new file mode 100644
@@ -0,0 +1,157 @@
+.. SPDX-License-Identifier: GPL-2.0
+
+==================
+Optimization Guide
+==================
+
+This document helps you estimating the amount of benefit that you could get
+from DAMON-based optimizations, and describes how you could achieve it. You
+are assumed to already read :doc:`start`.
+
+
+Check The Signs
+===============
+
+No optimization can provide same extent of benefit to every case. Therefore
+you should first guess how much improvements you could get using DAMON. If
+some of below conditions match your situation, you could consider using DAMON.
+
+- *Low IPC and High Cache Miss Ratios.* Low IPC means most of the CPU time is
+ spent waiting for the completion of time-consuming operations such as memory
+ access, while high cache miss ratios mean the caches don't help it well.
+ DAMON is not for cache level optimization, but DRAM level. However,
+ improving DRAM management will also help this case by reducing the memory
+ operation latency.
+- *Memory Over-commitment and Unknown Users.* If you are doing memory
+ overcommitment and you cannot control every user of your system, a memory
+ bank run could happen at any time. You can estimate when it will happen
+ based on DAMON's monitoring results and act earlier to avoid or deal better
+ with the crisis.
+- *Frequent Memory Pressure.* Frequent memory pressure means your system has
+ wrong configurations or memory hogs. DAMON will help you find the right
+ configuration and/or the criminals.
+- *Heterogeneous Memory System.* If your system is utilizing memory devices
+ that placed between DRAM and traditional hard disks, such as non-volatile
+ memory or fast SSDs, DAMON could help you utilizing the devices more
+ efficiently.
+
+
+Profile
+=======
+
+If you found some positive signals, you could start by profiling your workloads
+using DAMON. Find major workloads on your systems and analyze their data
+access pattern to find something wrong or can be improved. The DAMON user
+space tool (``damo``) will be useful for this.
+
+We recommend you to start from working set size distribution check using ``damo
+report wss``. If the distribution is ununiform or quite different from what
+you estimated, you could consider `Memory Configuration`_ optimization.
+
+Then, review the overall access pattern in heatmap form using ``damo report
+heats``. If it shows a simple pattern consists of a small number of memory
+regions having high contrast of access temperature, you could consider manual
+`Program Modification`_.
+
+If you still want to absorb more benefits, you should develop `Personalized
+DAMON Application`_ for your special case.
+
+You don't need to take only one approach among the above plans, but you could
+use multiple of the above approaches to maximize the benefit.
+
+
+Optimize
+========
+
+If the profiling result also says it's worth trying some optimization, you
+could consider below approaches. Note that some of the below approaches assume
+that your systems are configured with swap devices or other types of auxiliary
+memory so that you don't strictly required to accommodate the whole working set
+in the main memory. Most of the detailed optimization should be made on your
+concrete understanding of your memory devices.
+
+
+Memory Configuration
+--------------------
+
+No more no less, DRAM should be large enough to accommodate only important
+working sets, because DRAM is highly performance critical but expensive and
+heavily consumes the power. However, knowing the size of the real important
+working sets is difficult. As a consequence, people usually equips
+unnecessarily large or too small DRAM. Many problems stem from such wrong
+configurations.
+
+Using the working set size distribution report provided by ``damo report wss``,
+you can know the appropriate DRAM size for you. For example, roughly speaking,
+if you worry about only 95 percentile latency, you don't need to equip DRAM of
+a size larger than 95 percentile working set size.
+
+Let's see a real example. This `page
+<https://damonitor.github.io/doc/html/v17/admin-guide/mm/damon/guide.html#memory-configuration>`_
+shows the heatmap and the working set size distributions/changes of
+``freqmine`` workload in PARSEC3 benchmark suite. The working set size spikes
+up to 180 MiB, but keeps smaller than 50 MiB for more than 95% of the time.
+Even though you give only 50 MiB of memory space to the workload, it will work
+well for 95% of the time. Meanwhile, you can save the 130 MiB of memory space.
+
+
+Program Modification
+--------------------
+
+If the data access pattern heatmap plotted by ``damo report heats`` is quite
+simple so that you can understand how the things are going in the workload with
+your human eye, you could manually optimize the memory management.
+
+For example, suppose that the workload has two big memory object but only one
+object is frequently accessed while the other one is only occasionally
+accessed. Then, you could modify the program source code to keep the hot
+object in the main memory by invoking ``mlock()`` or ``madvise()`` with
+``MADV_WILLNEED``. Or, you could proactively evict the cold object using
+``madvise()`` with ``MADV_COLD`` or ``MADV_PAGEOUT``. Using both together
+would be also worthy.
+
+A research work [1]_ using the ``mlock()`` achieved up to 2.55x performance
+speedup.
+
+Let's see another realistic example access pattern for this kind of
+optimizations. This `page
+<https://damonitor.github.io/doc/html/v17/admin-guide/mm/damon/guide.html#program-modification>`_
+shows the visualized access patterns of streamcluster workload in PARSEC3
+benchmark suite. We can easily identify the 100 MiB sized hot object.
+
+
+Personalized DAMON Application
+------------------------------
+
+Above approaches will work well for many general cases, but would not enough
+for some special cases.
+
+If this is the case, it might be the time to forget the comfortable use of the
+user space tool and dive into the debugfs interface (refer to :doc:`usage` for
+the detail) of DAMON. Using the interface, you can control the DAMON more
+flexibly. Therefore, you can write your personalized DAMON application that
+controls the monitoring via the debugfs interface, analyzes the result, and
+applies complex optimizations itself. Using this, you can make more creative
+and wise optimizations.
+
+If you are a kernel space programmer, writing kernel space DAMON applications
+using the API (refer to the :doc:`/vm/damon/api` for more detail) would be an
+option.
+
+
+Reference Practices
+===================
+
+Referencing previously done successful practices could help you getting the
+sense for this kind of optimizations. There is an academic paper [1]_
+reporting the visualized access pattern and manual `Program
+Modification`_ results for a number of realistic workloads. You can also get
+the visualized access patterns [3]_ [4]_ [5]_ and automated DAMON-based memory
+operations results for other realistic workloads that collected with latest
+version of DAMON [2]_ .
+
+.. [1] https://dl.acm.org/doi/10.1145/3366626.3368125
+.. [2] https://damonitor.github.io/test/result/perf/latest/html/
+.. [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
+.. [4] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
+.. [5] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
new file mode 100644
@@ -0,0 +1,15 @@
+.. SPDX-License-Identifier: GPL-2.0
+
+========================
+Monitoring Data Accesses
+========================
+
+:doc:`DAMON </vm/damon/index>` allows light-weight data access monitoring.
+Using this, users can analyze and optimize their systems.
+
+.. toctree::
+ :maxdepth: 2
+
+ start
+ guide
+ usage
new file mode 100644
@@ -0,0 +1,29 @@
+.. SPDX-License-Identifier: GPL-2.0
+
+============
+Future Plans
+============
+
+DAMON is still on its first stage. Below plans are still under development.
+
+
+Automate Data Access Monitoring-based Memory Operation Schemes Execution
+========================================================================
+
+The ultimate goal of DAMON is to be used as a building block for the data
+access pattern aware kernel memory management optimization. It will make
+system just works efficiently. However, some users having very special
+workloads will want to further do their own optimization. DAMON will automate
+most of the tasks for such manual optimizations in near future. Users will be
+required to only describe what kind of data access pattern-based operation
+schemes they want in a simple form.
+
+By applying a very simple scheme for THP promotion/demotion with a prototype
+implementation, DAMON reduced 60% of THP memory footprint overhead while
+preserving 50% of the THP performance benefit. The detailed results can be
+seen on an external web page [1]_.
+
+Several RFC patchsets for this plan are available [2]_.
+
+.. [1] https://damonitor.github.io/test/result/perf/latest/html/
+.. [2] https://lore.kernel.org/linux-mm/20200616073828.16509-1-sjpark@amazon.com/
new file mode 100644
@@ -0,0 +1,96 @@
+.. SPDX-License-Identifier: GPL-2.0
+
+===============
+Getting Started
+===============
+
+This document briefly describes how you can use DAMON by demonstrating its
+default user space tool. Please note that this document describes only a part
+of its features for brevity. Please refer to :doc:`usage` for more details.
+
+
+TL; DR
+======
+
+Follow below 5 commands to monitor and visualize the access pattern of your
+workload. ::
+
+ $ git clone https://github.com/sjp38/linux -b damon/master
+ /* build the kernel with CONFIG_DAMON=y, install, reboot */
+ $ mount -t debugfs none /sys/kernel/debug/
+ $ cd linux/tools/damon
+ $ ./damo record $(pidof <your workload>)
+ $ ./damo report heats --heatmap access_pattern.png
+
+
+Prerequisites
+=============
+
+Kernel
+------
+
+You should first ensure your system is running on a kernel built with
+``CONFIG_DAMON=y``.
+
+
+User Space Tool
+---------------
+
+For the demonstration, we will use the default user space tool for DAMON,
+called DAMON Operator (DAMO). It is located at ``tools/damon/damo`` of the
+kernel source tree. For brevity, below examples assume you set ``$PATH`` to
+point it. It's not mandatory, though.
+
+Because DAMO is using the debugfs interface (refer to :doc:`usage` for the
+detail) of DAMON, you should ensure debugfs is mounted. Mount it manually as
+below::
+
+ # mount -t debugfs none /sys/kernel/debug/
+
+or append below line to your ``/etc/fstab`` file so that your system can
+automatically mount debugfs from next booting::
+
+ debugfs /sys/kernel/debug debugfs defaults 0 0
+
+
+Recording Data Access Patterns
+==============================
+
+Below commands record memory access pattern of a program and save the
+monitoring results in a file. ::
+
+ $ git clone https://github.com/sjp38/masim
+ $ cd masim; make; ./masim ./configs/zigzag.cfg &
+ $ sudo damo record -o damon.data $(pidof masim)
+
+The first two lines of the commands get an artificial memory access generator
+program and runs it in the background. It will repeatedly access two 100 MiB
+sized memory regions one by one. You can substitute this with your real
+workload. The last line asks ``damo`` to record the access pattern in
+``damon.data`` file.
+
+
+Visualizing Recorded Patterns
+=============================
+
+Below three commands visualize the recorded access patterns into three
+image files. ::
+
+ $ damo report heats --heatmap access_pattern_heatmap.png
+ $ damo report wss --range 0 101 1 --plot wss_dist.png
+ $ damo report wss --range 0 101 1 --sortby time --plot wss_chron_change.png
+
+- ``access_pattern_heatmap.png`` will show the data access pattern in a
+ heatmap, which shows when (x-axis) what memory region (y-axis) is how
+ frequently accessed (color).
+- ``wss_dist.png`` will show the distribution of the working set size.
+- ``wss_chron_change.png`` will show how the working set size has
+ chronologically changed.
+
+You can show the images in a web page [1]_ . Those made with other realistic
+workloads are also available [2]_ [3]_ [4]_.
+
+.. [1] https://damonitor.github.io/doc/html/v17/admin-guide/mm/damon/start.html#visualizing-recorded-patterns
+.. [2] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
+.. [3] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
+.. [4] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
new file mode 100644
@@ -0,0 +1,302 @@
+.. SPDX-License-Identifier: GPL-2.0
+
+===============
+Detailed Usages
+===============
+
+DAMON provides below three interfaces for different users.
+
+- *DAMON user space tool.*
+ This is for privileged people such as system administrators who want a
+ just-working human-friendly interface. Using this, users can use the DAMON’s
+ major features in a human-friendly way. It may not be highly tuned for
+ special cases, though. It supports only virtual address spaces monitoring.
+- *debugfs interface.*
+ This is for privileged user space programmers who want more optimized use of
+ DAMON. Using this, users can use DAMON’s major features by reading
+ from and writing to special debugfs files. Therefore, you can write and use
+ your personalized DAMON debugfs wrapper programs that reads/writes the
+ debugfs files instead of you. The DAMON user space tool is also a reference
+ implementation of such programs. It supports only virtual address spaces
+ monitoring.
+- *Kernel Space Programming Interface.*
+ This is for kernel space programmers. Using this, users can utilize every
+ feature of DAMON most flexibly and efficiently by writing kernel space
+ DAMON application programs for you. You can even extend DAMON for various
+ address spaces.
+
+This document does not describe the kernel space programming interface in
+detail. For that, please refer to the :doc:`/vm/damon/api`.
+
+
+DAMON User Space Tool
+=====================
+
+A reference implementation of the DAMON user space tools which provides a
+convenient user interface is in the kernel source tree. It is located at
+``tools/damon/damo`` of the tree.
+
+The tool provides a subcommands based interface. Every subcommand provides
+``-h`` option, which provides the minimal usage of it. Currently, the tool
+supports two subcommands, ``record`` and ``report``.
+
+Below example commands assume you set ``$PATH`` to point ``tools/damon/`` for
+brevity. It is not mandatory for use of ``damo``, though.
+
+
+Recording Data Access Pattern
+-----------------------------
+
+The ``record`` subcommand records the data access pattern of target workloads
+in a file (``./damon.data`` by default). You can specify the target with 1)
+the command for execution of the monitoring target process, or 2) pid of
+running target process. Below example shows a command target usage::
+
+ # cd <kernel>/tools/damon/
+ # damo record "sleep 5"
+
+The tool will execute ``sleep 5`` by itself and record the data access patterns
+of the process. Below example shows a pid target usage::
+
+ # sleep 5 &
+ # damo record `pidof sleep`
+
+The location of the recorded file can be explicitly set using ``-o`` option.
+You can further tune this by setting the monitoring attributes. To know about
+the monitoring attributes in detail, please refer to the
+:doc:`/vm/damon/design`.
+
+
+Analyzing Data Access Pattern
+-----------------------------
+
+The ``report`` subcommand reads a data access pattern record file (if not
+explicitly specified using ``-i`` option, reads ``./damon.data`` file by
+default) and generates human-readable reports. You can specify what type of
+report you want using a sub-subcommand to ``report`` subcommand. ``raw``,
+``heats``, and ``wss`` report types are supported for now.
+
+
+raw
+~~~
+
+``raw`` sub-subcommand simply transforms the binary record into a
+human-readable text. For example::
+
+ $ damo report raw
+ start_time: 193485829398
+ rel time: 0
+ nr_tasks: 1
+ target_id: 1348
+ nr_regions: 4
+ 560189609000-56018abce000( 22827008): 0
+ 7fbdff59a000-7fbdffaf1a00( 5601792): 0
+ 7fbdffaf1a00-7fbdffbb5000( 800256): 1
+ 7ffea0dc0000-7ffea0dfd000( 249856): 0
+
+ rel time: 100000731
+ nr_tasks: 1
+ target_id: 1348
+ nr_regions: 6
+ 560189609000-56018abce000( 22827008): 0
+ 7fbdff59a000-7fbdff8ce933( 3361075): 0
+ 7fbdff8ce933-7fbdffaf1a00( 2240717): 1
+ 7fbdffaf1a00-7fbdffb66d99( 480153): 0
+ 7fbdffb66d99-7fbdffbb5000( 320103): 1
+ 7ffea0dc0000-7ffea0dfd000( 249856): 0
+
+The first line shows the recording started timestamp (nanosecond). Records of
+data access patterns follows. Each record is separated by a blank line. Each
+record first specifies the recorded time (``rel time``) in relative to the
+start time, the number of monitored tasks in this record (``nr_tasks``).
+Recorded data access patterns of each task follow. Each data access pattern
+for each task shows the target's pid (``target_id``) and a number of monitored
+address regions in this access pattern (``nr_regions``) first. After that,
+each line shows the start/end address, size, and the number of observed
+accesses of each region.
+
+
+heats
+~~~~~
+
+The ``raw`` output is very detailed but hard to manually read. ``heats``
+sub-subcommand plots the data in 3-dimensional form, which represents the time
+in x-axis, address of regions in y-axis, and the access frequency in z-axis.
+Users can set the resolution of the map (``--tres`` and ``--ares``) and
+start/end point of each axis (``--tmin``, ``--tmax``, ``--amin``, and
+``--amax``) via optional arguments. For example::
+
+ $ damo report heats --tres 3 --ares 3
+ 0 0 0.0
+ 0 7609002 0.0
+ 0 15218004 0.0
+ 66112620851 0 0.0
+ 66112620851 7609002 0.0
+ 66112620851 15218004 0.0
+ 132225241702 0 0.0
+ 132225241702 7609002 0.0
+ 132225241702 15218004 0.0
+
+This command shows a recorded access pattern in heatmap of 3x3 resolution.
+Therefore it shows 9 data points in total. Each line shows each of the data
+points. The three numbers in each line represent time in nanosecond, address,
+and the observed access frequency.
+
+Users will be able to convert this text output into a heatmap image (represents
+z-axis values with colors) or other 3D representations using various tools such
+as 'gnuplot'. For more convenience, ``heats`` sub-subcommand provides the
+'gnuplot' based heatmap image creation. For this, you can use ``--heatmap``
+option. Also, note that because it uses 'gnuplot' internally, it will fail if
+'gnuplot' is not installed on your system. For example::
+
+ $ ./damo report heats --heatmap heatmap.png
+
+Creates the heatmap image in ``heatmap.png`` file. It supports ``pdf``,
+``png``, ``jpeg``, and ``svg``.
+
+If the target address space is virtual memory address space and you plot the
+entire address space, the huge unmapped regions will make the picture looks
+only black. Therefore you should do proper zoom in / zoom out using the
+resolution and axis boundary-setting arguments. To make this effort minimal,
+you can use ``--guide`` option as below::
+
+ $ ./damo report heats --guide
+ target_id:1348
+ time: 193485829398-198337863555 (4852034157)
+ region 0: 00000094564599762944-00000094564622589952 (22827008)
+ region 1: 00000140454009610240-00000140454016012288 (6402048)
+ region 2: 00000140731597193216-00000140731597443072 (249856)
+
+The output shows unions of monitored regions (start and end addresses in byte)
+and the union of monitored time duration (start and end time in nanoseconds) of
+each target task. Therefore, it would be wise to plot the data points in each
+union. If no axis boundary option is given, it will automatically find the
+biggest union in ``--guide`` output and set the boundary in it.
+
+
+wss
+~~~
+
+The ``wss`` type extracts the distribution and chronological working set size
+changes from the records. For example::
+
+ $ ./damo report wss
+ # <percentile> <wss>
+ # target_id 1348
+ # avr: 66228
+ 0 0
+ 25 0
+ 50 0
+ 75 0
+ 100 1920615
+
+Without any option, it shows the distribution of the working set sizes as
+above. It shows 0th, 25th, 50th, 75th, and 100th percentile and the average of
+the measured working set sizes in the access pattern records. In this case,
+the working set size was zero for 75th percentile but 1,920,615 bytes in max
+and 66,228 bytes on average.
+
+By setting the sort key of the percentile using '--sortby', you can show how
+the working set size has chronologically changed. For example::
+
+ $ ./damo report wss --sortby time
+ # <percentile> <wss>
+ # target_id 1348
+ # avr: 66228
+ 0 0
+ 25 0
+ 50 0
+ 75 0
+ 100 0
+
+The average is still 66,228. And, because the access was spiked in very short
+duration and this command plots only 4 data points, we cannot show when the
+access spikes made. Users can specify the resolution of the distribution
+(``--range``). By giving more fine resolution, the short duration spikes could
+be found.
+
+Similar to that of ``heats --heatmap``, it also supports 'gnuplot' based simple
+visualization of the distribution via ``--plot`` option.
+
+
+debugfs Interface
+=================
+
+DAMON exports four files, ``attrs``, ``target_ids``, ``record``, and
+``monitor_on`` under its debugfs directory, ``<debugfs>/damon/``.
+
+
+Attributes
+----------
+
+Users can get and set the ``sampling interval``, ``aggregation interval``,
+``regions update interval``, and min/max number of monitoring target regions by
+reading from and writing to the ``attrs`` file. To know about the monitoring
+attributes in detail, please refer to the :doc:`/vm/damon/design`. For
+example, below commands set those values to 5 ms, 100 ms, 1,000 ms, 10 and
+1000, and then check it again::
+
+ # cd <debugfs>/damon
+ # echo 5000 100000 1000000 10 1000 > attrs
+ # cat attrs
+ 5000 100000 1000000 10 1000
+
+
+Target IDs
+----------
+
+Some types of address spaces supports multiple monitoring target. For example,
+the virtual memory address spaces monitoring can have multiple processes as the
+monitoring targets. Users can set the targets by writing relevant id values of
+the targets to, and get the ids of the current targets by reading from the
+``target_ids`` file. In case of the virtual address spaces monitoring, the
+values should be pids of the monitoring target processes. For example, below
+commands set processes having pids 42 and 4242 as the monitoring targets and
+check it again::
+
+ # cd <debugfs>/damon
+ # echo 42 4242 > target_ids
+ # cat target_ids
+ 42 4242
+
+Note that setting the target ids doesn't start the monitoring.
+
+
+Record
+------
+
+This debugfs file allows you to record monitored access patterns in a regular
+binary file. The recorded results are first written in an in-memory buffer and
+flushed to a file in batch. Users can get and set the size of the buffer and
+the path to the result file by reading from and writing to the ``record`` file.
+For example, below commands set the buffer to be 4 KiB and the result to be
+saved in ``/damon.data``. ::
+
+ # cd <debugfs>/damon
+ # echo "4096 /damon.data" > record
+ # cat record
+ 4096 /damon.data
+
+The recording can be disabled by setting the buffer size zero.
+
+
+Turning On/Off
+--------------
+
+Setting the files as described above doesn't incur effect unless you explicitly
+start the monitoring. You can start, stop, and check the current status of the
+monitoring by writing to and reading from the ``monitor_on`` file. Writing
+``on`` to the file starts the monitoring of the targets with the attributes.
+Writing ``off`` to the file stops those. DAMON also stops if every target
+process is terminated. Below example commands turn on, off, and check the
+status of DAMON::
+
+ # cd <debugfs>/damon
+ # echo on > monitor_on
+ # echo off > monitor_on
+ # cat monitor_on
+ off
+
+Please note that you cannot write to the above-mentioned debugfs files while
+the monitoring is turned on. If you write to the files while DAMON is running,
+an error code such as ``-EBUSY`` will be returned.
@@ -27,6 +27,7 @@ the Linux memory management.
concepts
cma_debugfs
+ damon/index
hugetlbpage
idle_page_tracking
ksm
new file mode 100644
@@ -0,0 +1,20 @@
+.. SPDX-License-Identifier: GPL-2.0
+
+=============
+API Reference
+=============
+
+Kernel space programs can use every feature of DAMON using below APIs. All you
+need to do is including ``damon.h``, which is located in ``include/linux/`` of
+the source tree.
+
+Structures
+==========
+
+.. kernel-doc:: include/linux/damon.h
+
+
+Functions
+=========
+
+.. kernel-doc:: mm/damon.c
new file mode 100644
@@ -0,0 +1,166 @@
+.. SPDX-License-Identifier: GPL-2.0
+
+======
+Design
+======
+
+Configurable Layers
+===================
+
+DAMON provides data access monitoring functionality while making the accuracy
+and the overhead controllable. The fundamental access monitorings require
+primitives that dependent on and optimized for the target address space. On
+the other hand, the accuracy and overhead tradeoff mechanism, which is the core
+of DAMON, is in the pure logic space. DAMON separates the two parts in
+different layers and defines its interface to allow various low level
+primitives implementations configurable with the core logic.
+
+Due to this separated design and the configurable interface, users can extend
+DAMON for any address space by configuring the core logics with appropriate low
+level primitive implementations. If appropriate one is not provided, users can
+implement the primitives on their own.
+
+For example, physical memory, virtual memory, swap space, those for specific
+processes, NUMA nodes, files, and backing memory devices would be supportable.
+Also, if some architectures or devices support special optimized access check
+primitives, those will be easily configurable.
+
+
+Reference Implementations of Address Space Specific Primitives
+==============================================================
+
+The low level primitives for the fundamental access monitoring are defined in
+two parts:
+
+1. Identification of the monitoring target address range for the address space.
+2. Access check of specific address range in the target space.
+
+DAMON currently provides the implementation of the primitives for only the
+virtual address spaces. Below two subsections describe how it works.
+
+
+PTE Accessed-bit Based Access Check
+-----------------------------------
+
+The implementation for the virtual address space uses PTE Accessed-bit for
+basic access checks. It finds the relevant PTE Accessed bit from the address
+by walking the page table for the target task of the address. In this way, the
+implementation finds and clears the bit for next sampling target address and
+checks whether the bit set again after one sampling period. This could disturb
+other kernel subsystems using the Accessed bits, namely Idle page tracking and
+the reclaim logic. To avoid such disturbances, DAMON makes it mutually
+exclusive with Idle page tracking and uses ``PG_idle`` and ``PG_young`` page
+flags to solve the conflict with the reclaim logic, as Idle page tracking does.
+
+
+VMA-based Target Address Range Construction
+-------------------------------------------
+
+Only small parts in the super-huge virtual address space of the processes are
+mapped to the physical memory and accessed. Thus, tracking the unmapped
+address regions is just wasteful. However, because DAMON can deal with some
+level of noise using the adaptive regions adjustment mechanism, tracking every
+mapping is not strictly required but could even incur a high overhead in some
+cases. That said, too huge unmapped areas inside the monitoring target should
+be removed to not take the time for the adaptive mechanism.
+
+For the reason, this implementation converts the complex mappings to three
+distinct regions that cover every mapped area of the address space. The two
+gaps between the three regions are the two biggest unmapped areas in the given
+address space. The two biggest unmapped areas would be the gap between the
+heap and the uppermost mmap()-ed region, and the gap between the lowermost
+mmap()-ed region and the stack in most of the cases. Because these gaps are
+exceptionally huge in usual address spaces, excluding these will be sufficient
+to make a reasonable trade-off. Below shows this in detail::
+
+ <heap>
+ <BIG UNMAPPED REGION 1>
+ <uppermost mmap()-ed region>
+ (small mmap()-ed regions and munmap()-ed regions)
+ <lowermost mmap()-ed region>
+ <BIG UNMAPPED REGION 2>
+ <stack>
+
+
+Address Space Independent Core Mechanisms
+=========================================
+
+Below four sections describe each of the DAMON core mechanisms and the five
+monitoring attributes, ``sampling interval``, ``aggregation interval``,
+``regions update interval``, ``minimum number of regions``, and ``maximum
+number of regions``.
+
+
+Access Frequency Monitoring
+---------------------------
+
+The output of DAMON says what pages are how frequently accessed for a given
+duration. The resolution of the access frequency is controlled by setting
+``sampling interval`` and ``aggregation interval``. In detail, DAMON checks
+access to each page per ``sampling interval`` and aggregates the results. In
+other words, counts the number of the accesses to each page. After each
+``aggregation interval`` passes, DAMON calls callback functions that previously
+registered by users so that users can read the aggregated results and then
+clears the results. This can be described in below simple pseudo-code::
+
+ while monitoring_on:
+ for page in monitoring_target:
+ if accessed(page):
+ nr_accesses[page] += 1
+ if time() % aggregation_interval == 0:
+ for callback in user_registered_callbacks:
+ callback(monitoring_target, nr_accesses)
+ for page in monitoring_target:
+ nr_accesses[page] = 0
+ sleep(sampling interval)
+
+The monitoring overhead of this mechanism will arbitrarily increase as the
+size of the target workload grows.
+
+
+Region Based Sampling
+---------------------
+
+To avoid the unbounded increase of the overhead, DAMON groups adjacent pages
+that assumed to have the same access frequencies into a region. As long as the
+assumption (pages in a region have the same access frequencies) is kept, only
+one page in the region is required to be checked. Thus, for each ``sampling
+interval``, DAMON randomly picks one page in each region, waits for one
+``sampling interval``, checks whether the page is accessed meanwhile, and
+increases the access frequency of the region if so. Therefore, the monitoring
+overhead is controllable by setting the number of regions. DAMON allows users
+to set the minimum and the maximum number of regions for the trade-off.
+
+This scheme, however, cannot preserve the quality of the output if the
+assumption is not guaranteed.
+
+
+Adaptive Regions Adjustment
+---------------------------
+
+Even somehow the initial monitoring target regions are well constructed to
+fulfill the assumption (pages in same region have similar access frequencies),
+the data access pattern can be dynamically changed. This will result in low
+monitoring quality. To keep the assumption as much as possible, DAMON
+adaptively merges and splits each region based on their access frequency.
+
+For each ``aggregation interval``, it compares the access frequencies of
+adjacent regions and merges those if the frequency difference is small. Then,
+after it reports and clears the aggregated access frequency of each region, it
+splits each region into two or three regions if the total number of regions
+will not exceed the user-specified maximum number of regions after the split.
+
+In this way, DAMON provides its best-effort quality and minimal overhead while
+keeping the bounds users set for their trade-off.
+
+
+Dynamic Target Space Updates Handling
+-------------------------------------
+
+The monitoring target address range could dynamically changed. For example,
+virtual memory could be dynamically mapped and unmapped. Physical memory could
+be hot-plugged.
+
+As the changes could be quite frequent in some cases, DAMON checks the dynamic
+memory mapping changes and applies it to the abstracted target area only for
+each of a user-specified time interval (``regions update interval``).
new file mode 100644
@@ -0,0 +1,222 @@
+.. SPDX-License-Identifier: GPL-2.0
+
+==========
+Evaluation
+==========
+
+DAMON is lightweight. It increases system memory usage by only -0.25% and
+consumes less than 1% CPU time in most case. It slows target workloads down by
+only 0.94%.
+
+DAMON is accurate and useful for memory management optimizations. An
+experimental DAMON-based operation scheme for THP, 'ethp', removes 31.29% of
+THP memory overheads while preserving 60.64% of THP speedup. Another
+experimental DAMON-based 'proactive reclamation' implementation, 'prcl',
+reduces 87.95% of residential sets and 29.52% of system memory footprint while
+incurring only 2.15% runtime overhead in the best case (parsec3/freqmine).
+
+Setup
+=====
+
+On a QEMU/KVM based virtual machine utilizing 20GB of RAM and hosted by an
+Intel i7 machine that running a kernel that v16 DAMON patchset is applied, 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 use another wrapper
+scripts [5]_ for convenient setup and run of the workloads.
+
+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.
+
+Configurations
+--------------
+
+The configurations I use are as below.
+
+- orig: Linux v5.7 with 'madvise' THP policy
+- rec: 'orig' plus DAMON running with virtual memory access recording
+- prec: 'orig' plus DAMON running with physical memory access recording
+- thp: same with 'orig', but use 'always' THP policy
+- ethp: 'orig' plus a DAMON operation scheme, 'efficient THP'
+- prcl: 'orig' plus a DAMON operation scheme, 'proactive reclaim [6]_'
+
+I use 'rec' for measurement of DAMON overheads to target workloads and system
+memory. 'prec' is for physical memory monitroing and recording. It monitors
+17GB sized 'System RAM' region. The remaining configs including 'thp', 'ethp',
+and 'prcl' are for measurement of DAMON monitoring accuracy.
+
+'ethp' and 'prcl' are simple DAMON-based operation schemes developed for
+proof of concepts of DAMON. 'ethp' reduces memory space waste of THP by using
+DAMON for the 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 shows >=5% access rate, use regular
+ # pages if a region >=2MB shows <5% access rate for >=13 seconds
+ null null 5 null null null hugepage
+ 2M null null null 13s null nohugepage
+
+ # prcl: If a region >=4KB shows <=5% access rate for >=7 seconds, page out.
+ 4K null null 5 7s 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 more tuned.
+
+.. [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] "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 configuration except 'orig' shows
+its overhead relative to 'orig' in percent within parenthesizes.::
+
+ runtime orig rec (overhead) prec (overhead) thp (overhead) ethp (overhead) prcl (overhead)
+ parsec3/blackscholes 107.228 107.859 (0.59) 108.110 (0.82) 107.381 (0.14) 106.811 (-0.39) 114.766 (7.03)
+ parsec3/bodytrack 79.292 79.609 (0.40) 79.777 (0.61) 79.313 (0.03) 78.892 (-0.50) 80.398 (1.40)
+ parsec3/canneal 148.887 150.878 (1.34) 153.337 (2.99) 127.873 (-14.11) 132.272 (-11.16) 167.631 (12.59)
+ parsec3/dedup 11.970 11.975 (0.04) 12.024 (0.45) 11.752 (-1.82) 11.921 (-0.41) 13.244 (10.64)
+ parsec3/facesim 212.800 215.927 (1.47) 215.004 (1.04) 205.117 (-3.61) 207.401 (-2.54) 220.834 (3.78)
+ parsec3/ferret 190.646 192.560 (1.00) 192.414 (0.93) 190.662 (0.01) 192.309 (0.87) 193.497 (1.50)
+ parsec3/fluidanimate 213.951 216.459 (1.17) 217.578 (1.70) 209.500 (-2.08) 211.826 (-0.99) 218.299 (2.03)
+ parsec3/freqmine 291.050 292.117 (0.37) 293.279 (0.77) 289.553 (-0.51) 291.768 (0.25) 297.309 (2.15)
+ parsec3/raytrace 118.645 119.734 (0.92) 119.521 (0.74) 117.715 (-0.78) 118.844 (0.17) 134.045 (12.98)
+ parsec3/streamcluster 332.843 336.997 (1.25) 337.049 (1.26) 279.716 (-15.96) 290.985 (-12.58) 346.646 (4.15)
+ parsec3/swaptions 155.437 157.174 (1.12) 156.159 (0.46) 155.017 (-0.27) 154.955 (-0.31) 156.555 (0.72)
+ parsec3/vips 59.215 59.426 (0.36) 59.156 (-0.10) 59.243 (0.05) 58.858 (-0.60) 60.184 (1.64)
+ parsec3/x264 67.445 71.400 (5.86) 71.122 (5.45) 64.078 (-4.99) 66.027 (-2.10) 71.489 (6.00)
+ splash2x/barnes 81.826 81.800 (-0.03) 82.648 (1.00) 74.343 (-9.15) 79.063 (-3.38) 103.785 (26.84)
+ splash2x/fft 33.850 34.148 (0.88) 33.912 (0.18) 23.493 (-30.60) 32.684 (-3.44) 48.303 (42.70)
+ splash2x/lu_cb 86.404 86.333 (-0.08) 86.988 (0.68) 85.720 (-0.79) 85.944 (-0.53) 89.338 (3.40)
+ splash2x/lu_ncb 94.908 98.021 (3.28) 96.041 (1.19) 90.304 (-4.85) 93.279 (-1.72) 97.270 (2.49)
+ splash2x/ocean_cp 47.122 47.391 (0.57) 47.902 (1.65) 43.227 (-8.26) 44.609 (-5.33) 51.410 (9.10)
+ splash2x/ocean_ncp 93.147 92.911 (-0.25) 93.886 (0.79) 51.451 (-44.76) 71.107 (-23.66) 112.554 (20.83)
+ splash2x/radiosity 92.150 92.604 (0.49) 93.339 (1.29) 90.802 (-1.46) 91.824 (-0.35) 104.439 (13.34)
+ splash2x/radix 31.961 32.113 (0.48) 32.066 (0.33) 25.184 (-21.20) 30.412 (-4.84) 49.989 (56.41)
+ splash2x/raytrace 84.781 85.278 (0.59) 84.763 (-0.02) 83.192 (-1.87) 83.970 (-0.96) 85.382 (0.71)
+ splash2x/volrend 87.401 87.978 (0.66) 87.977 (0.66) 86.636 (-0.88) 87.169 (-0.26) 88.043 (0.73)
+ splash2x/water_nsquared 239.140 239.570 (0.18) 240.901 (0.74) 221.323 (-7.45) 224.670 (-6.05) 244.492 (2.24)
+ splash2x/water_spatial 89.538 89.978 (0.49) 90.171 (0.71) 89.729 (0.21) 89.238 (-0.34) 99.331 (10.94)
+ total 3051.620 3080.230 (0.94) 3085.130 (1.10) 2862.320 (-6.20) 2936.830 (-3.76) 3249.240 (6.48)
+
+
+ memused.avg orig rec (overhead) prec (overhead) thp (overhead) ethp (overhead) prcl (overhead)
+ parsec3/blackscholes 1676679.200 1683789.200 (0.42) 1680281.200 (0.21) 1613817.400 (-3.75) 1835229.200 (9.46) 1407952.800 (-16.03)
+ parsec3/bodytrack 1295736.000 1308412.600 (0.98) 1311988.000 (1.25) 1243417.400 (-4.04) 1435410.600 (10.78) 1255566.400 (-3.10)
+ parsec3/canneal 1004062.000 1008823.800 (0.47) 1000100.200 (-0.39) 983976.000 (-2.00) 1051719.600 (4.75) 993055.800 (-1.10)
+ parsec3/dedup 2389765.800 2393381.000 (0.15) 2366668.200 (-0.97) 2412948.600 (0.97) 2435885.600 (1.93) 2380172.800 (-0.40)
+ parsec3/facesim 488927.200 498228.000 (1.90) 496683.800 (1.59) 476327.800 (-2.58) 552890.000 (13.08) 449143.600 (-8.14)
+ parsec3/ferret 280324.600 282032.400 (0.61) 282284.400 (0.70) 258211.000 (-7.89) 331493.800 (18.25) 265850.400 (-5.16)
+ parsec3/fluidanimate 560636.200 569038.200 (1.50) 565067.400 (0.79) 556923.600 (-0.66) 588021.200 (4.88) 512901.600 (-8.51)
+ parsec3/freqmine 883286.000 904960.200 (2.45) 886105.200 (0.32) 849347.400 (-3.84) 998358.000 (13.03) 622542.800 (-29.52)
+ parsec3/raytrace 1639370.200 1642318.200 (0.18) 1626673.200 (-0.77) 1591284.200 (-2.93) 1755088.400 (7.06) 1410261.600 (-13.98)
+ parsec3/streamcluster 116955.600 127251.400 (8.80) 121441.000 (3.84) 113853.800 (-2.65) 139659.400 (19.41) 120335.200 (2.89)
+ parsec3/swaptions 8342.400 18555.600 (122.43) 16581.200 (98.76) 6745.800 (-19.14) 27487.200 (229.49) 14275.600 (71.12)
+ parsec3/vips 2776417.600 2784989.400 (0.31) 2820564.600 (1.59) 2694060.800 (-2.97) 2968650.000 (6.92) 2713590.000 (-2.26)
+ parsec3/x264 2912885.000 2936474.600 (0.81) 2936775.800 (0.82) 2799599.200 (-3.89) 3168695.000 (8.78) 2829085.800 (-2.88)
+ splash2x/barnes 1206459.600 1204145.600 (-0.19) 1177390.000 (-2.41) 1210556.800 (0.34) 1214978.800 (0.71) 907737.000 (-24.76)
+ splash2x/fft 9384156.400 9258749.600 (-1.34) 8560377.800 (-8.78) 9337563.000 (-0.50) 9228873.600 (-1.65) 9823394.400 (4.68)
+ splash2x/lu_cb 510210.800 514052.800 (0.75) 502735.200 (-1.47) 514459.800 (0.83) 523884.200 (2.68) 367563.200 (-27.96)
+ splash2x/lu_ncb 510091.200 516046.800 (1.17) 505327.600 (-0.93) 512568.200 (0.49) 524178.400 (2.76) 427981.800 (-16.10)
+ splash2x/ocean_cp 3342260.200 3294531.200 (-1.43) 3171236.000 (-5.12) 3379693.600 (1.12) 3314896.600 (-0.82) 3252406.000 (-2.69)
+ splash2x/ocean_ncp 3900447.200 3881682.600 (-0.48) 3816493.200 (-2.15) 7065506.200 (81.15) 4449224.400 (14.07) 3829931.200 (-1.81)
+ splash2x/radiosity 1466372.000 1463840.200 (-0.17) 1438554.000 (-1.90) 1475151.600 (0.60) 1474828.800 (0.58) 496636.000 (-66.13)
+ splash2x/radix 1760056.600 1691719.000 (-3.88) 1613057.400 (-8.35) 1384416.400 (-21.34) 1632274.400 (-7.26) 2141640.200 (21.68)
+ splash2x/raytrace 38794.000 48187.400 (24.21) 46728.400 (20.45) 41323.400 (6.52) 61499.800 (58.53) 68455.200 (76.46)
+ splash2x/volrend 138107.400 148197.000 (7.31) 146223.400 (5.88) 128076.400 (-7.26) 164593.800 (19.18) 140885.200 (2.01)
+ splash2x/water_nsquared 39072.000 49889.200 (27.69) 47548.400 (21.69) 37546.400 (-3.90) 57195.400 (46.38) 42994.200 (10.04)
+ splash2x/water_spatial 662099.800 665964.800 (0.58) 651017.000 (-1.67) 659808.400 (-0.35) 674475.600 (1.87) 519677.600 (-21.51)
+ total 38991500.000 38895300.000 (-0.25) 37787817.000 (-3.09) 41347200.000 (6.04) 40609600.000 (4.15) 36994100.000 (-5.12)
+
+
+DAMON Overheads
+---------------
+
+In total, DAMON virtual memory access recording feature ('rec') incurs 0.94%
+runtime overhead and -0.25% memory space overhead. Even though the size of the
+monitoring target region becomes much larger with the physical memory access
+recording ('prec'), it still shows only modest amount of overhead (1.10% for
+runtime and -3.09% for memory footprint).
+
+For a convenience test run of 'rec' and 'prec', I use a Python wrapper. The
+wrapper constantly consumes about 10-15MB of memory. This becomes a high
+memory overhead if the target workload has a 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 6.20% speedup but incurs 6.04% memory
+overhead. It achieves 44.76% speedup in the best case, but 81.15% memory
+overhead in the 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 3.76% speedup and 4.15% memory overhead. In other words, 'ethp' removes
+31.29% of THP memory waste while preserving 60.64% of THP speedup in total. In
+the case of the 'splash2x/ocean_ncp', 'ethp' removes 82.66% of THP memory waste
+while preserving 52.85% of THP speedup.
+
+
+Proactive Reclamation
+---------------------
+
+As similar to the original work, I use 4G 'zram' swap device for this
+configuration.
+
+In total, our 1 line implementation of Proactive Reclamation, 'prcl', incurred
+6.48% runtime overhead in total while achieving 5.12% 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) prec (overhead) thp (overhead) ethp (overhead) prcl (overhead)
+ parsec3/blackscholes 590412.200 589991.400 (-0.07) 591716.400 (0.22) 591131.000 (0.12) 591055.200 (0.11) 274623.600 (-53.49)
+ parsec3/bodytrack 32202.200 32297.400 (0.30) 32301.400 (0.31) 32328.000 (0.39) 32169.800 (-0.10) 25311.200 (-21.40)
+ parsec3/canneal 840063.600 839145.200 (-0.11) 839506.200 (-0.07) 835102.600 (-0.59) 839766.000 (-0.04) 833091.800 (-0.83)
+ parsec3/dedup 1185493.200 1202688.800 (1.45) 1204597.000 (1.61) 1238071.400 (4.44) 1201689.400 (1.37) 920688.600 (-22.34)
+ parsec3/facesim 311570.400 311542.000 (-0.01) 311665.000 (0.03) 316106.400 (1.46) 312003.400 (0.14) 252646.000 (-18.91)
+ parsec3/ferret 99783.200 99330.000 (-0.45) 99735.000 (-0.05) 102000.600 (2.22) 99927.400 (0.14) 90967.400 (-8.83)
+ parsec3/fluidanimate 531780.800 531800.800 (0.00) 531754.600 (-0.00) 532009.600 (0.04) 531822.400 (0.01) 479116.000 (-9.90)
+ parsec3/freqmine 551787.600 551550.600 (-0.04) 551950.000 (0.03) 556030.000 (0.77) 553720.400 (0.35) 66480.000 (-87.95)
+ parsec3/raytrace 895247.000 895240.200 (-0.00) 895770.400 (0.06) 895880.200 (0.07) 893516.600 (-0.19) 327339.600 (-63.44)
+ parsec3/streamcluster 110862.200 110840.400 (-0.02) 110878.600 (0.01) 112067.200 (1.09) 112010.800 (1.04) 109763.600 (-0.99)
+ parsec3/swaptions 5630.000 5580.800 (-0.87) 5599.600 (-0.54) 5624.200 (-0.10) 5697.400 (1.20) 3792.400 (-32.64)
+ parsec3/vips 31677.200 31881.800 (0.65) 31785.800 (0.34) 32177.000 (1.58) 32456.800 (2.46) 29692.000 (-6.27)
+ parsec3/x264 81796.400 81918.600 (0.15) 81827.600 (0.04) 82734.800 (1.15) 82854.000 (1.29) 81478.200 (-0.39)
+ splash2x/barnes 1216014.600 1215462.000 (-0.05) 1218535.200 (0.21) 1227689.400 (0.96) 1219022.000 (0.25) 650771.000 (-46.48)
+ splash2x/fft 9622775.200 9511973.400 (-1.15) 9688178.600 (0.68) 9733868.400 (1.15) 9651488.000 (0.30) 7567077.400 (-21.36)
+ splash2x/lu_cb 511102.400 509911.600 (-0.23) 511123.800 (0.00) 514466.800 (0.66) 510462.800 (-0.13) 361014.000 (-29.37)
+ splash2x/lu_ncb 510569.800 510724.600 (0.03) 510888.800 (0.06) 513951.600 (0.66) 509474.400 (-0.21) 424030.400 (-16.95)
+ splash2x/ocean_cp 3413563.600 3413721.800 (0.00) 3398399.600 (-0.44) 3446878.000 (0.98) 3404799.200 (-0.26) 3244787.400 (-4.94)
+ splash2x/ocean_ncp 3927797.400 3936294.400 (0.22) 3917698.800 (-0.26) 7181781.200 (82.85) 4525783.600 (15.22) 3693747.800 (-5.96)
+ splash2x/radiosity 1477264.800 1477569.200 (0.02) 1476954.200 (-0.02) 1485724.800 (0.57) 1474684.800 (-0.17) 230128.000 (-84.42)
+ splash2x/radix 1773025.000 1754424.200 (-1.05) 1743194.400 (-1.68) 1445575.200 (-18.47) 1694855.200 (-4.41) 1769750.000 (-0.18)
+ splash2x/raytrace 23292.000 23284.000 (-0.03) 23292.800 (0.00) 28704.800 (23.24) 26489.600 (13.73) 15753.000 (-32.37)
+ splash2x/volrend 44095.800 44068.200 (-0.06) 44107.600 (0.03) 44114.600 (0.04) 44054.000 (-0.09) 31616.000 (-28.30)
+ splash2x/water_nsquared 29416.800 29403.200 (-0.05) 29406.400 (-0.04) 30103.200 (2.33) 29433.600 (0.06) 24927.400 (-15.26)
+ splash2x/water_spatial 657791.000 657840.400 (0.01) 657826.600 (0.01) 657595.800 (-0.03) 656617.800 (-0.18) 481334.800 (-26.83)
+ total 28475091.000 28368400.000 (-0.37) 28508700.000 (0.12) 31641800.000 (11.12) 29036000.000 (1.97) 21989800.000 (-22.78)
+
+In total, 22.78% of residential sets were reduced.
+
+With parsec3/freqmine, 'prcl' reduced 87.95% of residential sets and 29.52% of
+system memory usage while incurring only 2.15% runtime overhead.
new file mode 100644
@@ -0,0 +1,58 @@
+.. SPDX-License-Identifier: GPL-2.0
+
+==========================
+Frequently Asked Questions
+==========================
+
+Why a new subsystem, instead of extending perf or other user space tools?
+=========================================================================
+
+First, because it needs to be lightweight as much as possible so that it can be
+used online, any unnecessary overhead such as kernel - user space context
+switching cost should be avoided. Second, DAMON aims to be used by other
+programs including the kernel. Therefore, having a dependency on specific
+tools like perf is not desirable. These are the two biggest reasons why DAMON
+is implemented in the kernel space.
+
+
+Can 'idle pages tracking' or 'perf mem' substitute DAMON?
+=========================================================
+
+Idle page tracking is a low level primitive for access check of the physical
+address space. 'perf mem' is similar, though it can use sampling to minimize
+the overhead. On the other hand, DAMON is a higher-level framework for the
+monitoring of various address spaces. It is focused on memory management
+optimization and provides sophisticated accuracy/overhead handling mechanisms.
+Therefore, 'idle pages tracking' and 'perf mem' could provide a subset of
+DAMON's output, but cannot substitute DAMON.
+
+
+How can I optimize my system's memory management using DAMON?
+=============================================================
+
+Because there are several ways for the DAMON-based optimizations, we wrote a
+separate document, :doc:`/admin-guide/mm/damon/guide`. Please refer to that.
+
+
+Does DAMON support virtual memory only?
+=======================================
+
+No. The core of the DAMON is address space independent. The address space
+specific low level primitive parts including monitoring target regions
+constructions and actual access checks can be implemented and configured on the
+DAMON core by the users. In this way, DAMON users can monitor any address
+space with any access check technique.
+
+Nonetheless, DAMON provides vma tracking and PTE Accessed bit check based
+implementations of the address space dependent functions for the virtual memory
+by default, for a reference and convenient use. In near future, we will
+provide those for physical memory address space.
+
+
+Can I simply monitor page granularity?
+======================================
+
+Yes. You can do so by setting the ``min_nr_regions`` attribute higher than the
+working set size divided by the page size. Because the monitoring target
+regions size is forced to be ``>=page size``, the region split will make no
+effect.
new file mode 100644
@@ -0,0 +1,31 @@
+.. SPDX-License-Identifier: GPL-2.0
+
+==========================
+DAMON: Data Access MONitor
+==========================
+
+DAMON is a data access monitoring framework subsystem for the Linux kernel.
+The core mechanisms of DAMON (refer to :doc:`design` for the detail) make it
+
+ - *accurate* (the monitoring output is useful enough for DRAM level memory
+ management; It might not appropriate for CPU Cache levels, though),
+ - *light-weight* (the monitoring overhead is low enough to be applied online),
+ and
+ - *scalable* (the upper-bound of the overhead is in constant range regardless
+ of the size of target workloads).
+
+Using this framework, therefore, the kernel's memory management mechanisms can
+make advanced decisions. Experimental memory management optimization works
+that incurring high data accesses monitoring overhead could implemented again.
+In user space, meanwhile, users who have some special workloads can write
+personalized applications for better understanding and optimizations of their
+workloads and systems.
+
+.. toctree::
+ :maxdepth: 2
+
+ faq
+ design
+ eval
+ api
+ plans
@@ -31,6 +31,7 @@ descriptions of data structures and algorithms.
active_mm
balance
cleancache
+ damon/index
free_page_reporting
frontswap
highmem