[PATCH v12 00/16] Introduce Data Access MONitor (DAMON)

From: SeongJae Park
Date: Mon May 18 2020 - 06:01:28 EST

From: SeongJae Park <sjpark@xxxxxxxxx>

Introduction
============

DAMON is a data access monitoring framework subsystem for the Linux kernel.
The core mechanisms of DAMON called 'region based sampling' and adaptive
regions adjustment' (refer to :doc:`mechanisms` for the detail) make it
accurate, efficient, and scalable. Using this framework, therefore, the
kernel's core memory management mechanisms including reclamation and THP can be
optimized for better memory management. The memory management optimization
works that have not merged into the mainline due to their high data access
monitoring overhead will be able to have another try. In user space,
meanwhile, users who have some special workloads will be able to write
personalized tools or applications for more understanding and specialized
optimizations of their systems using the DAMON as a framework.

More Information
================

We prepared a showcase web site[1] that you can get more information of DAMON.
There are

- the official documentation of DAMON[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].

[1] https://damonitor.github.io
[2] https://damonitor.github.io/doc/html/latest
[3] https://damonitor.github.io/test/result/visual/latest/heatmap.0.html
[4] https://damonitor.github.io/test/result/visual/latest/heatmap.1.html
[5] https://damonitor.github.io/test/result/visual/latest/heatmap.2.html
[6] https://damonitor.github.io/test/result/visual/latest/wss_sz.html
[7] https://damonitor.github.io/test/result/visual/latest/wss_time.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html

Evaluations
===========

We evaluated DAMON's overhead, monitoring quality and usefulness using 25
realistic workloads on my QEMU/KVM based virtual machine.

DAMON is lightweight. It consumes only 0.03% more system memory and up to 1%
CPU time. It makes target worloads only 0.7% slower.

DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP removes 63.12% of THP memory
overheads while preserving 49.15% of THP speedup. Another experimental
DAMON-based 'proactive reclamation' implementation reduces 85.85% of
residentail sets and 21.98% of system memory footprint while incurring only
2.42% runtime overhead in best case (parsec3/freqmine).

NOTE that the experimentail THP optimization and proactive reclamation are not
for production, just only for proof of concepts.

Please refer to 'Appendix C' for detailed evaluation setup and results.

Baseline and Complete Git Trees
===============================

The patches are based on the v5.6. You can also clone the complete git
tree:

$ git clone git://github.com/sjp38/linux -b damon/patches/v12

The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v12

There are a couple of trees for entire DAMON patchset series. The first one[1]
contains the changes for latest release, while the other one[2] contains the
changes for next release.

[1] https://github.com/sjp38/linux/tree/damon/master
[2] https://github.com/sjp38/linux/tree/damon/next

Sequence Of Patches
===================

The patches are organized in the following sequence. The first two patches are
preparation of DAMON patchset. The 1st patch adds typos found in previous
versions of DAMON patchset to 'scripts/spelling.txt' so that the typos can be
caught by 'checkpatch.pl'. The 2nd patch exports 'lookup_page_ext()' to GPL
modules so that it can be used by DAMON even though it is built as a loadable
module.

Next five patches implement the core of DAMON and it's programming interface.
The 3rd patch introduces DAMON module, it's data structures, and data structure
related common functions. Following four patches (4nd to 7th) implements the
core mechanisms of DAMON, namely regions based sampling (patch 4), adaptive
regions adjustment (patches 5-6), and dynamic memory mapping chage adoption
(patch 7).

Following four patches are for low level users of DAMON. The 8th patch
implements callbacks for each of monitoring steps so that users can do whatever
they want with the access patterns. The 9th one implements recording of access
patterns in DAMON for better convenience and efficiency. Each of next two
patches (10th and 11th) respectively adds a debugfs interface for privileged
people and/or programs in user space, and a tracepoint for other tracepoints
supporting tracers such as perf.

Two patches for high level users of DAMON follows. To provide a minimal
reference to the debugfs interface and for high level use/tests of the DAMON,
the next patch (12th) implements an user space tool. The 13th patch adds a
document for administrators of DAMON.

Next two patches are for tests. The 14th and 15th patches provide unit tests
(based on kunit) and user space tests (based on kselftest), respectively.

Finally, the last patch (16th) updates the MAINTAINERS file.

Patch History
=============

Below are main changes for recent 5 versions of this patchset. Please refer to
oldest patchset listed here to get older history.

Changes from v11
(https://lore.kernel.org/linux-mm/20200511123302.12520-1-sjpark@xxxxxxxxxx/)
- Rewrite the document (Stefan Nuernberger)
- Make 'damon_for_each_*' argument order consistent (Leonard Foerster)
- Fix wrong comment in 'kdamond_merge_regions()' (Leonard Foerster)

Changes from v10
(https://lore.kernel.org/linux-mm/20200505110815.10532-1-sjpark@xxxxxxxxxx/)
- Reduce aggressive split overhead by doing it only if required

Changes from v9
(https://lore.kernel.org/linux-mm/20200427120442.24179-1-sjpark@xxxxxxxxxx/)
- Split each region into 4 subregions if possible (Jonathan Cameraon)
- Update kunit test for the split code change

Changes from v8
(https://lore.kernel.org/linux-mm/20200406130938.14066-1-sjpark@xxxxxxxxxx/)
- Make regions always aligned by minimal region size that can be changed
(Stefan Nuernberger)
- Store binary format version in the recording file (Stefan Nuernberger)
- Use 'int' for pid instead of 'unsigned long' (Stefan Nuernberger)
- Fix a race condition in damon thread termination (Stefan Nuernberger)
- Optimize random value generation and recording (Stefan Nuernberger)
- Clean up commit messages and comments (Stefan Nuernberger)
- Clean up code (Stefan Nuernberger)
- Use explicit signalling and 'do_exit()' for damon thread termination
- Add more typos to spelling.txt
- Update the performance evaluation results
- Describe future plans in the cover letter

Changes from v7
(https://lore.kernel.org/linux-mm/20200318112722.30143-1-sjpark@xxxxxxxxxx/)
- Cleanup variable names (Jonathan Cameron)
- Split sampling address setup from access_check() (Jonathan Cameron)
- Make sampling address to always locate in the region (Jonathan Cameron)
- Make initial region's sampling addr to be old (Jonathan Cameron)
- Split kdamond on/off function to seperate functions (Jonathan Cameron)
- Fix wrong kernel doc comments (Jonathan Cameron)
- Reset 'last_accessed' to false in kdamond_check_access() if necessary
- Rebase on v5.6

SeongJae Park (16):
scripts/spelling: Add a few more typos
mm/page_ext: Export lookup_page_ext() to GPL modules
mm: Introduce Data Access MONitor (DAMON)
mm/damon: Implement region based sampling
mm/damon: Adaptively adjust regions
mm/damon: Split regions into 3 subregions if necessary
mm/damon: Apply dynamic memory mapping changes
mm/damon: Implement callbacks
mm/damon: Implement access pattern recording
mm/damon: Add debugfs interface
mm/damon: Add tracepoints
tools: Add a minimal user-space tool for DAMON
Documentation/admin-guide/mm: Add a document for DAMON
mm/damon: Add kunit tests
mm/damon: Add user space selftests
MAINTAINERS: Update for DAMON

Documentation/admin-guide/mm/damon/api.rst | 9 +
.../admin-guide/mm/damon/damon_heatmap.png | Bin 0 -> 8366 bytes
.../admin-guide/mm/damon/damon_wss_change.png | Bin 0 -> 7211 bytes
.../admin-guide/mm/damon/damon_wss_dist.png | Bin 0 -> 6173 bytes
Documentation/admin-guide/mm/damon/faq.rst | 39 +
.../admin-guide/mm/damon/freqmine_heatmap.png | Bin 0 -> 8687 bytes
.../admin-guide/mm/damon/freqmine_wss_sz.png | Bin 0 -> 4986 bytes
.../mm/damon/freqmine_wss_time.png | Bin 0 -> 6283 bytes
Documentation/admin-guide/mm/damon/guide.rst | 196 +++
Documentation/admin-guide/mm/damon/index.rst | 50 +
.../admin-guide/mm/damon/mechanisms.rst | 111 ++
Documentation/admin-guide/mm/damon/plans.rst | 49 +
Documentation/admin-guide/mm/damon/start.rst | 119 ++
.../mm/damon/streamcluster_heatmap.png | Bin 0 -> 37916 bytes
.../mm/damon/streamcluster_wss_sz.png | Bin 0 -> 5522 bytes
.../mm/damon/streamcluster_wss_time.png | Bin 0 -> 6322 bytes
Documentation/admin-guide/mm/damon/usage.rst | 305 ++++
Documentation/admin-guide/mm/index.rst | 1 +
Documentation/index.rst | 176 +-
MAINTAINERS | 12 +
include/linux/damon.h | 78 +
include/trace/events/damon.h | 43 +
mm/Kconfig | 23 +
mm/Makefile | 1 +
mm/damon-test.h | 622 +++++++
mm/damon.c | 1511 +++++++++++++++++
mm/page_ext.c | 1 +
scripts/spelling.txt | 8 +
tools/damon/.gitignore | 1 +
tools/damon/_dist.py | 36 +
tools/damon/_recfile.py | 23 +
tools/damon/bin2txt.py | 67 +
tools/damon/damo | 37 +
tools/damon/heats.py | 362 ++++
tools/damon/nr_regions.py | 91 +
tools/damon/record.py | 212 +++
tools/damon/report.py | 45 +
tools/damon/wss.py | 97 ++
tools/testing/selftests/damon/Makefile | 7 +
.../selftests/damon/_chk_dependency.sh | 28 +
tools/testing/selftests/damon/_chk_record.py | 108 ++
.../testing/selftests/damon/debugfs_attrs.sh | 139 ++
.../testing/selftests/damon/debugfs_record.sh | 50 +
43 files changed, 4489 insertions(+), 168 deletions(-)
create mode 100644 Documentation/admin-guide/mm/damon/api.rst
create mode 100644 Documentation/admin-guide/mm/damon/damon_heatmap.png
create mode 100644 Documentation/admin-guide/mm/damon/damon_wss_change.png
create mode 100644 Documentation/admin-guide/mm/damon/damon_wss_dist.png
create mode 100644 Documentation/admin-guide/mm/damon/faq.rst
create mode 100644 Documentation/admin-guide/mm/damon/freqmine_heatmap.png
create mode 100644 Documentation/admin-guide/mm/damon/freqmine_wss_sz.png
create mode 100644 Documentation/admin-guide/mm/damon/freqmine_wss_time.png
create mode 100644 Documentation/admin-guide/mm/damon/guide.rst
create mode 100644 Documentation/admin-guide/mm/damon/index.rst
create mode 100644 Documentation/admin-guide/mm/damon/mechanisms.rst
create mode 100644 Documentation/admin-guide/mm/damon/plans.rst
create mode 100644 Documentation/admin-guide/mm/damon/start.rst
create mode 100644 Documentation/admin-guide/mm/damon/streamcluster_heatmap.png
create mode 100644 Documentation/admin-guide/mm/damon/streamcluster_wss_sz.png
create mode 100644 Documentation/admin-guide/mm/damon/streamcluster_wss_time.png
create mode 100644 Documentation/admin-guide/mm/damon/usage.rst
create mode 100644 include/linux/damon.h
create mode 100644 include/trace/events/damon.h
create mode 100644 mm/damon-test.h
create mode 100644 mm/damon.c
create mode 100644 tools/damon/.gitignore
create mode 100644 tools/damon/_dist.py
create mode 100644 tools/damon/_recfile.py
create mode 100644 tools/damon/bin2txt.py
create mode 100755 tools/damon/damo
create mode 100644 tools/damon/heats.py
create mode 100644 tools/damon/nr_regions.py
create mode 100644 tools/damon/record.py
create mode 100644 tools/damon/report.py
create mode 100644 tools/damon/wss.py
create mode 100644 tools/testing/selftests/damon/Makefile
create mode 100644 tools/testing/selftests/damon/_chk_dependency.sh
create mode 100644 tools/testing/selftests/damon/_chk_record.py
create mode 100755 tools/testing/selftests/damon/debugfs_attrs.sh
create mode 100755 tools/testing/selftests/damon/debugfs_record.sh

--
2.17.1

================================== >8 =========================================
Appendix A: Related Works
=========================

There are a number of researches[1,2,3,4,5,6] optimizing memory management
mechanisms based on the actual memory access patterns that shows impressive
results. However, most of those has no deep consideration about the monitoring
of the accesses itself. Some of those focused on the overhead of the
monitoring, but does not consider the accuracy scalability[6] or has additional
dependencies[7]. Indeed, one recent research[5] about the proactive
reclamation has also proposed[8] to the kernel community but the monitoring
overhead was considered a main problem.

[1] Subramanya R Dulloor, Amitabha Roy, Zheguang Zhao, Narayanan Sundaram,
Nadathur Satish, Rajesh Sankaran, Jeff Jackson, and Karsten Schwan. 2016.
Data tiering in heterogeneous memory systems. In Proceedings of the 11th
European Conference on Computer Systems (EuroSys). ACM, 15.
[2] Youngjin Kwon, Hangchen Yu, Simon Peter, Christopher J Rossbach, and Emmett
Witchel. 2016. Coordinated and efficient huge page management with ingens.
In 12th USENIX Symposium on Operating Systems Design and Implementation
(OSDI). 705â721.
[3] Harald Servat, Antonio J PeÃa, GermÃn Llort, Estanislao Mercadal,
HansChristian Hoppe, and JesÃs Labarta. 2017. Automating the application
data placement in hybrid memory systems. In 2017 IEEE International
Conference on Cluster Computing (CLUSTER). IEEE, 126â136.
[4] Vlad Nitu, Boris Teabe, Alain Tchana, Canturk Isci, and Daniel Hagimont.
2018. Welcome to zombieland: practical and energy-efficient memory
disaggregation in a datacenter. In Proceedings of the 13th European
Conference on Computer Systems (EuroSys). ACM, 16.
[5] Andres Lagar-Cavilla, Junwhan Ahn, Suleiman Souhlal, Neha Agarwal, Radoslaw
Burny, Shakeel Butt, Jichuan Chang, Ashwin Chaugule, Nan Deng, Junaid
Shahid, Greg Thelen, Kamil Adam Yurtsever, Yu Zhao, and Parthasarathy
Ranganathan. 2019. Software-Defined Far Memory in Warehouse-Scale
Computers. In Proceedings of the 24th International Conference on
Architectural Support for Programming Languages and Operating Systems
(ASPLOS). ACM, New York, NY, USA, 317â330.
DOI:https://doi.org/10.1145/3297858.3304053
[6] Carl Waldspurger, Trausti Saemundsson, Irfan Ahmad, and Nohhyun Park.
2017. Cache Modeling and Optimization using Miniature Simulations. In 2017
USENIX Annual Technical Conference (ATC). USENIX Association, Santa
Clara, CA, 487â498.
https://www.usenix.org/conference/atc17/technical-sessions/
[7] Haojie Wang, Jidong Zhai, Xiongchao Tang, Bowen Yu, Xiaosong Ma, and
Wenguang Chen. 2018. Spindle: Informed Memory Access Monitoring. In 2018
USENIX Annual Technical Conference (ATC). USENIX Association, Boston, MA,
561â574. https://www.usenix.org/conference/atc18/presentation/wang-haojie
[8] Jonathan Corbet. 2019. Proactively reclaiming idle memory. (2019).
https://lwn.net/Articles/787611/.


Appendix B: Limitations of Other Access Monitoring Techniques
=============================================================

The memory access instrumentation techniques which are applied to
many tools such as Intel PIN is essential for correctness required cases such
as memory access bug detections or cache level optimizations. However, those
usually incur exceptionally high overhead which is unacceptable.

Periodic access checks based on access counting features (e.g., PTE Accessed
bits or PG_Idle flags) can reduce the overhead. It sacrifies some of the
quality but it's still ok to many of this domain. However, the overhead
arbitrarily increase as the size of the target workload grows. Miniature-like
static region based sampling can set the upperbound of the overhead, but it
will now decrease the quality of the output as the size of the workload grows.

DAMON is another solution that overcomes the limitations. It is 1) accurate
enough for this domain, 2) light-weight so that it can be applied online, and
3) allow users to set the upper-bound of the overhead, regardless of the size
of target workloads. It is implemented as a simple and small kernel module to
support various users in both of the user space and the kernel space. Refer to
'Evaluations' section below for detailed performance of DAMON.

For the goals, DAMON utilizes its two core mechanisms, which allows lightweight
overhead and high quality of output, repectively. To show how DAMON promises
those, refer to 'Mechanisms of DAMON' section below.


Appendix C: 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 500ms 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/20200303121406.20954-1-sjpark@xxxxxxxxxx/
[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 107.065 107.478 (0.39) 106.682 (-0.36) 107.365 (0.28) 111.811 (4.43)
parsec3/bodytrack 79.256 79.450 (0.25) 78.645 (-0.77) 79.314 (0.07) 80.305 (1.32)
parsec3/canneal 139.497 141.181 (1.21) 121.526 (-12.88) 130.074 (-6.75) 154.644 (10.86)
parsec3/dedup 11.879 11.873 (-0.05) 11.693 (-1.56) 11.948 (0.58) 12.694 (6.86)
parsec3/facesim 207.814 208.467 (0.31) 203.743 (-1.96) 206.759 (-0.51) 214.603 (3.27)
parsec3/ferret 190.124 190.955 (0.44) 189.575 (-0.29) 190.852 (0.38) 191.548 (0.75)
parsec3/fluidanimate 211.046 212.282 (0.59) 208.832 (-1.05) 212.143 (0.52) 218.774 (3.66)
parsec3/freqmine 289.259 290.096 (0.29) 288.510 (-0.26) 290.177 (0.32) 296.269 (2.42)
parsec3/raytrace 118.522 119.701 (0.99) 119.469 (0.80) 118.964 (0.37) 130.584 (10.18)
parsec3/streamcluster 323.619 327.830 (1.30) 283.374 (-12.44) 287.837 (-11.06) 330.216 (2.04)
parsec3/swaptions 154.007 155.714 (1.11) 154.767 (0.49) 154.955 (0.62) 155.256 (0.81)
parsec3/vips 58.822 58.750 (-0.12) 58.564 (-0.44) 58.807 (-0.02) 60.320 (2.55)
parsec3/x264 67.335 72.516 (7.69) 64.680 (-3.94) 70.096 (4.10) 72.465 (7.62)
splash2x/barnes 80.335 80.979 (0.80) 73.758 (-8.19) 78.874 (-1.82) 99.226 (23.52)
splash2x/fft 33.441 33.312 (-0.38) 22.909 (-31.49) 31.561 (-5.62) 41.496 (24.09)
splash2x/lu_cb 85.691 85.706 (0.02) 84.352 (-1.56) 85.943 (0.29) 88.914 (3.76)
splash2x/lu_ncb 92.338 92.749 (0.45) 89.773 (-2.78) 92.888 (0.60) 94.104 (1.91)
splash2x/ocean_cp 44.542 44.795 (0.57) 42.958 (-3.56) 44.061 (-1.08) 49.091 (10.21)
splash2x/ocean_ncp 82.101 82.006 (-0.12) 51.418 (-37.37) 64.496 (-21.44) 105.998 (29.11)
splash2x/radiosity 91.296 91.353 (0.06) 90.668 (-0.69) 91.379 (0.09) 103.265 (13.11)
splash2x/radix 31.243 31.417 (0.56) 25.176 (-19.42) 30.297 (-3.03) 38.474 (23.14)
splash2x/raytrace 84.405 84.863 (0.54) 83.498 (-1.08) 83.637 (-0.91) 85.166 (0.90)
splash2x/volrend 87.516 88.156 (0.73) 86.311 (-1.38) 87.016 (-0.57) 88.318 (0.92)
splash2x/water_nsquared 233.515 233.826 (0.13) 221.169 (-5.29) 224.430 (-3.89) 236.929 (1.46)
splash2x/water_spatial 89.207 89.448 (0.27) 89.396 (0.21) 89.826 (0.69) 97.700 (9.52)
total 2993.890 3014.920 (0.70) 2851.460 (-4.76) 2923.710 (-2.34) 3158.180 (5.49)


memused.avg orig rec (overhead) thp (overhead) ethp (overhead) prcl (overhead)
parsec3/blackscholes 1819997.200 1832626.000 (0.69) 1821707.000 (0.09) 1830010.400 (0.55) 1651016.200 (-9.28)
parsec3/bodytrack 1416437.600 1430462.200 (0.99) 1420736.400 (0.30) 1428355.600 (0.84) 1430327.000 (0.98)
parsec3/canneal 1040414.400 1050736.800 (0.99) 1041515.600 (0.11) 1048562.200 (0.78) 1049049.400 (0.83)
parsec3/dedup 2414431.800 2454260.400 (1.65) 2423175.400 (0.36) 2396560.200 (-0.74) 2379898.200 (-1.43)
parsec3/facesim 540432.200 551410.200 (2.03) 545978.200 (1.03) 558558.400 (3.35) 483755.400 (-10.49)
parsec3/ferret 318728.600 333971.800 (4.78) 322158.200 (1.08) 332889.200 (4.44) 327896.400 (2.88)
parsec3/fluidanimate 576917.800 585126.600 (1.42) 575123.200 (-0.31) 585429.200 (1.48) 484810.600 (-15.97)
parsec3/freqmine 987882.200 997030.600 (0.93) 990429.200 (0.26) 998484.000 (1.07) 770740.200 (-21.98)
parsec3/raytrace 1747059.800 1752904.000 (0.33) 1738853.600 (-0.47) 1753948.600 (0.39) 1578118.000 (-9.67)
parsec3/streamcluster 121857.600 133934.400 (9.91) 121777.800 (-0.07) 133145.800 (9.26) 131512.800 (7.92)
parsec3/swaptions 14123.000 29254.400 (107.14) 14017.200 (-0.75) 26470.600 (87.43) 28429.800 (101.30)
parsec3/vips 2957631.800 2972884.400 (0.52) 2938855.400 (-0.63) 2960746.000 (0.11) 2946850.800 (-0.36)
parsec3/x264 3184777.200 3214527.400 (0.93) 3177061.000 (-0.24) 3192446.600 (0.24) 3185851.800 (0.03)
splash2x/barnes 1209737.200 1214763.200 (0.42) 1242138.400 (2.68) 1215857.600 (0.51) 994280.800 (-17.81)
splash2x/fft 9362799.400 9178844.600 (-1.96) 9264052.600 (-1.05) 9164996.600 (-2.11) 9452048.200 (0.95)
splash2x/lu_cb 515716.000 524071.600 (1.62) 521226.200 (1.07) 524261.400 (1.66) 372910.200 (-27.69)
splash2x/lu_ncb 512898.200 523057.600 (1.98) 520630.800 (1.51) 523779.000 (2.12) 446282.400 (-12.99)
splash2x/ocean_cp 3346038.000 3288703.600 (-1.71) 3386906.600 (1.22) 3330937.200 (-0.45) 3266442.400 (-2.38)
splash2x/ocean_ncp 3886945.600 3871894.000 (-0.39) 7066192.000 (81.79) 5065229.800 (30.31) 3652078.200 (-6.04)
splash2x/radiosity 1467107.200 1468850.800 (0.12) 1481292.600 (0.97) 1470335.800 (0.22) 530923.400 (-63.81)
splash2x/radix 1708330.800 1699792.200 (-0.50) 1352708.600 (-20.82) 1601339.200 (-6.26) 2043947.800 (19.65)
splash2x/raytrace 44817.200 59047.800 (31.75) 52010.200 (16.05) 60407.200 (34.79) 53916.400 (20.30)
splash2x/volrend 151534.200 167791.400 (10.73) 151759.000 (0.15) 165012.400 (8.89) 160864.600 (6.16)
splash2x/water_nsquared 46549.400 61846.800 (32.86) 51741.200 (11.15) 59214.400 (27.21) 91869.400 (97.36)
splash2x/water_spatial 669085.200 675929.200 (1.02) 665924.600 (-0.47) 676218.200 (1.07) 538430.200 (-19.53)
total 40062200.000 40073700.000 (0.03) 42888000.000 (7.05) 41103100.000 (2.60) 38052200.000 (-5.02)


DAMON Overheads
~~~~~~~~~~~~~~~

In total, DAMON recording feature incurs 0.70% runtime overhead and 0.03%
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. 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 4.76% speedup but incurs 7.05% memory
overhead. It achieves 37.37% speedup in best case, but 81.79% 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 2.34% speedup and 2.60% memory overhead. In other words, 'ethp' removes
63.12% of THP memory waste while preserving 49.15% of THP speedup in total. In
case of the 'splash2x/ocean_ncp', 'ethp' removes 62.94% of THP memory waste
while preserving 57.37% of THP speedup.


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
8.41% runtime overhead in total while achieving 5.83% 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 591452.000 591466.400 (0.00) 593145.200 (0.29) 590609.400 (-0.14) 324379.000 (-45.16)
parsec3/bodytrack 32458.600 32352.200 (-0.33) 32218.400 (-0.74) 32376.400 (-0.25) 27186.000 (-16.24)
parsec3/canneal 841311.600 839888.400 (-0.17) 837008.400 (-0.51) 837811.000 (-0.42) 823276.200 (-2.14)
parsec3/dedup 1219096.600 1228038.800 (0.73) 1235610.800 (1.35) 1214267.000 (-0.40) 992031.000 (-18.63)
parsec3/facesim 311322.200 311574.400 (0.08) 316277.000 (1.59) 312593.800 (0.41) 188789.400 (-39.36)
parsec3/ferret 99536.600 99556.800 (0.02) 102366.000 (2.84) 99799.000 (0.26) 88392.000 (-11.20)
parsec3/fluidanimate 531893.600 531856.000 (-0.01) 532143.400 (0.05) 532190.200 (0.06) 421798.800 (-20.70)
parsec3/freqmine 553533.200 552730.400 (-0.15) 555642.600 (0.38) 553895.400 (0.07) 78335.000 (-85.85)
parsec3/raytrace 894094.200 894849.000 (0.08) 889964.000 (-0.46) 892865.000 (-0.14) 332911.800 (-62.77)
parsec3/streamcluster 110938.000 110968.200 (0.03) 111673.400 (0.66) 111312.200 (0.34) 109911.200 (-0.93)
parsec3/swaptions 5630.000 5634.800 (0.09) 5656.600 (0.47) 5692.000 (1.10) 4028.400 (-28.45)
parsec3/vips 32107.000 32045.200 (-0.19) 32207.800 (0.31) 32293.800 (0.58) 29093.600 (-9.39)
parsec3/x264 81926.000 82143.000 (0.26) 83258.400 (1.63) 82570.600 (0.79) 80651.800 (-1.56)
splash2x/barnes 1215468.800 1217889.800 (0.20) 1222006.800 (0.54) 1217425.600 (0.16) 752405.200 (-38.10)
splash2x/fft 9584734.800 9568872.800 (-0.17) 9660321.400 (0.79) 9646012.000 (0.64) 8367492.800 (-12.70)
splash2x/lu_cb 510555.400 510807.400 (0.05) 514448.600 (0.76) 509281.800 (-0.25) 349272.200 (-31.59)
splash2x/lu_ncb 510310.000 508915.600 (-0.27) 513886.000 (0.70) 510288.400 (-0.00) 431521.800 (-15.44)
splash2x/ocean_cp 3408724.400 3408424.600 (-0.01) 3446054.400 (1.10) 3419536.200 (0.32) 3173818.600 (-6.89)
splash2x/ocean_ncp 3923539.600 3922605.400 (-0.02) 7175526.600 (82.88) 5152558.800 (31.32) 3475756.000 (-11.41)
splash2x/radiosity 1476050.000 1475470.400 (-0.04) 1485747.000 (0.66) 1476232.600 (0.01) 269512.200 (-81.74)
splash2x/radix 1756385.400 1752676.000 (-0.21) 1431621.600 (-18.49) 1711460.800 (-2.56) 1923448.200 (9.51)
splash2x/raytrace 23286.400 23311.200 (0.11) 28440.800 (22.13) 26977.200 (15.85) 15685.200 (-32.64)
splash2x/volrend 44089.400 44125.600 (0.08) 44436.600 (0.79) 44250.400 (0.37) 27616.800 (-37.36)
splash2x/water_nsquared 29437.600 29403.200 (-0.12) 29817.400 (1.29) 30040.000 (2.05) 25369.600 (-13.82)
splash2x/water_spatial 656264.400 656566.400 (0.05) 656016.400 (-0.04) 656420.200 (0.02) 474480.400 (-27.70)
total 28444100.000 28432200.000 (-0.04) 31535300.000 (10.87) 29698900.000 (4.41) 22787200.000 (-19.89)

In total, 19.89% of residential sets were reduced.

With parsec3/freqmine, 'prcl' reduced 85.85% of residential sets and 21.98% of
system memory usage while incurring only 2.42% runtime overhead.


Appendix D: Prototype Evaluations
=================================

A prototype of DAMON has evaluated on an Intel Xeon E7-8837 machine using 20
benchmarks that picked from SPEC CPU 2006, NAS, Tensorflow Benchmark,
SPLASH-2X, and PARSEC 3 benchmark suite. Nonethless, this section provides
only summary of the results. For more detail, please refer to the slides used
for the introduction of DAMON at the Linux Plumbers Conference 2019[1] or the
MIDDLEWARE'19 industrial track paper[2].

[1] SeongJae Park, Tracing Data Access Pattern with Bounded Overhead and
Best-effort Accuracy. In The Linux Kernel Summit, September 2019.
https://linuxplumbersconf.org/event/4/contributions/548/
[2] SeongJae Park, Yunjae Lee, Heon Y. Yeom, Profiling Dynamic Data Access
Patterns with Controlled Overhead and Quality. In 20th ACM/IFIP
International Middleware Conference Industry, December 2019.
https://dl.acm.org/doi/10.1145/3366626.3368125


Quality
-------

We first traced and visualized the data access pattern of each workload. We
were able to confirm that the visualized results are reasonably accurate by
manually comparing those with the source code of the workloads.

To see the usefulness of the monitoring, we optimized 9 memory intensive
workloads among them for memory pressure situations using the DAMON outputs.
In detail, we identified frequently accessed memory regions in each workload
based on the DAMON results and protected them with ``mlock()`` system calls by
manually modifying the source code. The optimized versions consistently show
speedup (2.55x in best case, 1.65x in average) under artificial memory
pressures. We use cgroups for the pressure.


Overhead
--------

We also measured the overhead of DAMON. The upperbound we set was kept as
expected. Besides, it was much lower (0.6 percent of the bound in best case,
13.288 percent of the bound in average). This reduction of the overhead is
mainly resulted from its core mechanism called adaptive regions adjustment.
Refer to 'Appendix D' for more detail about the mechanism. We also compared
the overhead of DAMON with that of a straightforward periodic PTE Accessed bit
checking based monitoring. DAMON's overhead was smaller than it by 94,242.42x
in best case, 3,159.61x in average.

The latest version of DAMON running with its default configuration consumes
only up to 1% of CPU time when applied to realistic workloads in PARSEC3 and
SPLASH-2X and makes no visible slowdown to the target processes.