.726 287.937 295.679
parsec3/raytrace 118.068 118.799 118.176 118.610 135.509
parsec3/streamcluster 321.708 325.773 278.506 286.412 331.865
parsec3/swaptions 153.486 154.619 153.313 154.787 154.585
parsec3/vips 58.593 58.580 58.559 58.581 59.696
parsec3/x264 53.934 63.179 61.104 61.830 61.840
splash2x/barnes 79.666 80.331 73.083 79.423 106.500
splash2x/fft 33.458 33.545 24.069 25.341 39.888
splash2x/lu_cb 84.779 85.469 84.314 85.569 89.915
splash2x/lu_ncb 92.453 92.986 89.793 93.039 92.983
splash2x/ocean_cp 44.894 44.891 42.812 43.827 45.304
splash2x/ocean_ncp 85.951 85.799 50.511 80.033 197.438
splash2x/radiosity 90.698 90.951 90.108 91.064 102.641
splash2x/radix 31.600 31.773 24.938 31.876 31.988
splash2x/raytrace 83.977 84.248 81.770 82.501 84.260
splash2x/volrend 85.998 87.010 85.410 86.104 87.028
splash2x/water_nsquared 234.910 235.154 214.699 220.915 237.450
splash2x/water_spatial 88.707 89.583 88.665 89.728 101.680
memused.avg_min orig rec thp ethp prcl
parsec3/blackscholes 1626034.000 1826394.000 1823462.000 1819386.000 1609356.000
parsec3/bodytrack 1410568.000 1426374.000 1420538.000 1418411.000 1428290.000
parsec3/canneal 1037619.000 1055270.000 1036476.000 1043511.000 1043598.000
parsec3/dedup 2278984.000 2425195.000 2321960.000 2305712.000 2450959.000
parsec3/facesim 539439.000 546496.000 541226.000 551830.000 549381.000
parsec3/ferret 313549.000 329464.000 315377.000 325668.000 327287.000
parsec3/fluidanimate 567802.000 580600.000 576843.000 585364.000 472514.000
parsec3/freqmine 985728.000 994180.000 988480.000 995520.000 758166.000
parsec3/raytrace 1743676.000 1751094.000 1739729.000 1756620.000 1565002.000
parsec3/streamcluster 118300.000 134336.000 118264.000 130212.000 128156.000
parsec3/swaptions 13349.000 26645.000 11214.000 23299.000 24083.000
parsec3/vips 2949617.000 2913746.000 2951520.000 2962613.000 2909249.000
parsec3/x264 3028977.000 3117420.000 3155154.000 3115667.000 3116631.000
splash2x/barnes 1198278.000 1207195.000 1202163.000 1204844.000 894250.000
splash2x/fft 9186591.000 9121129.000 9353880.000 8836885.000 9169777.000
splash2x/lu_cb 512473.000 521889.000 517808.000 519858.000 321310.000
splash2x/lu_ncb 511032.000 523169.000 517393.000 519181.000 520978.000
splash2x/ocean_cp 3336477.000 3284399.000 3378526.000 3279741.000 3168018.000
splash2x/ocean_ncp 3904752.000 3867722.000 6942630.000 3877713.000 3456496.000
splash2x/radiosity 1458102.000 1467236.000 1478526.000 1469471.000 478366.000
splash2x/radix 1707691.000 1709799.000 1325458.000 1653979.000 1712997.000
splash2x/raytrace 44544.000 57325.000 51175.000 56467.000 49993.000
splash2x/volrend 147380.000 165395.000 148418.000 160706.000 158839.000
splash2x/water_nsquared 40565.000 50695.000 40621.000 60591.000 52073.000
splash2x/water_spatial 658452.000 669534.000 661689.000 672001.000 429351.000
rss.avg_min orig rec thp ethp prcl
parsec3/blackscholes 588688.000 588768.000 590760.000 593537.000 253301.000
parsec3/bodytrack 32271.000 32030.000 32077.000 31813.000 21384.000
parsec3/canneal 838622.000 839941.000 835341.000 836376.000 836723.000
parsec3/dedup 1171348.000 1151005.000 1202905.000 1141935.000 769036.000
parsec3/facesim 310763.000 310815.000 314095.000 311852.000 306036.000
parsec3/ferret 99122.000 99205.000 99157.000 99626.000 86395.000
parsec3/fluidanimate 531668.000 531676.000 531799.000 531732.000 403331.000
parsec3/freqmine 551454.000 551564.000 554663.000 551718.000 46064.000
parsec3/raytrace 893632.000 893410.000 891552.000 890752.000 292042.000
parsec3/streamcluster 110692.000 110788.000 110265.000 110879.000 107960.000
parsec3/swaptions 5553.000 5490.000 5483.000 5490.000 2448.000
parsec3/vips 31774.000 31392.000 31642.000 31741.000 28075.000
parsec3/x264 80396.000 80622.000 81852.000 81985.000 81338.000
splash2x/barnes 1214960.000 1211801.000 1223343.000 1215511.000 468692.000
splash2x/fft 9409274.000 9734749.000 9744377.000 9396273.000 6808707.000
splash2x/lu_cb 509818.000 508734.000 514300.000 508933.000 299910.000
splash2x/lu_ncb 509198.000 509626.000 513836.000 509460.000 508424.000
splash2x/ocean_cp 3326117.000 3398321.000 3435367.000 3397484.000 3033199.000
splash2x/ocean_ncp 3893515.000 3883868.000 7170363.000 3930558.000 1947105.000
splash2x/radiosity 1458297.000 1472266.000 1484928.000 1473122.000 174362.000
splash2x/radix 1678377.000 1742057.000 1317705.000 1701828.000 1292568.000
splash2x/raytrace 23072.000 23100.000 28164.000 25113.000 15614.000
splash2x/volrend 43853.000 43376.000 44038.000 43864.000 32451.000
splash2x/water_nsquared 29280.000 29292.000 29212.000 29292.000 23105.000
splash2x/water_spatial 654275.000 654178.000 653748.000 655154.000 329977.000
runtime_max orig rec thp ethp prcl
parsec3/blackscholes 108.836 110.267 109.402 108.690 116.322
parsec3/bodytrack 80.281 80.127 79.771 80.604 80.763
parsec3/canneal 146.168 145.781 127.499 144.079 146.620
parsec3/dedup 12.060 12.023 11.821 12.137 13.534
parsec3/facesim 214.575 210.365 208.368 211.301 211.897
parsec3/ferret 194.344 192.015 192.115 194.198 193.653
parsec3/fluidanimate 216.783 214.909 211.344 214.586 226.676
parsec3/freqmine 301.163 295.427 293.462 293.899 301.802
parsec3/raytrace 118.739 120.519 120.106 120.069 139.163
parsec3/streamcluster 326.266 330.810 281.617 290.491 335.005
parsec3/swaptions 157.631 156.817 154.746 158.719 158.727
parsec3/vips 59.446 59.966 59.119 59.528 62.585
parsec3/x264 78.949 77.977 72.571 73.702 73.746
splash2x/barnes 81.961 82.439 73.990 81.263 122.656
splash2x/fft 34.651 34.855 24.361 33.808 54.323
splash2x/lu_cb 86.882 87.071 84.763 86.854 93.114
splash2x/lu_ncb 94.552 95.203 91.275 95.261 95.371
splash2x/ocean_cp 45.866 45.708 43.286 44.321 51.266
splash2x/ocean_ncp 87.486 88.455 51.209 89.471 202.513
splash2x/radiosity 92.630 92.815 91.356 92.697 105.534
splash2x/radix 32.343 32.558 25.553 32.258 46.881
splash2x/raytrace 84.810 85.564 82.870 84.682 85.177
splash2x/volrend 89.089 88.052 88.042 89.216 88.009
splash2x/water_nsquared 241.486 239.589 225.392 228.507 240.056
splash2x/water_spatial 90.689 89.915 90.754 90.884 105.631
memused.avg_max orig rec thp ethp prcl
parsec3/blackscholes 1827611.000 1839623.000 1827981.000 1834325.000 1832457.000
parsec3/bodytrack 1419378.000 1441113.000 1425765.000 1436107.000 1437923.000
parsec3/canneal 1048886.000 1061549.000 1045699.000 1051540.000 1053781.000
parsec3/dedup 2461273.000 2481847.000 2450571.000 2475857.000 2475652.000
parsec3/facesim 543151.000 553591.000 546997.000 555622.000 554682.000
parsec3/ferret 321345.000 334826.000 322860.000 333540.000 333881.000
parsec3/fluidanimate 578475.000 588724.000 578768.000 589231.000 569241.000
parsec3/freqmine 1006016.000 1002293.000 991407.000 1000760.000 767840.000
parsec3/raytrace 1753028.000 1760656.000 1749529.000 1759651.000 1602907.000
parsec3/streamcluster 123413.000 143752.000 146342.000 133802.000 133524.000
parsec3/swaptions 17087.000 30836.000 17974.000 29534.000 27933.000
parsec3/vips 2965565.000 2998231.000 2962478.000 2994743.000 2978585.000
parsec3/x264 3217103.000 3239797.000 3254244.000 3220854.000 3213514.000
splash2x/barnes 1215088.000 1221159.000 1225814.000 1213774.000 963246.000
splash2x/fft 9496743.000 9235225.000 9419146.000 9227770.000 10048865.000
splash2x/lu_cb 517110.000 524777.000 522489.000 525367.000 338903.000
splash2x/lu_ncb 517671.000 528083.000 523740.000 523987.000 529324.000
splash2x/ocean_cp 3352652.000 3293007.000 3390730.000 3305424.000 3288700.000
splash2x/ocean_ncp 3916516.000 3905207.000 7066994.000 4710056.000 3494469.000
splash2x/radiosity 1476725.000 1471964.000 1485721.000 1482665.000 711743.000
splash2x/radix 1779129.000 1789044.000 1393082.000 1730226.000 1950825.000
splash2x/raytrace 48293.000 62020.000 52400.000 63424.000 54173.000
splash2x/volrend 151966.000 169113.000 154117.000 165760.000 165202.000
splash2x/water_nsquared 49559.000 62399.000 49236.000 62566.000 96087.000
splash2x/water_spatial 667558.000 674501.000 669156.000 681962.000 519628.000
rss.avg_max orig rec thp ethp prcl
parsec3/blackscholes 594058.000 593414.000 595593.000 593873.000 582550.000
parsec3/bodytrack 32476.000 32296.000 32333.000 32226.000 21985.000
parsec3/canneal 841973.000 840566.000 838038.000 840251.000 838550.000
parsec3/dedup 1234781.000 1235660.000 1242284.000 1238262.000 1132789.000
parsec3/facesim 311962.000 311641.000 317545.000 313614.000 311617.000
parsec3/ferret 99652.000 99679.000 101709.000 100216.000 93520.000
parsec3/fluidanimate 531892.000 531804.000 532019.000 532497.000 521361.000
parsec3/freqmine 553834.000 553611.000 557455.000 555494.000 67824.000
parsec3/raytrace 896365.000 897219.000 893722.000 894511.000 338623.000
parsec3/streamcluster 110974.000 110946.000 111724.000 112376.000 108123.000
parsec3/swaptions 5760.000 5798.000 5703.000 5665.000 3534.000
parsec3/vips 32109.000 32002.000 32980.000 32501.000 30093.000
parsec3/x264 82184.000 82123.000 83702.000 83695.000 82039.000
splash2x/barnes 1218190.000 1223450.000 1228802.000 1219696.000 637849.000
splash2x/fft 9764225.000 9766120.000 9840720.000 9791957.000 8733143.000
splash2x/lu_cb 510807.000 511101.000 514377.000 510828.000 324164.000
splash2x/lu_ncb 510422.000 510503.000 513859.000 510581.000 510579.000
splash2x/ocean_cp 3409746.000 3410829.000 3448338.000 3427647.000 3410785.000
splash2x/ocean_ncp 3937063.000 3934331.000 7180056.000 4791695.000 2089488.000
splash2x/radiosity 1477543.000 1477505.000 1485980.000 1477653.000 472920.000
splash2x/radix 1777745.000 1814084.000 1447943.000 1789252.000 1837798.000
splash2x/raytrace 23236.000 23380.000 29380.000 27273.000 16503.000
splash2x/volrend 44124.000 44217.000 44194.000 44472.000 32528.000
splash2x/water_nsquared 29476.000 29372.000 30499.000 30511.000 24440.000
splash2x/water_spatial 657080.000 655855.000 656402.000 657002.000 427395.000
From: SeongJae Park <[email protected]>
Signed-off-by: SeongJae Park <[email protected]>
---
mm/madvise.c | 1 +
1 file changed, 1 insertion(+)
diff --git a/mm/madvise.c b/mm/madvise.c
index 80f8a1839f70..151aaf285cdd 100644
--- a/mm/madvise.c
+++ b/mm/madvise.c
@@ -1151,6 +1151,7 @@ int do_madvise(struct task_struct *target_task, struct mm_struct *mm,
return error;
}
+EXPORT_SYMBOL_GPL(do_madvise);
SYSCALL_DEFINE3(madvise, unsigned long, start, size_t, len_in, int, behavior)
{
--
2.17.1
From: SeongJae Park <[email protected]>
DAMON can be used as a primitive for data access pattern awared memory
maangement optimizations. However, users who want such optimizations
should run DAMON, read the monitoring results, analyze it, plan a new
memory management scheme, and apply the new scheme by themselves. It
would not be too hard, but still require some level of efforts. For
complicated optimizations, this effort is inevitable.
That said, in many cases, users would simply want to apply an actions to
a memory region of a specific size having a specific access frequency
for a specific time. For example, "page out a memory region larger than
100 MiB but having a low access frequency more than 10 minutes", or "Use
THP for a memory region larger than 2 MiB having a high access frequency
for more than 2 seconds".
For such optimizations, users will need to first account the age of each
region themselves. To reduce such efforts, this commit implements a
simple age account of each region in DAMON. For each aggregation step,
DAMON compares the access frequency and start/end address of each region
with those from last aggregation and reset the age of the region if the
change is significant. Else, the age is incremented.
Signed-off-by: SeongJae Park <[email protected]>
---
include/linux/damon.h | 5 ++
mm/damon.c | 105 ++++++++++++++++++++++++++++++++++++++++--
2 files changed, 106 insertions(+), 4 deletions(-)
diff --git a/include/linux/damon.h b/include/linux/damon.h
index 47fb0ec03030..49205c71c63d 100644
--- a/include/linux/damon.h
+++ b/include/linux/damon.h
@@ -22,6 +22,11 @@ struct damon_region {
unsigned long sampling_addr;
unsigned int nr_accesses;
struct list_head list;
+
+ unsigned int age;
+ unsigned long last_vm_start;
+ unsigned long last_vm_end;
+ unsigned int last_nr_accesses;
};
/* Represents a monitoring target task */
diff --git a/mm/damon.c b/mm/damon.c
index 4ca8a822c30c..3eeb729f3947 100644
--- a/mm/damon.c
+++ b/mm/damon.c
@@ -79,6 +79,10 @@ static struct damon_region *damon_new_region(struct damon_ctx *ctx,
region->sampling_addr = damon_rand(ctx, vm_start, vm_end);
INIT_LIST_HEAD(®ion->list);
+ region->age = 0;
+ region->last_vm_start = vm_start;
+ region->last_vm_end = vm_end;
+
return region;
}
@@ -613,11 +617,44 @@ static void kdamond_reset_aggregated(struct damon_ctx *c)
sizeof(r->nr_accesses));
trace_damon_aggregated(t->pid, nr,
r->vm_start, r->vm_end, r->nr_accesses);
+ r->last_nr_accesses = r->nr_accesses;
r->nr_accesses = 0;
}
}
}
+#define diff_of(a, b) (a > b ? a - b : b - a)
+
+/*
+ * Increase or reset the age of the given monitoring target region
+ *
+ * If the area or '->nr_accesses' has changed significantly, reset the '->age'.
+ * Else, increase the age.
+ */
+static void damon_do_count_age(struct damon_region *r, unsigned int threshold)
+{
+ unsigned long sz_threshold = (r->vm_end - r->vm_start) / 5;
+
+ if (diff_of(r->vm_start, r->last_vm_start) +
+ diff_of(r->vm_end, r->last_vm_end) > sz_threshold)
+ r->age = 0;
+ else if (diff_of(r->nr_accesses, r->last_nr_accesses) > threshold)
+ r->age = 0;
+ else
+ r->age++;
+}
+
+static void kdamond_count_age(struct damon_ctx *c, unsigned int threshold)
+{
+ struct damon_task *t;
+ struct damon_region *r;
+
+ damon_for_each_task(c, t) {
+ damon_for_each_region(r, t)
+ damon_do_count_age(r, threshold);
+ }
+}
+
#define sz_damon_region(r) (r->vm_end - r->vm_start)
/*
@@ -626,33 +663,86 @@ static void kdamond_reset_aggregated(struct damon_ctx *c)
static void damon_merge_two_regions(struct damon_region *l,
struct damon_region *r)
{
- l->nr_accesses = (l->nr_accesses * sz_damon_region(l) +
- r->nr_accesses * sz_damon_region(r)) /
- (sz_damon_region(l) + sz_damon_region(r));
+ unsigned long sz_l = sz_damon_region(l), sz_r = sz_damon_region(r);
+
+ l->nr_accesses = (l->nr_accesses * sz_l + r->nr_accesses * sz_r) /
+ (sz_l + sz_r);
+ l->age = (l->age * sz_l + r->age * sz_r) / (sz_l + sz_r);
l->vm_end = r->vm_end;
damon_destroy_region(r);
}
-#define diff_of(a, b) (a > b ? a - b : b - a)
+static inline void set_last_area(struct damon_region *r, struct region *last)
+{
+ r->last_vm_start = last->start;
+ r->last_vm_end = last->end;
+}
+
+static inline void get_last_area(struct damon_region *r, struct region *last)
+{
+ last->start = r->last_vm_start;
+ last->end = r->last_vm_end;
+}
/*
* Merge adjacent regions having similar access frequencies
*
* t task that merge operation will make change
* thres merge regions having '->nr_accesses' diff smaller than this
+ *
+ * After each merge, the biggest mergee region becomes the last shape of the
+ * new region. If two regions splitted from one region at the end of previous
+ * aggregation interval are merged into one region, we handle the two regions
+ * as one big mergee, because it can lead to unproper last shape record if we
+ * don't do so.
+ *
+ * To understand why we take special care for regions splitted from one region,
+ * suppose that a region of size 10 has splitted into two regions of size 4 and
+ * 6. Two regions show similar access frequency for next aggregation interval
+ * and thus now be merged into one region again. Because the split is made
+ * regardless of the access pattern, DAMON should say the region of size 10 had
+ * no area change for last aggregation interval. However, if the two mergees
+ * are handled seperatively, DAMON will say the merged region has changed its
+ * size from 6 to 10.
*/
static void damon_merge_regions_of(struct damon_task *t, unsigned int thres)
{
struct damon_region *r, *prev = NULL, *next;
+ struct region biggest_mergee; /* the biggest region being merged */
+ unsigned long sz_biggest = 0; /* size of the biggest_mergee */
+ unsigned long sz_mergee = 0; /* size of current mergee */
damon_for_each_region_safe(r, next, t) {
if (!prev || prev->vm_end != r->vm_start ||
diff_of(prev->nr_accesses, r->nr_accesses) > thres) {
+ if (sz_biggest)
+ set_last_area(prev, &biggest_mergee);
+
prev = r;
+ sz_biggest = sz_damon_region(prev);
+ get_last_area(prev, &biggest_mergee);
continue;
}
+
+
+ /* Set size of current mergee and biggest mergee */
+ sz_mergee += sz_damon_region(r);
+ if (sz_mergee > sz_biggest) {
+ sz_biggest = sz_mergee;
+ get_last_area(r, &biggest_mergee);
+ }
+
+ /*
+ * If next region and current region is not originated from
+ * same region, initialize the size of mergee.
+ */
+ if (r->last_vm_start != next->last_vm_start)
+ sz_mergee = 0;
+
damon_merge_two_regions(prev, r);
}
+ if (sz_biggest)
+ set_last_area(prev, &biggest_mergee);
}
/*
@@ -685,6 +775,12 @@ static void damon_split_region_at(struct damon_ctx *ctx,
struct damon_region *new;
new = damon_new_region(ctx, r->vm_start + sz_r, r->vm_end);
+ new->age = r->age;
+ new->last_vm_start = r->vm_start;
+ new->last_nr_accesses = r->last_nr_accesses;
+
+ r->last_vm_start = r->vm_start;
+ r->last_vm_end = r->vm_end;
r->vm_end = new->vm_start;
damon_insert_region(new, r, damon_next_region(r));
@@ -874,6 +970,7 @@ static int kdamond_fn(void *data)
if (kdamond_aggregate_interval_passed(ctx)) {
kdamond_merge_regions(ctx, max_nr_accesses / 10);
+ kdamond_count_age(ctx, max_nr_accesses / 10);
if (ctx->aggregate_cb)
ctx->aggregate_cb(ctx);
kdamond_reset_aggregated(ctx);
--
2.17.1
From: SeongJae Park <[email protected]>
After merges of regions, each region should know their last shape in
proper way to measure the changes from the last modification and reset
the age if the changes are significant. This commit adds kunit test
cases checking whether the regions are knowing their last shape properly
after merges of regions.
Signed-off-by: SeongJae Park <[email protected]>
Reviewed-by: Brendan Higgins <[email protected]>
---
mm/damon-test.h | 5 +++++
1 file changed, 5 insertions(+)
diff --git a/mm/damon-test.h b/mm/damon-test.h
index 498c637b78ff..133de6c70c37 100644
--- a/mm/damon-test.h
+++ b/mm/damon-test.h
@@ -540,6 +540,8 @@ static void damon_test_merge_regions_of(struct kunit *test)
unsigned long saddrs[] = {0, 114, 130, 156, 170};
unsigned long eaddrs[] = {112, 130, 156, 170, 230};
+ unsigned long lsa[] = {0, 114, 130, 156, 184};
+ unsigned long lea[] = {100, 122, 156, 170, 230};
int i;
t = damon_new_task(42);
@@ -556,6 +558,9 @@ static void damon_test_merge_regions_of(struct kunit *test)
r = damon_nth_region_of(t, i);
KUNIT_EXPECT_EQ(test, r->vm_start, saddrs[i]);
KUNIT_EXPECT_EQ(test, r->vm_end, eaddrs[i]);
+ KUNIT_EXPECT_EQ(test, r->last_vm_start, lsa[i]);
+ KUNIT_EXPECT_EQ(test, r->last_vm_end, lea[i]);
+
}
damon_free_task(t);
}
--
2.17.1
From: SeongJae Park <[email protected]>
This commit implements 'schemes' subcommand of the damon userspace tool.
It can be used to describe and apply the data access monitoring-based
operation schemes in more human friendly fashion.
Signed-off-by: SeongJae Park <[email protected]>
---
tools/damon/_convert_damos.py | 125 +++++++++++++++++++++++++++++
tools/damon/_damon.py | 143 ++++++++++++++++++++++++++++++++++
tools/damon/damo | 7 ++
tools/damon/record.py | 135 +++-----------------------------
tools/damon/schemes.py | 105 +++++++++++++++++++++++++
5 files changed, 392 insertions(+), 123 deletions(-)
create mode 100755 tools/damon/_convert_damos.py
create mode 100644 tools/damon/_damon.py
create mode 100644 tools/damon/schemes.py
diff --git a/tools/damon/_convert_damos.py b/tools/damon/_convert_damos.py
new file mode 100755
index 000000000000..0f1e7e3d4ccc
--- /dev/null
+++ b/tools/damon/_convert_damos.py
@@ -0,0 +1,125 @@
+#!/usr/bin/env python3
+
+"""
+Change human readable data access monitoring-based operation schemes to the low
+level input for the '<debugfs>/damon/schemes' file. Below is an example of the
+schemes written in the human readable format:
+
+# format is: <min/max size> <min/max frequency (0-100)> <min/max age> <action>
+# lines starts with '#' or blank are ignored.
+# B/K/M/G/T for Bytes/KiB/MiB/GiB/TiB
+# us/ms/s/m/h/d for micro-seconds/milli-seconds/seconds/minutes/hours/days
+# 'null' means zero, which passes the check
+
+# if a region (no matter of its size) keeps a high access frequency for more
+# than 100ms, put the region on the head of the LRU list (call madvise() with
+# MADV_WILLNEED).
+null null 80 null 100ms null willneed
+
+# if a region keeps a low access frequency for more than 100ms, put the
+# region on the tail of the LRU list (call madvise() with MADV_COLD).
+0B 0B 10 20 200ms 1h cold
+
+# if a region keeps a very low access frequency for more than 100ms, swap
+# out the region immediately (call madvise() with MADV_PAGEOUT).
+0B null 0 10 100ms 2h pageout
+
+# if a region of a size bigger than 2MiB keeps a very high access frequency
+# for more than 100ms, let the region to use huge pages (call madvise()
+# with MADV_HUGEPAGE).
+2M null 90 99 100ms 2h hugepage
+
+# If a regions of a size bigger than 2MiB keeps no high access frequency
+# for more than 100ms, avoid the region from using huge pages (call
+# madvise() with MADV_NOHUGEPAGE).
+2M null 0 25 100ms 2h nohugepage
+"""
+
+import argparse
+
+unit_to_bytes = {'B': 1, 'K': 1024, 'M': 1024 * 1024, 'G': 1024 * 1024 * 1024,
+ 'T': 1024 * 1024 * 1024 * 1024}
+
+def text_to_bytes(txt):
+ if txt == 'null':
+ return 0
+ unit = txt[-1]
+ number = int(txt[:-1])
+ return number * unit_to_bytes[unit]
+
+unit_to_usecs = {'us': 1, 'ms': 1000, 's': 1000 * 1000, 'm': 60 * 1000 * 1000,
+ 'h': 60 * 60 * 1000 * 1000, 'd': 24 * 60 * 60 * 1000 * 1000}
+
+def text_to_us(txt):
+ if txt == 'null':
+ return 0
+ unit = txt[-2:]
+ if unit in ['us', 'ms']:
+ number = int(txt[:-2])
+ else:
+ unit = txt[-1]
+ number = int(txt[:-1])
+ return number * unit_to_usecs[unit]
+
+damos_action_to_int = {'DAMOS_WILLNEED': 0, 'DAMOS_COLD': 1,
+ 'DAMOS_PAGEOUT': 2, 'DAMOS_HUGEPAGE': 3, 'DAMOS_NOHUGEPAGE': 4}
+
+def text_to_damos_action(txt):
+ return damos_action_to_int['DAMOS_' + txt.upper()]
+
+def text_to_nr_accesses(txt, max_nr_accesses):
+ if txt == 'null':
+ return 0
+ return int(int(txt) * max_nr_accesses / 100)
+
+def debugfs_scheme(line, sample_interval, aggr_interval):
+ fields = line.split()
+ if len(fields) != 7:
+ print('wrong input line: %s' % line)
+ exit(1)
+
+ limit_nr_accesses = aggr_interval / sample_interval
+ try:
+ min_sz = text_to_bytes(fields[0])
+ max_sz = text_to_bytes(fields[1])
+ min_nr_accesses = text_to_nr_accesses(fields[2], limit_nr_accesses)
+ max_nr_accesses = text_to_nr_accesses(fields[3], limit_nr_accesses)
+ min_age = text_to_us(fields[4]) / aggr_interval
+ max_age = text_to_us(fields[5]) / aggr_interval
+ action = text_to_damos_action(fields[6])
+ except:
+ print('wrong input field')
+ raise
+ return '%d\t%d\t%d\t%d\t%d\t%d\t%d' % (min_sz, max_sz, min_nr_accesses,
+ max_nr_accesses, min_age, max_age, action)
+
+def convert(schemes_file, sample_interval, aggr_interval):
+ lines = []
+ with open(schemes_file, 'r') as f:
+ for line in f:
+ if line.startswith('#'):
+ continue
+ line = line.strip()
+ if line == '':
+ continue
+ lines.append(debugfs_scheme(line, sample_interval, aggr_interval))
+ return '\n'.join(lines)
+
+def main():
+ parser = argparse.ArgumentParser()
+ parser.add_argument('input', metavar='<file>',
+ help='input file describing the schemes')
+ parser.add_argument('-s', '--sample', metavar='<interval>', type=int,
+ default=5000, help='sampling interval (us)')
+ parser.add_argument('-a', '--aggr', metavar='<interval>', type=int,
+ default=100000, help='aggregation interval (us)')
+ args = parser.parse_args()
+
+ schemes_file = args.input
+ sample_interval = args.sample
+ aggr_interval = args.aggr
+
+ print(convert(schemes_file, sample_interval, aggr_interval))
+
+if __name__ == '__main__':
+ main()
diff --git a/tools/damon/_damon.py b/tools/damon/_damon.py
new file mode 100644
index 000000000000..0a703ec7471a
--- /dev/null
+++ b/tools/damon/_damon.py
@@ -0,0 +1,143 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: GPL-2.0
+
+"""
+Contains core functions for DAMON debugfs control.
+"""
+
+import os
+import subprocess
+
+debugfs_attrs = None
+debugfs_record = None
+debugfs_schemes = None
+debugfs_pids = None
+debugfs_monitor_on = None
+
+def set_target_pid(pid):
+ return subprocess.call('echo %s > %s' % (pid, debugfs_pids), shell=True,
+ executable='/bin/bash')
+
+def turn_damon(on_off):
+ return subprocess.call("echo %s > %s" % (on_off, debugfs_monitor_on),
+ shell=True, executable="/bin/bash")
+
+def is_damon_running():
+ with open(debugfs_monitor_on, 'r') as f:
+ return f.read().strip() == 'on'
+
+class Attrs:
+ sample_interval = None
+ aggr_interval = None
+ regions_update_interval = None
+ min_nr_regions = None
+ max_nr_regions = None
+ rbuf_len = None
+ rfile_path = None
+ schemes = None
+
+ def __init__(self, s, a, r, n, x, l, f, c):
+ self.sample_interval = s
+ self.aggr_interval = a
+ self.regions_update_interval = r
+ self.min_nr_regions = n
+ self.max_nr_regions = x
+ self.rbuf_len = l
+ self.rfile_path = f
+ self.schemes = c
+
+ def __str__(self):
+ return "%s %s %s %s %s %s %s\n%s" % (self.sample_interval,
+ self.aggr_interval, self.regions_update_interval,
+ self.min_nr_regions, self.max_nr_regions, self.rbuf_len,
+ self.rfile_path, self.schemes)
+
+ def attr_str(self):
+ return "%s %s %s %s %s " % (self.sample_interval, self.aggr_interval,
+ self.regions_update_interval, self.min_nr_regions,
+ self.max_nr_regions)
+
+ def record_str(self):
+ return '%s %s ' % (self.rbuf_len, self.rfile_path)
+
+ def apply(self):
+ ret = subprocess.call('echo %s > %s' % (self.attr_str(), debugfs_attrs),
+ shell=True, executable='/bin/bash')
+ if ret:
+ return ret
+ ret = subprocess.call('echo %s > %s' % (self.record_str(),
+ debugfs_record), shell=True, executable='/bin/bash')
+ if ret:
+ return ret
+ return subprocess.call('echo %s > %s' % (
+ self.schemes.replace('\n', ' '), debugfs_schemes), shell=True,
+ executable='/bin/bash')
+
+def current_attrs():
+ with open(debugfs_attrs, 'r') as f:
+ attrs = f.read().split()
+ attrs = [int(x) for x in attrs]
+
+ with open(debugfs_record, 'r') as f:
+ rattrs = f.read().split()
+ attrs.append(int(rattrs[0]))
+ attrs.append(rattrs[1])
+
+ with open(debugfs_schemes, 'r') as f:
+ schemes = f.read()
+ attrs.append(schemes)
+
+ return Attrs(*attrs)
+
+def chk_update_debugfs(debugfs):
+ global debugfs_attrs
+ global debugfs_record
+ global debugfs_schemes
+ global debugfs_pids
+ global debugfs_monitor_on
+
+ debugfs_damon = os.path.join(debugfs, 'damon')
+ debugfs_attrs = os.path.join(debugfs_damon, 'attrs')
+ debugfs_record = os.path.join(debugfs_damon, 'record')
+ debugfs_schemes = os.path.join(debugfs_damon, 'schemes')
+ debugfs_pids = os.path.join(debugfs_damon, 'pids')
+ debugfs_monitor_on = os.path.join(debugfs_damon, 'monitor_on')
+
+ if not os.path.isdir(debugfs_damon):
+ print("damon debugfs dir (%s) not found", debugfs_damon)
+ exit(1)
+
+ for f in [debugfs_attrs, debugfs_record, debugfs_schemes, debugfs_pids,
+ debugfs_monitor_on]:
+ if not os.path.isfile(f):
+ print("damon debugfs file (%s) not found" % f)
+ exit(1)
+
+def cmd_args_to_attrs(args):
+ "Generate attributes with specified arguments"
+ sample_interval = args.sample
+ aggr_interval = args.aggr
+ regions_update_interval = args.updr
+ min_nr_regions = args.minr
+ max_nr_regions = args.maxr
+ rbuf_len = args.rbuf
+ if not os.path.isabs(args.out):
+ args.out = os.path.join(os.getcwd(), args.out)
+ rfile_path = args.out
+ schemes = args.schemes
+ return Attrs(sample_interval, aggr_interval, regions_update_interval,
+ min_nr_regions, max_nr_regions, rbuf_len, rfile_path, schemes)
+
+def set_attrs_argparser(parser):
+ parser.add_argument('-d', '--debugfs', metavar='<debugfs>', type=str,
+ default='/sys/kernel/debug', help='debugfs mounted path')
+ parser.add_argument('-s', '--sample', metavar='<interval>', type=int,
+ default=5000, help='sampling interval')
+ parser.add_argument('-a', '--aggr', metavar='<interval>', type=int,
+ default=100000, help='aggregate interval')
+ parser.add_argument('-u', '--updr', metavar='<interval>', type=int,
+ default=1000000, help='regions update interval')
+ parser.add_argument('-n', '--minr', metavar='<# regions>', type=int,
+ default=10, help='minimal number of regions')
+ parser.add_argument('-m', '--maxr', metavar='<# regions>', type=int,
+ default=1000, help='maximum number of regions')
diff --git a/tools/damon/damo b/tools/damon/damo
index 58e1099ae5fc..ce7180069bef 100755
--- a/tools/damon/damo
+++ b/tools/damon/damo
@@ -5,6 +5,7 @@ import argparse
import record
import report
+import schemes
class SubCmdHelpFormatter(argparse.RawDescriptionHelpFormatter):
def _format_action(self, action):
@@ -25,6 +26,10 @@ parser_record = subparser.add_parser('record',
help='record data accesses of the given target processes')
record.set_argparser(parser_record)
+parser_schemes = subparser.add_parser('schemes',
+ help='apply operation schemes to the given target process')
+schemes.set_argparser(parser_schemes)
+
parser_report = subparser.add_parser('report',
help='report the recorded data accesses in the specified form')
report.set_argparser(parser_report)
@@ -33,5 +38,7 @@ args = parser.parse_args()
if args.command == 'record':
record.main(args)
+elif args.command == 'schemes':
+ schemes.main(args)
elif args.command == 'report':
report.main(args)
diff --git a/tools/damon/record.py b/tools/damon/record.py
index a547d479a103..3bbf7b8359da 100644
--- a/tools/damon/record.py
+++ b/tools/damon/record.py
@@ -6,28 +6,12 @@ Record data access patterns of the target process.
"""
import argparse
-import copy
import os
import signal
import subprocess
import time
-debugfs_attrs = None
-debugfs_record = None
-debugfs_pids = None
-debugfs_monitor_on = None
-
-def set_target_pid(pid):
- return subprocess.call('echo %s > %s' % (pid, debugfs_pids), shell=True,
- executable='/bin/bash')
-
-def turn_damon(on_off):
- return subprocess.call("echo %s > %s" % (on_off, debugfs_monitor_on),
- shell=True, executable="/bin/bash")
-
-def is_damon_running():
- with open(debugfs_monitor_on, 'r') as f:
- return f.read().strip() == 'on'
+import _damon
def do_record(target, is_target_cmd, attrs, old_attrs):
if os.path.isfile(attrs.rfile_path):
@@ -36,93 +20,29 @@ def do_record(target, is_target_cmd, attrs, old_attrs):
if attrs.apply():
print('attributes (%s) failed to be applied' % attrs)
cleanup_exit(old_attrs, -1)
- print('# damon attrs: %s' % attrs)
+ print('# damon attrs: %s %s' % (attrs.attr_str(), attrs.record_str()))
if is_target_cmd:
p = subprocess.Popen(target, shell=True, executable='/bin/bash')
target = p.pid
- if set_target_pid(target):
+ if _damon.set_target_pid(target):
print('pid setting (%s) failed' % target)
cleanup_exit(old_attrs, -2)
- if turn_damon('on'):
+ if _damon.turn_damon('on'):
print('could not turn on damon' % target)
cleanup_exit(old_attrs, -3)
if is_target_cmd:
p.wait()
while True:
# damon will turn it off by itself if the target tasks are terminated.
- if not is_damon_running():
+ if not _damon.is_damon_running():
break
time.sleep(1)
cleanup_exit(old_attrs, 0)
-class Attrs:
- sample_interval = None
- aggr_interval = None
- regions_update_interval = None
- min_nr_regions = None
- max_nr_regions = None
- rbuf_len = None
- rfile_path = None
-
- def __init__(self, s, a, r, n, x, l, f):
- self.sample_interval = s
- self.aggr_interval = a
- self.regions_update_interval = r
- self.min_nr_regions = n
- self.max_nr_regions = x
- self.rbuf_len = l
- self.rfile_path = f
-
- def __str__(self):
- return "%s %s %s %s %s %s %s" % (self.sample_interval, self.aggr_interval,
- self.regions_update_interval, self.min_nr_regions,
- self.max_nr_regions, self.rbuf_len, self.rfile_path)
-
- def attr_str(self):
- return "%s %s %s %s %s " % (self.sample_interval, self.aggr_interval,
- self.regions_update_interval, self.min_nr_regions,
- self.max_nr_regions)
-
- def record_str(self):
- return '%s %s ' % (self.rbuf_len, self.rfile_path)
-
- def apply(self):
- ret = subprocess.call('echo %s > %s' % (self.attr_str(), debugfs_attrs),
- shell=True, executable='/bin/bash')
- if ret:
- return ret
- return subprocess.call('echo %s > %s' % (self.record_str(),
- debugfs_record), shell=True, executable='/bin/bash')
-
-def current_attrs():
- with open(debugfs_attrs, 'r') as f:
- attrs = f.read().split()
- attrs = [int(x) for x in attrs]
-
- with open(debugfs_record, 'r') as f:
- rattrs = f.read().split()
- attrs.append(int(rattrs[0]))
- attrs.append(rattrs[1])
- return Attrs(*attrs)
-
-def cmd_args_to_attrs(args):
- "Generate attributes with specified arguments"
- sample_interval = args.sample
- aggr_interval = args.aggr
- regions_update_interval = args.updr
- min_nr_regions = args.minr
- max_nr_regions = args.maxr
- rbuf_len = args.rbuf
- if not os.path.isabs(args.out):
- args.out = os.path.join(os.getcwd(), args.out)
- rfile_path = args.out
- return Attrs(sample_interval, aggr_interval, regions_update_interval,
- min_nr_regions, max_nr_regions, rbuf_len, rfile_path)
-
def cleanup_exit(orig_attrs, exit_code):
- if is_damon_running():
- if turn_damon('off'):
+ if _damon.is_damon_running():
+ if _damon.turn_damon('off'):
print('failed to turn damon off!')
if orig_attrs:
if orig_attrs.apply():
@@ -133,51 +53,19 @@ def sighandler(signum, frame):
print('\nsignal %s received' % signum)
cleanup_exit(orig_attrs, signum)
-def chk_update_debugfs(debugfs):
- global debugfs_attrs
- global debugfs_record
- global debugfs_pids
- global debugfs_monitor_on
-
- debugfs_damon = os.path.join(debugfs, 'damon')
- debugfs_attrs = os.path.join(debugfs_damon, 'attrs')
- debugfs_record = os.path.join(debugfs_damon, 'record')
- debugfs_pids = os.path.join(debugfs_damon, 'pids')
- debugfs_monitor_on = os.path.join(debugfs_damon, 'monitor_on')
-
- if not os.path.isdir(debugfs_damon):
- print("damon debugfs dir (%s) not found", debugfs_damon)
- exit(1)
-
- for f in [debugfs_attrs, debugfs_record, debugfs_pids, debugfs_monitor_on]:
- if not os.path.isfile(f):
- print("damon debugfs file (%s) not found" % f)
- exit(1)
-
def chk_permission():
if os.geteuid() != 0:
print("Run as root")
exit(1)
def set_argparser(parser):
+ _damon.set_attrs_argparser(parser)
parser.add_argument('target', type=str, metavar='<target>',
help='the target command or the pid to record')
- parser.add_argument('-s', '--sample', metavar='<interval>', type=int,
- default=5000, help='sampling interval')
- parser.add_argument('-a', '--aggr', metavar='<interval>', type=int,
- default=100000, help='aggregate interval')
- parser.add_argument('-u', '--updr', metavar='<interval>', type=int,
- default=1000000, help='regions update interval')
- parser.add_argument('-n', '--minr', metavar='<# regions>', type=int,
- default=10, help='minimal number of regions')
- parser.add_argument('-m', '--maxr', metavar='<# regions>', type=int,
- default=1000, help='maximum number of regions')
parser.add_argument('-l', '--rbuf', metavar='<len>', type=int,
default=1024*1024, help='length of record result buffer')
parser.add_argument('-o', '--out', metavar='<file path>', type=str,
default='damon.data', help='output file path')
- parser.add_argument('-d', '--debugfs', metavar='<debugfs>', type=str,
- default='/sys/kernel/debug', help='debugfs mounted path')
def main(args=None):
global orig_attrs
@@ -187,13 +75,14 @@ def main(args=None):
args = parser.parse_args()
chk_permission()
- chk_update_debugfs(args.debugfs)
+ _damon.chk_update_debugfs(args.debugfs)
signal.signal(signal.SIGINT, sighandler)
signal.signal(signal.SIGTERM, sighandler)
- orig_attrs = current_attrs()
+ orig_attrs = _damon.current_attrs()
- new_attrs = cmd_args_to_attrs(args)
+ args.schemes = ''
+ new_attrs = _damon.cmd_args_to_attrs(args)
target = args.target
target_fields = target.split()
diff --git a/tools/damon/schemes.py b/tools/damon/schemes.py
new file mode 100644
index 000000000000..408a73813234
--- /dev/null
+++ b/tools/damon/schemes.py
@@ -0,0 +1,105 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: GPL-2.0
+
+"""
+Apply given operation schemes to the target process.
+"""
+
+import argparse
+import os
+import signal
+import subprocess
+import time
+
+import _convert_damos
+import _damon
+
+def run_damon(target, is_target_cmd, attrs, old_attrs):
+ if os.path.isfile(attrs.rfile_path):
+ os.rename(attrs.rfile_path, attrs.rfile_path + '.old')
+
+ if attrs.apply():
+ print('attributes (%s) failed to be applied' % attrs)
+ cleanup_exit(old_attrs, -1)
+ print('# damon attrs: %s %s' % (attrs.attr_str(), attrs.record_str()))
+ for line in attrs.schemes.split('\n'):
+ print('# scheme: %s' % line)
+ if is_target_cmd:
+ p = subprocess.Popen(target, shell=True, executable='/bin/bash')
+ target = p.pid
+ if _damon.set_target_pid(target):
+ print('pid setting (%s) failed' % target)
+ cleanup_exit(old_attrs, -2)
+ if _damon.turn_damon('on'):
+ print('could not turn on damon' % target)
+ cleanup_exit(old_attrs, -3)
+ if is_target_cmd:
+ p.wait()
+ while True:
+ # damon will turn it off by itself if the target tasks are terminated.
+ if not _damon.is_damon_running():
+ break
+ time.sleep(1)
+
+ cleanup_exit(old_attrs, 0)
+
+def cleanup_exit(orig_attrs, exit_code):
+ if _damon.is_damon_running():
+ if turn_damon('off'):
+ print('failed to turn damon off!')
+ if orig_attrs:
+ if orig_attrs.apply():
+ print('original attributes (%s) restoration failed!' % orig_attrs)
+ exit(exit_code)
+
+def sighandler(signum, frame):
+ print('\nsignal %s received' % signum)
+ cleanup_exit(orig_attrs, signum)
+
+def chk_permission():
+ if os.geteuid() != 0:
+ print("Run as root")
+ exit(1)
+
+def set_argparser(parser):
+ _damon.set_attrs_argparser(parser)
+ parser.add_argument('target', type=str, metavar='<target>',
+ help='the target command or the pid to record')
+ parser.add_argument('-c', '--schemes', metavar='<file>', type=str,
+ default='damon.schemes',
+ help='data access monitoring-based operation schemes')
+
+def main(args=None):
+ global orig_attrs
+ if not args:
+ parser = argparse.ArgumentParser()
+ set_argparser(parser)
+ args = parser.parse_args()
+
+ chk_permission()
+ _damon.chk_update_debugfs(args.debugfs)
+
+ signal.signal(signal.SIGINT, sighandler)
+ signal.signal(signal.SIGTERM, sighandler)
+ orig_attrs = _damon.current_attrs()
+
+ args.rbuf = 0
+ args.out = 'null'
+ args.schemes = _convert_damos.convert(args.schemes, args.sample, args.aggr)
+ new_attrs = _damon.cmd_args_to_attrs(args)
+ target = args.target
+
+ target_fields = target.split()
+ if not subprocess.call('which %s > /dev/null' % target_fields[0],
+ shell=True, executable='/bin/bash'):
+ run_damon(target, True, new_attrs, orig_attrs)
+ else:
+ try:
+ pid = int(target)
+ except:
+ print('target \'%s\' is neither a command, nor a pid' % target)
+ exit(1)
+ run_damon(target, False, new_attrs, orig_attrs)
+
+if __name__ == '__main__':
+ main()
--
2.17.1
From: SeongJae Park <[email protected]>
This commit adds simple selftets for 'schemes' debugfs file of DAMON.
Signed-off-by: SeongJae Park <[email protected]>
---
.../testing/selftests/damon/debugfs_attrs.sh | 29 +++++++++++++++++++
1 file changed, 29 insertions(+)
diff --git a/tools/testing/selftests/damon/debugfs_attrs.sh b/tools/testing/selftests/damon/debugfs_attrs.sh
index d5188b0f71b1..82a98c81975b 100755
--- a/tools/testing/selftests/damon/debugfs_attrs.sh
+++ b/tools/testing/selftests/damon/debugfs_attrs.sh
@@ -97,6 +97,35 @@ fi
echo $ORIG_CONTENT > $file
+# Test schemes file
+file="$DBGFS/schemes"
+
+ORIG_CONTENT=$(cat $file)
+echo "1 2 3 4 5 6 3" > $file
+if [ $? -ne 0 ]
+then
+ echo "$file write fail"
+ echo $ORIG_CONTENT > $file
+ exit 1
+fi
+
+echo "1 2
+3 4 5 6 3" > $file
+if [ $? -eq 0 ]
+then
+ echo "$file splitted write success (expected fail)"
+ echo $ORIG_CONTENT > $file
+ exit 1
+fi
+
+echo > $file
+if [ $? -ne 0 ]
+then
+ echo "$file empty string writing fail"
+ echo $ORIG_CONTENT > $file
+ exit 1
+fi
+
# Test pids file
file="$DBGFS/pids"
--
2.17.1
On Mon, 30 Mar 2020 13:50:35 +0200
SeongJae Park <[email protected]> wrote:
> From: SeongJae Park <[email protected]>
>
> DAMON[1] can be used as a primitive for data access awared memory management
> optimizations. That said, users who want such optimizations should run DAMON,
> read the monitoring results, analyze it, plan a new memory management scheme,
> and apply the new scheme by themselves. Such efforts will be inevitable for
> some complicated optimizations.
>
> However, in many other cases, the users could simply want the system to apply a
> memory management action to a memory region of a specific size having a
> specific access frequency for a specific time. For example, "page out a memory
> region larger than 100 MiB keeping only rare accesses more than 2 minutes", or
> "Do not use THP for a memory region larger than 2 MiB rarely accessed for more
> than 1 seconds".
>
> This RFC patchset makes DAMON to handle such data access monitoring-based
> operation schemes. With this change, users can do the data access awared
> optimizations by simply specifying their schemes to DAMON.
Hi SeongJae,
I'm wondering if I'm misreading the results below or a data handling mixup
occured. See inline.
Thanks,
Jonathan
>
>
> Evaluations
> ===========
>
> Setup
> -----
>
> On my personal QEMU/KVM based virtual machine on an Intel i7 host machine
> running Ubuntu 18.04, I measure runtime and consumed system memory while
> running various realistic workloads with several configurations. I use 13 and
> 12 workloads in PARSEC3[3] and SPLASH-2X[4] benchmark suites, respectively. I
> personally use another wrapper scripts[5] for setup and run of the workloads.
> On top of this patchset, we also applied the DAMON-based operation schemes
> patchset[6] for this evaluation.
>
> Measurement
> ~~~~~~~~~~~
>
> For the measurement of the amount of consumed memory in system global scope, I
> drop caches before starting each of the workloads and monitor 'MemFree' in the
> '/proc/meminfo' file. To make results more stable, I repeat the runs 5 times
> and average results. You can get stdev, min, and max of the numbers among the
> repeated runs in appendix below.
>
> Configurations
> ~~~~~~~~~~~~~~
>
> The configurations I use are as below.
>
> orig: Linux v5.5 with 'madvise' THP policy
> rec: 'orig' plus DAMON running with record feature
> thp: same with 'orig', but use 'always' THP policy
> ethp: 'orig' plus a DAMON operation scheme[6], 'efficient THP'
> prcl: 'orig' plus a DAMON operation scheme, 'proactive reclaim[7]'
>
> I use 'rec' for measurement of DAMON overheads to target workloads and system
> memory. The remaining configs including 'thp', 'ethp', and 'prcl' are for
> measurement of DAMON monitoring accuracy.
>
> 'ethp' and 'prcl' is simple DAMON-based operation schemes developed for
> proof of concepts of DAMON. 'ethp' reduces memory space waste of THP by using
> DAMON for decision of promotions and demotion for huge pages, while 'prcl' is
> as similar as the original work. Those are implemented as below:
>
> # format: <min/max size> <min/max frequency (0-100)> <min/max age> <action>
> # ethp: Use huge pages if a region >2MB shows >5% access rate, use regular
> # pages if a region >2MB shows <5% access rate for >1 second
> 2M null 5 null null null hugepage
> 2M null null 5 1s null nohugepage
>
> # prcl: If a region >4KB shows <5% access rate for >5 seconds, page out.
> 4K null null 5 5s null pageout
>
> Note that both 'ethp' and 'prcl' are designed with my only straightforward
> intuition, because those are for only proof of concepts and monitoring accuracy
> of DAMON. In other words, those are not for production. For production use,
> those should be tuned more.
>
>
> [1] "Redis latency problems troubleshooting", https://redis.io/topics/latency
> [2] "Disable Transparent Huge Pages (THP)",
> https://docs.mongodb.com/manual/tutorial/transparent-huge-pages/
> [3] "The PARSEC Becnhmark Suite", https://parsec.cs.princeton.edu/index.htm
> [4] "SPLASH-2x", https://parsec.cs.princeton.edu/parsec3-doc.htm#splash2x
> [5] "parsec3_on_ubuntu", https://github.com/sjp38/parsec3_on_ubuntu
> [6] "[RFC v4 0/7] Implement Data Access Monitoring-based Memory Operation
> Schemes",
> https://lore.kernel.org/linux-mm/[email protected]/
> [7] "Proactively reclaiming idle memory", https://lwn.net/Articles/787611/
>
>
> Results
> -------
>
> Below two tables show the measurement results. The runtimes are in seconds
> while the memory usages are in KiB. Each configurations except 'orig' shows
> its overhead relative to 'orig' in percent within parenthesises.
>
> runtime orig rec (overhead) thp (overhead) ethp (overhead) prcl (overhead)
> parsec3/blackscholes 107.594 107.956 (0.34) 106.750 (-0.78) 107.672 (0.07) 111.916 (4.02)
> parsec3/bodytrack 79.230 79.368 (0.17) 78.908 (-0.41) 79.705 (0.60) 80.423 (1.50)
> parsec3/canneal 142.831 143.810 (0.69) 123.530 (-13.51) 133.778 (-6.34) 144.998 (1.52)
> parsec3/dedup 11.986 11.959 (-0.23) 11.762 (-1.87) 12.028 (0.35) 13.313 (11.07)
> parsec3/facesim 210.125 209.007 (-0.53) 205.226 (-2.33) 207.766 (-1.12) 209.815 (-0.15)
> parsec3/ferret 191.601 191.177 (-0.22) 190.420 (-0.62) 191.775 (0.09) 192.638 (0.54)
> parsec3/fluidanimate 212.735 212.970 (0.11) 209.151 (-1.68) 211.904 (-0.39) 218.573 (2.74)
> parsec3/freqmine 291.225 290.873 (-0.12) 289.258 (-0.68) 289.884 (-0.46) 298.373 (2.45)
> parsec3/raytrace 118.289 119.586 (1.10) 119.045 (0.64) 119.064 (0.66) 137.919 (16.60)
> parsec3/streamcluster 323.565 328.168 (1.42) 279.565 (-13.60) 287.452 (-11.16) 333.244 (2.99)
> parsec3/swaptions 155.140 155.473 (0.21) 153.816 (-0.85) 156.423 (0.83) 156.237 (0.71)
> parsec3/vips 58.979 59.311 (0.56) 58.733 (-0.42) 59.005 (0.04) 61.062 (3.53)
> parsec3/x264 70.539 68.413 (-3.01) 64.760 (-8.19) 67.180 (-4.76) 68.103 (-3.45)
> splash2x/barnes 80.414 81.751 (1.66) 73.585 (-8.49) 80.232 (-0.23) 115.753 (43.95)
> splash2x/fft 33.902 34.111 (0.62) 24.228 (-28.53) 29.926 (-11.73) 44.438 (31.08)
> splash2x/lu_cb 85.556 86.001 (0.52) 84.538 (-1.19) 86.000 (0.52) 91.447 (6.89)
> splash2x/lu_ncb 93.399 93.652 (0.27) 90.463 (-3.14) 94.008 (0.65) 93.901 (0.54)
> splash2x/ocean_cp 45.253 45.191 (-0.14) 43.049 (-4.87) 44.022 (-2.72) 46.588 (2.95)
> splash2x/ocean_ncp 86.927 87.065 (0.16) 50.747 (-41.62) 86.855 (-0.08) 199.553 (129.57)
> splash2x/radiosity 91.433 91.511 (0.09) 90.626 (-0.88) 91.865 (0.47) 104.524 (14.32)
> splash2x/radix 31.923 32.023 (0.31) 25.194 (-21.08) 32.035 (0.35) 39.231 (22.89)
> splash2x/raytrace 84.367 84.677 (0.37) 82.417 (-2.31) 83.505 (-1.02) 84.857 (0.58)
> splash2x/volrend 87.499 87.495 (-0.00) 86.775 (-0.83) 87.311 (-0.21) 87.511 (0.01)
> splash2x/water_nsquared 236.397 236.759 (0.15) 219.902 (-6.98) 224.228 (-5.15) 238.562 (0.92)
> splash2x/water_spatial 89.646 89.767 (0.14) 89.735 (0.10) 90.347 (0.78) 103.585 (15.55)
> total 3020.570 3028.080 (0.25) 2852.190 (-5.57) 2953.960 (-2.21) 3276.550 (8.47)
>
>
> memused.avg orig rec (overhead) thp (overhead) ethp (overhead) prcl (overhead)
> parsec3/blackscholes 1785916.600 1834201.400 (2.70) 1826249.200 (2.26) 1828079.200 (2.36) 1712210.600 (-4.13)
> parsec3/bodytrack 1415049.400 1434317.600 (1.36) 1423715.000 (0.61) 1430392.600 (1.08) 1435136.000 (1.42)
> parsec3/canneal 1043489.800 1058617.600 (1.45) 1040484.600 (-0.29) 1048664.800 (0.50) 1050280.000 (0.65)
> parsec3/dedup 2414453.200 2458493.200 (1.82) 2411379.400 (-0.13) 2400516.000 (-0.58) 2461120.800 (1.93)
> parsec3/facesim 541597.200 550097.400 (1.57) 544364.600 (0.51) 553240.000 (2.15) 552316.400 (1.98)
> parsec3/ferret 317986.600 332346.000 (4.52) 320218.000 (0.70) 331085.000 (4.12) 330895.200 (4.06)
> parsec3/fluidanimate 576183.400 585442.000 (1.61) 577780.200 (0.28) 587703.400 (2.00) 506501.000 (-12.09)
> parsec3/freqmine 990869.200 997817.000 (0.70) 990350.400 (-0.05) 997669.000 (0.69) 763325.800 (-22.96)
> parsec3/raytrace 1748370.800 1757109.200 (0.50) 1746153.800 (-0.13) 1757830.400 (0.54) 1581455.800 (-9.55)
> parsec3/streamcluster 121521.800 140452.400 (15.58) 129725.400 (6.75) 132266.000 (8.84) 130558.200 (7.44)
> parsec3/swaptions 15592.400 29018.800 (86.11) 14765.800 (-5.30) 27260.200 (74.83) 26631.600 (70.80)
> parsec3/vips 2957567.600 2967993.800 (0.35) 2956623.200 (-0.03) 2973062.600 (0.52) 2951402.000 (-0.21)
> parsec3/x264 3169012.400 3175048.800 (0.19) 3190345.400 (0.67) 3189353.000 (0.64) 3172924.200 (0.12)
> splash2x/barnes 1209066.000 1213125.400 (0.34) 1217261.400 (0.68) 1209661.600 (0.05) 921041.800 (-23.82)
> splash2x/fft 9359313.200 9195213.000 (-1.75) 9377562.400 (0.19) 9050957.600 (-3.29) 9517977.000 (1.70)
> splash2x/lu_cb 514966.200 522939.400 (1.55) 520870.400 (1.15) 522635.000 (1.49) 329933.600 (-35.93)
> splash2x/lu_ncb 514180.400 525974.800 (2.29) 521420.200 (1.41) 521063.600 (1.34) 523557.000 (1.82)
> splash2x/ocean_cp 3346493.400 3288078.000 (-1.75) 3382253.800 (1.07) 3289477.600 (-1.70) 3260810.400 (-2.56)
> splash2x/ocean_ncp 3909966.400 3882968.800 (-0.69) 7037196.000 (79.98) 4046363.400 (3.49) 3471452.400 (-11.22)
> splash2x/radiosity 1471119.400 1470626.800 (-0.03) 1482604.200 (0.78) 1472718.400 (0.11) 546893.600 (-62.82)
> splash2x/radix 1748360.800 1729163.400 (-1.10) 1371463.200 (-21.56) 1701993.600 (-2.65) 1817519.600 (3.96)
> splash2x/raytrace 46670.000 60172.200 (28.93) 51901.600 (11.21) 60782.600 (30.24) 52644.800 (12.80)
> splash2x/volrend 150666.600 167444.200 (11.14) 151335.200 (0.44) 163345.000 (8.41) 162760.000 (8.03)
> splash2x/water_nsquared 45720.200 59422.400 (29.97) 46031.000 (0.68) 61801.400 (35.17) 62627.000 (36.98)
> splash2x/water_spatial 663052.200 672855.800 (1.48) 665787.600 (0.41) 674696.200 (1.76) 471052.600 (-28.96)
> total 40077300.000 40108900.000 (0.08) 42997900.000 (7.29) 40032700.000 (-0.11) 37813000.000 (-5.65)
>
>
> DAMON Overheads
> ~~~~~~~~~~~~~~~
>
> In total, DAMON recording feature incurs 0.25% runtime overhead (up to 1.66% in
> worst case with 'splash2x/barnes') and 0.08% memory space overhead.
>
> For convenience test run of 'rec', I use a Python wrapper. The wrapper
> constantly consumes about 10-15MB of memory. This becomes high memory overhead
> if the target workload has small memory footprint. In detail, 16%, 86%, 29%,
> 11%, and 30% overheads shown for parsec3/streamcluster (125 MiB),
> parsec3/swaptions (15 MiB), splash2x/raytrace (45 MiB), splash2x/volrend (151
> MiB), and splash2x/water_nsquared (46 MiB)). Nonetheless, the overheads are
> not from DAMON, but from the wrapper, and thus should be ignored. This fake
> memory overhead continues in 'ethp' and 'prcl', as those configurations are
> also using the Python wrapper.
>
>
> Efficient THP
> ~~~~~~~~~~~~~
>
> THP 'always' enabled policy achieves 5.57% speedup but incurs 7.29% memory
> overhead. It achieves 41.62% speedup in best case, but 79.98% memory overhead
> in worst case. Interestingly, both the best and worst case are with
> 'splash2x/ocean_ncp').
The results above don't seems to support this any more?
> runtime orig rec (overhead) thp (overhead) ethp (overhead) prcl (overhead)
> splash2x/ocean_ncp 86.927 87.065 (0.16) 50.747 (-41.62) 86.855 (-0.08) 199.553 (129.57)
>
> The 2-lines implementation of data access monitoring based THP version ('ethp')
> shows 2.21% speedup and -0.11% memory overhead. In other words, 'ethp' removes
> 100% of THP memory waste while preserving 39.67% of THP speedup in total.
>
>
> Proactive Reclamation
> ~~~~~~~~~~~~~~~~~~~~
>
> As same to the original work, I use 'zram' swap device for this configuration.
>
> In total, our 1 line implementation of Proactive Reclamation, 'prcl', incurred
> 8.47% runtime overhead in total while achieving 5.65% system memory usage
> reduction.
>
> Nonetheless, as the memory usage is calculated with 'MemFree' in
> '/proc/meminfo', it contains the SwapCached pages. As the swapcached pages can
> be easily evicted, I also measured the residential set size of the workloads:
>
> rss.avg orig rec (overhead) thp (overhead) ethp (overhead) prcl (overhead)
> parsec3/blackscholes 592502.000 589764.400 (-0.46) 592132.600 (-0.06) 593702.000 (0.20) 406639.400 (-31.37)
> parsec3/bodytrack 32365.400 32195.000 (-0.53) 32210.800 (-0.48) 32114.600 (-0.77) 21537.600 (-33.45)
> parsec3/canneal 839904.200 840292.200 (0.05) 836866.400 (-0.36) 838263.200 (-0.20) 837895.800 (-0.24)
> parsec3/dedup 1208337.200 1218465.600 (0.84) 1233278.600 (2.06) 1200490.200 (-0.65) 882911.400 (-26.93)
> parsec3/facesim 311380.800 311363.600 (-0.01) 315642.600 (1.37) 312573.400 (0.38) 310257.400 (-0.36)
> parsec3/ferret 99514.800 99542.000 (0.03) 100454.200 (0.94) 99879.800 (0.37) 89679.200 (-9.88)
> parsec3/fluidanimate 531760.800 531735.200 (-0.00) 531865.400 (0.02) 531940.800 (0.03) 440781.000 (-17.11)
> parsec3/freqmine 552455.400 552882.600 (0.08) 555793.600 (0.60) 553019.800 (0.10) 58067.000 (-89.49)
> parsec3/raytrace 894798.400 894953.400 (0.02) 892223.400 (-0.29) 893012.400 (-0.20) 315259.800 (-64.77)
> parsec3/streamcluster 110780.400 110856.800 (0.07) 110954.000 (0.16) 111310.800 (0.48) 108066.800 (-2.45)
> parsec3/swaptions 5614.600 5645.600 (0.55) 5553.200 (-1.09) 5552.600 (-1.10) 3251.800 (-42.08)
> parsec3/vips 31942.200 31752.800 (-0.59) 32042.600 (0.31) 32226.600 (0.89) 29012.200 (-9.17)
> parsec3/x264 81770.800 81609.200 (-0.20) 82800.800 (1.26) 82612.200 (1.03) 81805.800 (0.04)
> splash2x/barnes 1216515.600 1217113.800 (0.05) 1225605.600 (0.75) 1217325.000 (0.07) 540108.400 (-55.60)
> splash2x/fft 9668660.600 9751350.800 (0.86) 9773806.400 (1.09) 9613555.400 (-0.57) 7951241.800 (-17.76)
> splash2x/lu_cb 510368.800 510095.800 (-0.05) 514350.600 (0.78) 510276.000 (-0.02) 311584.800 (-38.95)
> splash2x/lu_ncb 509904.800 510001.600 (0.02) 513847.000 (0.77) 510073.400 (0.03) 509905.600 (0.00)
> splash2x/ocean_cp 3389550.600 3404466.000 (0.44) 3443363.600 (1.59) 3410388.000 (0.61) 3330608.600 (-1.74)
> splash2x/ocean_ncp 3923723.200 3911148.200 (-0.32) 7175800.400 (82.88) 4104482.400 (4.61) 2030525.000 (-48.25)
> splash2x/radiosity 1472994.600 1475946.400 (0.20) 1485636.800 (0.86) 1476193.000 (0.22) 262161.400 (-82.20)
> splash2x/radix 1750329.800 1765697.000 (0.88) 1413304.000 (-19.25) 1754154.400 (0.22) 1516142.600 (-13.38)
> splash2x/raytrace 23149.600 23208.000 (0.25) 28574.400 (23.43) 26694.600 (15.31) 16257.800 (-29.77)
> splash2x/volrend 43968.800 43919.000 (-0.11) 44087.600 (0.27) 44224.000 (0.58) 32484.400 (-26.12)
> splash2x/water_nsquared 29348.000 29338.400 (-0.03) 29604.600 (0.87) 29779.400 (1.47) 23644.800 (-19.43)
> splash2x/water_spatial 655263.600 655097.800 (-0.03) 655199.200 (-0.01) 656282.400 (0.16) 379816.800 (-42.04)
> total 28486900.000 28598400.000 (0.39) 31625000.000 (11.02) 28640100.000 (0.54) 20489600.000 (-28.07)
>
> In total, 28.07% of residential sets were reduced.
>
> With parsec3/freqmine, 'prcl' reduced 22.96% of system memory usage and 89.49%
> of residential sets while incurring only 2.45% runtime overhead.
>
>
> Sequence Of Patches
> ===================
>
> The patches are based on the v5.6 plus v7 DAMON patchset[1] and Minchan's
> ``do_madvise()`` patch[2]. Minchan's patch was necessary for reuse of
> ``madvise()`` code in DAMON. You can also clone the complete git tree:
>
> $ git clone git://github.com/sjp38/linux -b damos/rfc/v5
>
> The web is also available:
> https://github.com/sjp38/linux/releases/tag/damos/rfc/v5
>
>
> [1] https://lore.kernel.org/linux-mm/[email protected]/
> [2] https://lore.kernel.org/linux-mm/[email protected]/
>
> The first patch allows DAMON to reuse ``madvise()`` code for the actions. The
> second patch accounts age of each region. The third patch implements the
> handling of the schemes in DAMON and exports a kernel space programming
> interface for it. The fourth patch implements a debugfs interface for
> privileged people and programs. The fifth and sixth patches each adds
> kunittests and selftests for these changes, and finally the seventhe patch
> modifies the user space tool for DAMON to support description and applying of
> schemes in human freiendly way.
>
>
> Patch History
> =============
>
> Changes from RFC v4
> (https://lore.kernel.org/linux-mm/[email protected]/)
> - Handle CONFIG_ADVISE_SYSCALL
> - Clean up code (Jonathan Cameron)
> - Update test results
> - Rebase on v5.6 + DAMON v7
>
> Changes from RFC v3
> (https://lore.kernel.org/linux-mm/[email protected]/)
> - Add Reviewed-by from Brendan Higgins
> - Code cleanup: Modularize madvise() call
> - Fix a trivial bug in the wrapper python script
> - Add more stable and detailed evaluation results with updated ETHP scheme
>
> Changes from RFC v2
> (https://lore.kernel.org/linux-mm/[email protected]/)
> - Fix aging mechanism for more better 'old region' selection
> - Add more kunittests and kselftests for this patchset
> - Support more human friedly description and application of 'schemes'
>
> Changes from RFC v1
> (https://lore.kernel.org/linux-mm/[email protected]/)
> - Properly adjust age accounting related properties after splitting, merging,
> and action applying
> SeongJae Park (7):
> mm/madvise: Export do_madvise() to external GPL modules
> mm/damon: Account age of target regions
> mm/damon: Implement data access monitoring-based operation schemes
> mm/damon/schemes: Implement a debugfs interface
> mm/damon-test: Add kunit test case for regions age accounting
> mm/damon/selftests: Add 'schemes' debugfs tests
> damon/tools: Support more human friendly 'schemes' control
>
> include/linux/damon.h | 29 ++
> mm/damon-test.h | 5 +
> mm/damon.c | 428 +++++++++++++++++-
> mm/madvise.c | 1 +
> tools/damon/_convert_damos.py | 125 +++++
> tools/damon/_damon.py | 143 ++++++
> tools/damon/damo | 7 +
> tools/damon/record.py | 135 +-----
> tools/damon/schemes.py | 105 +++++
> .../testing/selftests/damon/debugfs_attrs.sh | 29 ++
> 10 files changed, 878 insertions(+), 129 deletions(-)
> create mode 100755 tools/damon/_convert_damos.py
> create mode 100644 tools/damon/_damon.py
> create mode 100644 tools/damon/schemes.py
>
On Tue, 31 Mar 2020 16:51:55 +0100 Jonathan Cameron <[email protected]> wrote:
> On Mon, 30 Mar 2020 13:50:35 +0200
> SeongJae Park <[email protected]> wrote:
>
> > From: SeongJae Park <[email protected]>
> >
> > DAMON[1] can be used as a primitive for data access awared memory management
> > optimizations. That said, users who want such optimizations should run DAMON,
> > read the monitoring results, analyze it, plan a new memory management scheme,
> > and apply the new scheme by themselves. Such efforts will be inevitable for
> > some complicated optimizations.
> >
> > However, in many other cases, the users could simply want the system to apply a
> > memory management action to a memory region of a specific size having a
> > specific access frequency for a specific time. For example, "page out a memory
> > region larger than 100 MiB keeping only rare accesses more than 2 minutes", or
> > "Do not use THP for a memory region larger than 2 MiB rarely accessed for more
> > than 1 seconds".
> >
> > This RFC patchset makes DAMON to handle such data access monitoring-based
> > operation schemes. With this change, users can do the data access awared
> > optimizations by simply specifying their schemes to DAMON.
>
>
> Hi SeongJae,
>
> I'm wondering if I'm misreading the results below or a data handling mixup
> occured. See inline.
Thank you for question, Jonathan!
>
> Thanks,
>
> Jonathan
>
> >
[...]
> > Results
> > -------
> >
> > Below two tables show the measurement results. The runtimes are in seconds
> > while the memory usages are in KiB. Each configurations except 'orig' shows
> > its overhead relative to 'orig' in percent within parenthesises.
> >
> > runtime orig rec (overhead) thp (overhead) ethp (overhead) prcl (overhead)
> > parsec3/blackscholes 107.594 107.956 (0.34) 106.750 (-0.78) 107.672 (0.07) 111.916 (4.02)
> > parsec3/bodytrack 79.230 79.368 (0.17) 78.908 (-0.41) 79.705 (0.60) 80.423 (1.50)
> > parsec3/canneal 142.831 143.810 (0.69) 123.530 (-13.51) 133.778 (-6.34) 144.998 (1.52)
> > parsec3/dedup 11.986 11.959 (-0.23) 11.762 (-1.87) 12.028 (0.35) 13.313 (11.07)
> > parsec3/facesim 210.125 209.007 (-0.53) 205.226 (-2.33) 207.766 (-1.12) 209.815 (-0.15)
> > parsec3/ferret 191.601 191.177 (-0.22) 190.420 (-0.62) 191.775 (0.09) 192.638 (0.54)
> > parsec3/fluidanimate 212.735 212.970 (0.11) 209.151 (-1.68) 211.904 (-0.39) 218.573 (2.74)
> > parsec3/freqmine 291.225 290.873 (-0.12) 289.258 (-0.68) 289.884 (-0.46) 298.373 (2.45)
> > parsec3/raytrace 118.289 119.586 (1.10) 119.045 (0.64) 119.064 (0.66) 137.919 (16.60)
> > parsec3/streamcluster 323.565 328.168 (1.42) 279.565 (-13.60) 287.452 (-11.16) 333.244 (2.99)
> > parsec3/swaptions 155.140 155.473 (0.21) 153.816 (-0.85) 156.423 (0.83) 156.237 (0.71)
> > parsec3/vips 58.979 59.311 (0.56) 58.733 (-0.42) 59.005 (0.04) 61.062 (3.53)
> > parsec3/x264 70.539 68.413 (-3.01) 64.760 (-8.19) 67.180 (-4.76) 68.103 (-3.45)
> > splash2x/barnes 80.414 81.751 (1.66) 73.585 (-8.49) 80.232 (-0.23) 115.753 (43.95)
> > splash2x/fft 33.902 34.111 (0.62) 24.228 (-28.53) 29.926 (-11.73) 44.438 (31.08)
> > splash2x/lu_cb 85.556 86.001 (0.52) 84.538 (-1.19) 86.000 (0.52) 91.447 (6.89)
> > splash2x/lu_ncb 93.399 93.652 (0.27) 90.463 (-3.14) 94.008 (0.65) 93.901 (0.54)
> > splash2x/ocean_cp 45.253 45.191 (-0.14) 43.049 (-4.87) 44.022 (-2.72) 46.588 (2.95)
> > splash2x/ocean_ncp 86.927 87.065 (0.16) 50.747 (-41.62) 86.855 (-0.08) 199.553 (129.57)
> > splash2x/radiosity 91.433 91.511 (0.09) 90.626 (-0.88) 91.865 (0.47) 104.524 (14.32)
> > splash2x/radix 31.923 32.023 (0.31) 25.194 (-21.08) 32.035 (0.35) 39.231 (22.89)
> > splash2x/raytrace 84.367 84.677 (0.37) 82.417 (-2.31) 83.505 (-1.02) 84.857 (0.58)
> > splash2x/volrend 87.499 87.495 (-0.00) 86.775 (-0.83) 87.311 (-0.21) 87.511 (0.01)
> > splash2x/water_nsquared 236.397 236.759 (0.15) 219.902 (-6.98) 224.228 (-5.15) 238.562 (0.92)
> > splash2x/water_spatial 89.646 89.767 (0.14) 89.735 (0.10) 90.347 (0.78) 103.585 (15.55)
> > total 3020.570 3028.080 (0.25) 2852.190 (-5.57) 2953.960 (-2.21) 3276.550 (8.47)
> >
> >
> > memused.avg orig rec (overhead) thp (overhead) ethp (overhead) prcl (overhead)
> > parsec3/blackscholes 1785916.600 1834201.400 (2.70) 1826249.200 (2.26) 1828079.200 (2.36) 1712210.600 (-4.13)
> > parsec3/bodytrack 1415049.400 1434317.600 (1.36) 1423715.000 (0.61) 1430392.600 (1.08) 1435136.000 (1.42)
> > parsec3/canneal 1043489.800 1058617.600 (1.45) 1040484.600 (-0.29) 1048664.800 (0.50) 1050280.000 (0.65)
> > parsec3/dedup 2414453.200 2458493.200 (1.82) 2411379.400 (-0.13) 2400516.000 (-0.58) 2461120.800 (1.93)
> > parsec3/facesim 541597.200 550097.400 (1.57) 544364.600 (0.51) 553240.000 (2.15) 552316.400 (1.98)
> > parsec3/ferret 317986.600 332346.000 (4.52) 320218.000 (0.70) 331085.000 (4.12) 330895.200 (4.06)
> > parsec3/fluidanimate 576183.400 585442.000 (1.61) 577780.200 (0.28) 587703.400 (2.00) 506501.000 (-12.09)
> > parsec3/freqmine 990869.200 997817.000 (0.70) 990350.400 (-0.05) 997669.000 (0.69) 763325.800 (-22.96)
> > parsec3/raytrace 1748370.800 1757109.200 (0.50) 1746153.800 (-0.13) 1757830.400 (0.54) 1581455.800 (-9.55)
> > parsec3/streamcluster 121521.800 140452.400 (15.58) 129725.400 (6.75) 132266.000 (8.84) 130558.200 (7.44)
> > parsec3/swaptions 15592.400 29018.800 (86.11) 14765.800 (-5.30) 27260.200 (74.83) 26631.600 (70.80)
> > parsec3/vips 2957567.600 2967993.800 (0.35) 2956623.200 (-0.03) 2973062.600 (0.52) 2951402.000 (-0.21)
> > parsec3/x264 3169012.400 3175048.800 (0.19) 3190345.400 (0.67) 3189353.000 (0.64) 3172924.200 (0.12)
> > splash2x/barnes 1209066.000 1213125.400 (0.34) 1217261.400 (0.68) 1209661.600 (0.05) 921041.800 (-23.82)
> > splash2x/fft 9359313.200 9195213.000 (-1.75) 9377562.400 (0.19) 9050957.600 (-3.29) 9517977.000 (1.70)
> > splash2x/lu_cb 514966.200 522939.400 (1.55) 520870.400 (1.15) 522635.000 (1.49) 329933.600 (-35.93)
> > splash2x/lu_ncb 514180.400 525974.800 (2.29) 521420.200 (1.41) 521063.600 (1.34) 523557.000 (1.82)
> > splash2x/ocean_cp 3346493.400 3288078.000 (-1.75) 3382253.800 (1.07) 3289477.600 (-1.70) 3260810.400 (-2.56)
> > splash2x/ocean_ncp 3909966.400 3882968.800 (-0.69) 7037196.000 (79.98) 4046363.400 (3.49) 3471452.400 (-11.22)
> > splash2x/radiosity 1471119.400 1470626.800 (-0.03) 1482604.200 (0.78) 1472718.400 (0.11) 546893.600 (-62.82)
> > splash2x/radix 1748360.800 1729163.400 (-1.10) 1371463.200 (-21.56) 1701993.600 (-2.65) 1817519.600 (3.96)
> > splash2x/raytrace 46670.000 60172.200 (28.93) 51901.600 (11.21) 60782.600 (30.24) 52644.800 (12.80)
> > splash2x/volrend 150666.600 167444.200 (11.14) 151335.200 (0.44) 163345.000 (8.41) 162760.000 (8.03)
> > splash2x/water_nsquared 45720.200 59422.400 (29.97) 46031.000 (0.68) 61801.400 (35.17) 62627.000 (36.98)
> > splash2x/water_spatial 663052.200 672855.800 (1.48) 665787.600 (0.41) 674696.200 (1.76) 471052.600 (-28.96)
> > total 40077300.000 40108900.000 (0.08) 42997900.000 (7.29) 40032700.000 (-0.11) 37813000.000 (-5.65)
> >
> >
[...]
> >
> > Efficient THP
> > ~~~~~~~~~~~~~
> >
> > THP 'always' enabled policy achieves 5.57% speedup but incurs 7.29% memory
> > overhead. It achieves 41.62% speedup in best case, but 79.98% memory overhead
> > in worst case. Interestingly, both the best and worst case are with
> > 'splash2x/ocean_ncp').
>
> The results above don't seems to support this any more?
>
> > runtime orig rec (overhead) thp (overhead) ethp (overhead) prcl (overhead)
> > splash2x/ocean_ncp 86.927 87.065 (0.16) 50.747 (-41.62) 86.855 (-0.08) 199.553 (129.57)
Hmm... But, I don't get what point you meaning... In the data, column of 'thp'
means the THP 'always' enabled policy. And, the following column shows the
overhead of it compared to that of 'orig', in percent. Thus, the data says THP
'always' enabled policy enabled kernel consumes 50.747 seconds to finish
splash2x/ocean_ncp, while THP disabled original kernel consumes 86.927 seconds.
Thus, the overhead is ``(50.747 - 86.927) / 86.927 = -0.4162``. In other
words, 41.62% speedup.
Also, 5.57% speedup and 7.29% memory overhead is for _total_. This data shows
it.
> > runtime orig rec (overhead) thp (overhead) ethp (overhead) prcl (overhead)
> > total 3020.570 3028.080 (0.25) 2852.190 (-5.57) 2953.960 (-2.21) 3276.550 (8.47)
Maybe I made you confused by ambiguously saying this. Sorry if so. Or, if I'm
still misunderstanding your point, please let me know.
Thanks,
SeongJae Park
[...]
On Tue, 31 Mar 2020 18:18:19 +0200
SeongJae Park <[email protected]> wrote:
> On Tue, 31 Mar 2020 16:51:55 +0100 Jonathan Cameron <[email protected]> wrote:
>
> > On Mon, 30 Mar 2020 13:50:35 +0200
> > SeongJae Park <[email protected]> wrote:
> >
> > > From: SeongJae Park <[email protected]>
> > >
> > > DAMON[1] can be used as a primitive for data access awared memory management
> > > optimizations. That said, users who want such optimizations should run DAMON,
> > > read the monitoring results, analyze it, plan a new memory management scheme,
> > > and apply the new scheme by themselves. Such efforts will be inevitable for
> > > some complicated optimizations.
> > >
> > > However, in many other cases, the users could simply want the system to apply a
> > > memory management action to a memory region of a specific size having a
> > > specific access frequency for a specific time. For example, "page out a memory
> > > region larger than 100 MiB keeping only rare accesses more than 2 minutes", or
> > > "Do not use THP for a memory region larger than 2 MiB rarely accessed for more
> > > than 1 seconds".
> > >
> > > This RFC patchset makes DAMON to handle such data access monitoring-based
> > > operation schemes. With this change, users can do the data access awared
> > > optimizations by simply specifying their schemes to DAMON.
> >
> >
> > Hi SeongJae,
> >
> > I'm wondering if I'm misreading the results below or a data handling mixup
> > occured. See inline.
>
> Thank you for question, Jonathan!
>
> >
> > Thanks,
> >
> > Jonathan
> >
> > >
> [...]
> > > Results
> > > -------
> > >
> > > Below two tables show the measurement results. The runtimes are in seconds
> > > while the memory usages are in KiB. Each configurations except 'orig' shows
> > > its overhead relative to 'orig' in percent within parenthesises.
> > >
> > > runtime orig rec (overhead) thp (overhead) ethp (overhead) prcl (overhead)
> > > parsec3/blackscholes 107.594 107.956 (0.34) 106.750 (-0.78) 107.672 (0.07) 111.916 (4.02)
> > > parsec3/bodytrack 79.230 79.368 (0.17) 78.908 (-0.41) 79.705 (0.60) 80.423 (1.50)
> > > parsec3/canneal 142.831 143.810 (0.69) 123.530 (-13.51) 133.778 (-6.34) 144.998 (1.52)
> > > parsec3/dedup 11.986 11.959 (-0.23) 11.762 (-1.87) 12.028 (0.35) 13.313 (11.07)
> > > parsec3/facesim 210.125 209.007 (-0.53) 205.226 (-2.33) 207.766 (-1.12) 209.815 (-0.15)
> > > parsec3/ferret 191.601 191.177 (-0.22) 190.420 (-0.62) 191.775 (0.09) 192.638 (0.54)
> > > parsec3/fluidanimate 212.735 212.970 (0.11) 209.151 (-1.68) 211.904 (-0.39) 218.573 (2.74)
> > > parsec3/freqmine 291.225 290.873 (-0.12) 289.258 (-0.68) 289.884 (-0.46) 298.373 (2.45)
> > > parsec3/raytrace 118.289 119.586 (1.10) 119.045 (0.64) 119.064 (0.66) 137.919 (16.60)
> > > parsec3/streamcluster 323.565 328.168 (1.42) 279.565 (-13.60) 287.452 (-11.16) 333.244 (2.99)
> > > parsec3/swaptions 155.140 155.473 (0.21) 153.816 (-0.85) 156.423 (0.83) 156.237 (0.71)
> > > parsec3/vips 58.979 59.311 (0.56) 58.733 (-0.42) 59.005 (0.04) 61.062 (3.53)
> > > parsec3/x264 70.539 68.413 (-3.01) 64.760 (-8.19) 67.180 (-4.76) 68.103 (-3.45)
> > > splash2x/barnes 80.414 81.751 (1.66) 73.585 (-8.49) 80.232 (-0.23) 115.753 (43.95)
> > > splash2x/fft 33.902 34.111 (0.62) 24.228 (-28.53) 29.926 (-11.73) 44.438 (31.08)
> > > splash2x/lu_cb 85.556 86.001 (0.52) 84.538 (-1.19) 86.000 (0.52) 91.447 (6.89)
> > > splash2x/lu_ncb 93.399 93.652 (0.27) 90.463 (-3.14) 94.008 (0.65) 93.901 (0.54)
> > > splash2x/ocean_cp 45.253 45.191 (-0.14) 43.049 (-4.87) 44.022 (-2.72) 46.588 (2.95)
> > > splash2x/ocean_ncp 86.927 87.065 (0.16) 50.747 (-41.62) 86.855 (-0.08) 199.553 (129.57)
> > > splash2x/radiosity 91.433 91.511 (0.09) 90.626 (-0.88) 91.865 (0.47) 104.524 (14.32)
> > > splash2x/radix 31.923 32.023 (0.31) 25.194 (-21.08) 32.035 (0.35) 39.231 (22.89)
> > > splash2x/raytrace 84.367 84.677 (0.37) 82.417 (-2.31) 83.505 (-1.02) 84.857 (0.58)
> > > splash2x/volrend 87.499 87.495 (-0.00) 86.775 (-0.83) 87.311 (-0.21) 87.511 (0.01)
> > > splash2x/water_nsquared 236.397 236.759 (0.15) 219.902 (-6.98) 224.228 (-5.15) 238.562 (0.92)
> > > splash2x/water_spatial 89.646 89.767 (0.14) 89.735 (0.10) 90.347 (0.78) 103.585 (15.55)
> > > total 3020.570 3028.080 (0.25) 2852.190 (-5.57) 2953.960 (-2.21) 3276.550 (8.47)
> > >
> > >
> > > memused.avg orig rec (overhead) thp (overhead) ethp (overhead) prcl (overhead)
> > > parsec3/blackscholes 1785916.600 1834201.400 (2.70) 1826249.200 (2.26) 1828079.200 (2.36) 1712210.600 (-4.13)
> > > parsec3/bodytrack 1415049.400 1434317.600 (1.36) 1423715.000 (0.61) 1430392.600 (1.08) 1435136.000 (1.42)
> > > parsec3/canneal 1043489.800 1058617.600 (1.45) 1040484.600 (-0.29) 1048664.800 (0.50) 1050280.000 (0.65)
> > > parsec3/dedup 2414453.200 2458493.200 (1.82) 2411379.400 (-0.13) 2400516.000 (-0.58) 2461120.800 (1.93)
> > > parsec3/facesim 541597.200 550097.400 (1.57) 544364.600 (0.51) 553240.000 (2.15) 552316.400 (1.98)
> > > parsec3/ferret 317986.600 332346.000 (4.52) 320218.000 (0.70) 331085.000 (4.12) 330895.200 (4.06)
> > > parsec3/fluidanimate 576183.400 585442.000 (1.61) 577780.200 (0.28) 587703.400 (2.00) 506501.000 (-12.09)
> > > parsec3/freqmine 990869.200 997817.000 (0.70) 990350.400 (-0.05) 997669.000 (0.69) 763325.800 (-22.96)
> > > parsec3/raytrace 1748370.800 1757109.200 (0.50) 1746153.800 (-0.13) 1757830.400 (0.54) 1581455.800 (-9.55)
> > > parsec3/streamcluster 121521.800 140452.400 (15.58) 129725.400 (6.75) 132266.000 (8.84) 130558.200 (7.44)
> > > parsec3/swaptions 15592.400 29018.800 (86.11) 14765.800 (-5.30) 27260.200 (74.83) 26631.600 (70.80)
> > > parsec3/vips 2957567.600 2967993.800 (0.35) 2956623.200 (-0.03) 2973062.600 (0.52) 2951402.000 (-0.21)
> > > parsec3/x264 3169012.400 3175048.800 (0.19) 3190345.400 (0.67) 3189353.000 (0.64) 3172924.200 (0.12)
> > > splash2x/barnes 1209066.000 1213125.400 (0.34) 1217261.400 (0.68) 1209661.600 (0.05) 921041.800 (-23.82)
> > > splash2x/fft 9359313.200 9195213.000 (-1.75) 9377562.400 (0.19) 9050957.600 (-3.29) 9517977.000 (1.70)
> > > splash2x/lu_cb 514966.200 522939.400 (1.55) 520870.400 (1.15) 522635.000 (1.49) 329933.600 (-35.93)
> > > splash2x/lu_ncb 514180.400 525974.800 (2.29) 521420.200 (1.41) 521063.600 (1.34) 523557.000 (1.82)
> > > splash2x/ocean_cp 3346493.400 3288078.000 (-1.75) 3382253.800 (1.07) 3289477.600 (-1.70) 3260810.400 (-2.56)
> > > splash2x/ocean_ncp 3909966.400 3882968.800 (-0.69) 7037196.000 (79.98) 4046363.400 (3.49) 3471452.400 (-11.22)
> > > splash2x/radiosity 1471119.400 1470626.800 (-0.03) 1482604.200 (0.78) 1472718.400 (0.11) 546893.600 (-62.82)
> > > splash2x/radix 1748360.800 1729163.400 (-1.10) 1371463.200 (-21.56) 1701993.600 (-2.65) 1817519.600 (3.96)
> > > splash2x/raytrace 46670.000 60172.200 (28.93) 51901.600 (11.21) 60782.600 (30.24) 52644.800 (12.80)
> > > splash2x/volrend 150666.600 167444.200 (11.14) 151335.200 (0.44) 163345.000 (8.41) 162760.000 (8.03)
> > > splash2x/water_nsquared 45720.200 59422.400 (29.97) 46031.000 (0.68) 61801.400 (35.17) 62627.000 (36.98)
> > > splash2x/water_spatial 663052.200 672855.800 (1.48) 665787.600 (0.41) 674696.200 (1.76) 471052.600 (-28.96)
> > > total 40077300.000 40108900.000 (0.08) 42997900.000 (7.29) 40032700.000 (-0.11) 37813000.000 (-5.65)
> > >
> > >
> [...]
> > >
> > > Efficient THP
> > > ~~~~~~~~~~~~~
> > >
> > > THP 'always' enabled policy achieves 5.57% speedup but incurs 7.29% memory
> > > overhead. It achieves 41.62% speedup in best case, but 79.98% memory overhead
> > > in worst case. Interestingly, both the best and worst case are with
> > > 'splash2x/ocean_ncp').
> >
> > The results above don't seems to support this any more?
> >
> > > runtime orig rec (overhead) thp (overhead) ethp (overhead) prcl (overhead)
> > > splash2x/ocean_ncp 86.927 87.065 (0.16) 50.747 (-41.62) 86.855 (-0.08) 199.553 (129.57)
>
> Hmm... But, I don't get what point you meaning... In the data, column of 'thp'
> means the THP 'always' enabled policy. And, the following column shows the
> overhead of it compared to that of 'orig', in percent. Thus, the data says THP
> 'always' enabled policy enabled kernel consumes 50.747 seconds to finish
> splash2x/ocean_ncp, while THP disabled original kernel consumes 86.927 seconds.
ah. I got myself confused.
However, I was expecting to see a significant performance advantage
to ethp for this particular case as we did in the previous version.
In the previous version (you posted in reply to v6 of Damon), for ethp we had a significant gain with:
runtime orig rec (overhead) thp (overhead) ethp (overhead) prcl (overhead)
splash2x/ocean_ncp 81.360 81.434 (0.09) 51.157 (-37.12) 66.711 (-18.00) 91.611 (12.60)
So, in ethp we got roughly half the performance back (at the cost of some of the memory)
That was a result I have been trying to replicate, hence was at the front of my mind!
Any idea why that changed so much?
Thanks,
Jonathan
> Thus, the overhead is ``(50.747 - 86.927) / 86.927 = -0.4162``. In other
> words, 41.62% speedup.
>
> Also, 5.57% speedup and 7.29% memory overhead is for _total_. This data shows
> it.
>
> > > runtime orig rec (overhead) thp (overhead) ethp (overhead) prcl (overhead)
> > > total 3020.570 3028.080 (0.25) 2852.190 (-5.57) 2953.960 (-2.21) 3276.550 (8.47)
>
> Maybe I made you confused by ambiguously saying this. Sorry if so. Or, if I'm
> still misunderstanding your point, please let me know.
>
>
> Thanks,
> SeongJae Park
>
> [...]
On Tue, 31 Mar 2020 17:39:08 +0100 Jonathan Cameron <[email protected]> wrote:
> On Tue, 31 Mar 2020 18:18:19 +0200
> SeongJae Park <[email protected]> wrote:
>
> > On Tue, 31 Mar 2020 16:51:55 +0100 Jonathan Cameron <[email protected]> wrote:
> >
> > > On Mon, 30 Mar 2020 13:50:35 +0200
> > > SeongJae Park <[email protected]> wrote:
> > >
> > > > From: SeongJae Park <[email protected]>
> > > >
> > > > DAMON[1] can be used as a primitive for data access awared memory management
> > > > optimizations. That said, users who want such optimizations should run DAMON,
> > > > read the monitoring results, analyze it, plan a new memory management scheme,
> > > > and apply the new scheme by themselves. Such efforts will be inevitable for
> > > > some complicated optimizations.
> > > >
> > > > However, in many other cases, the users could simply want the system to apply a
> > > > memory management action to a memory region of a specific size having a
> > > > specific access frequency for a specific time. For example, "page out a memory
> > > > region larger than 100 MiB keeping only rare accesses more than 2 minutes", or
> > > > "Do not use THP for a memory region larger than 2 MiB rarely accessed for more
> > > > than 1 seconds".
> > > >
> > > > This RFC patchset makes DAMON to handle such data access monitoring-based
> > > > operation schemes. With this change, users can do the data access awared
> > > > optimizations by simply specifying their schemes to DAMON.
[...]
> > > >
> > > > Efficient THP
> > > > ~~~~~~~~~~~~~
> > > >
> > > > THP 'always' enabled policy achieves 5.57% speedup but incurs 7.29% memory
> > > > overhead. It achieves 41.62% speedup in best case, but 79.98% memory overhead
> > > > in worst case. Interestingly, both the best and worst case are with
> > > > 'splash2x/ocean_ncp').
> > >
> > > The results above don't seems to support this any more?
> > >
> > > > runtime orig rec (overhead) thp (overhead) ethp (overhead) prcl (overhead)
> > > > splash2x/ocean_ncp 86.927 87.065 (0.16) 50.747 (-41.62) 86.855 (-0.08) 199.553 (129.57)
> >
> > Hmm... But, I don't get what point you meaning... In the data, column of 'thp'
> > means the THP 'always' enabled policy. And, the following column shows the
> > overhead of it compared to that of 'orig', in percent. Thus, the data says THP
> > 'always' enabled policy enabled kernel consumes 50.747 seconds to finish
> > splash2x/ocean_ncp, while THP disabled original kernel consumes 86.927 seconds.
>
> ah. I got myself confused.
>
> However, I was expecting to see a significant performance advantage
> to ethp for this particular case as we did in the previous version.
>
> In the previous version (you posted in reply to v6 of Damon), for ethp we had a significant gain with:
>
> runtime orig rec (overhead) thp (overhead) ethp (overhead) prcl (overhead)
> splash2x/ocean_ncp 81.360 81.434 (0.09) 51.157 (-37.12) 66.711 (-18.00) 91.611 (12.60)
>
> So, in ethp we got roughly half the performance back (at the cost of some of the memory)
>
> That was a result I have been trying to replicate, hence was at the front of my mind!
>
> Any idea why that changed so much?
Ah, I forgot to explain about this change. Thank you for let me know.
Overall, ETHP in DAMON-based Operations Schemes RFC v5 shows worse peak
performance gains.
For example, splash2x/fft shows best case speedup with ETHP for both this
version and previous version. The speedup changed from 19% to 12%. In case of
splash2x/ocean_ncp, it changed from 18% to only 0.08%.
That said, total performance speedup is improved. It was 1.83% before, and
2.21% now. Also, there are several workloads showing better speedup. In case
of parsec3/canneal, the speedup changed from 3.86% to 6.34%.
Also note that ETHP's memory savings for the workloads showing less speedup are
much improved. For example, the memory overhead of ETHP for splash2x/ocean_ncp
was 24.4% before, but only 3.5% now.
This is due to the fact that I didn't update the schemes for the updated DAMON.
Basically, the effect of the schemes are access pattern dependent. In this
case, because DAMON has changed so that it might report access pattern that
different from that of previous version, the schemes should also be modified to
make best performance. However, I didn't update the schemes because those are
for only proof of the concepts, not for productions.
The change of report was not so huge, fortunately. I also confirmed this with
my human eyes by comparing the visualized access patterns of the two version.
The overall trend (better performance, less memory overhead) also changed only
subtle. However, some individual workloads got some remarkable changes.
Thanks,
SeongJae Park
>
> Thanks,
>
> Jonathan
>
>
> > Thus, the overhead is ``(50.747 - 86.927) / 86.927 = -0.4162``. In other
> > words, 41.62% speedup.
> >
> > Also, 5.57% speedup and 7.29% memory overhead is for _total_. This data shows
> > it.
> >
> > > > runtime orig rec (overhead) thp (overhead) ethp (overhead) prcl (overhead)
> > > > total 3020.570 3028.080 (0.25) 2852.190 (-5.57) 2953.960 (-2.21) 3276.550 (8.47)
> >
> > Maybe I made you confused by ambiguously saying this. Sorry if so. Or, if I'm
> > still misunderstanding your point, please let me know.
>
>
> >
> >
> > Thanks,
> > SeongJae Park
> >
> > [...]
>