Message-ID: <20200110131522.29964-1-sjpark@amazon.com>
Date:   Fri, 10 Jan 2020 14:15:17 +0100
From:   SeongJae Park <sjpark@...zon.com>
To:     <akpm@...ux-foundation.org>
CC:     <linux-mm@...ck.org>, <linux-kernel@...r.kernel.org>,
        <corbet@....net>, <linux-doc@...r.kernel.org>, <mgorman@...e.de>,
        <brendanhiggins@...gle.com>, <sj38.park@...il.com>,
        SeongJae Park <sjpark@...zon.de>
Subject: [RFC PATCH 0/5] Introduce Data Access MONitor (DAMON)

From: SeongJae Park <sjpark@...zon.de>

This RFC patchset introduces a new kernel module for practical monitoring of
data accesses, namely DAMON.

The patches are organized in the following sequence.  The first and second
patch introduces the core logic and the raw level user interface of DAMON,
respectively.  To provide a minimal reference to the raw level interfaces and
for more convenient test of the DAMON itself, the third patch implements an
user space wrapper tools for the DAMON.  The fourth patch adds a document for
the DAMON, and finally the fifth patch provides DAMON's unit tests, which is
using the kunit framework.

The patches are based on the v5.4 plus the back-ported kunit, which retrieved
from v5.5-rc1.  You can also clone the complete git tree by:

    $ git clone git://github.com/sjp38/linux -b damon/rfc/v1

The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/rfc/v1

----

DAMON is a kernel module that allows users to monitor the actual memory access
pattern of specific user-space processes.  It aims to be 1) accurate enough to
be useful for performance-centric domains, and 2) sufficiently light-weight so
that it can be applied online.

For the goals, DAMON utilizes its two core mechanisms, called region-based
sampling and adaptive regions adjustment.  The region-based sampling allows
users to make their own trade-off between the quality and the overhead of the
monitoring and set the upperbound of the monitoring overhead.  Further, the
adaptive regions adjustment mechanism makes DAMON to maximize the quality and
minimize the overhead with its best efforts while preserving the users
configured trade-off.


Background
==========

For performance-centric analysis and optimizations of memory management schemes
(either that of kernel space or user space), the actual data access pattern of
the workloads is highly useful.  The information need to be only reasonable
rather than strictly correct, because some level of incorrectness can be
handled in many performance-centric domains.  It also need to be taken within
reasonably short time with only light-weight overhead.

Manually extracting such data is not easy and time consuming if the target
workload is huge and complex, even for the developers of the programs.  There
are a range of tools and techniques developed for general memory access
investigations, and some of those could be partially used for this purpose.
However, most of those are not practical or unscalable, mainly because those
are designed with no consideration about the trade-off between the accuracy of
the output and the overhead.

The memory access instrumentation techniques which is applied to many tools
such as Intel PIN is essential for correctness required cases such as invalid
memory access bug detections.  However, those usually incur high overhead which
is unacceptable for many of the performance-centric domains.  Periodic access
checks based on H/W or S/W access counting features (e.g., the Accessed bits of
PTEs or the PG_Idle flags of pages) can dramatically decrease the overhead by
forgiving some of the quality, compared to the instrumentation based
techniques.  The reduced quality is still reasonable for many of the domains,
but the overhead can 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.


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).  705–721.
[3] Harald Servat, Antonio J Peña, Germán Llort, Estanislao Mercadal,
    HansChristian Hoppe, and Jesús Labarta. 2017. Automating the application
    data placement in hybrid memory systems. In 2017 IEEE International
    Conference on Cluster Computing (CLUSTER). IEEE, 126–136.
[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, 317–330.
    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, 487–498.
    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,
    561–574.  https://www.usenix.org/conference/atc18/presentation/wang-haojie
[8] Jonathan Corbet. 2019. Proactively reclaiming idle memory. (2019).
    https://lwn.net/Articles/787611/.


Expected Use-cases
==================

A straightforward usecase of DAMON would be the program behavior analysis.
With the DAMON output, users can confirm whether the program is running as
intended or not.  This will be useful for debuggings and tests of design
points.

The monitored results can also be useful for counting the dynamic working set
size of workloads.  For the administration of memory overcommitted systems or
selection of the environments (e.g., containers providing different amount of
memory) for your workloads, this will be useful.

If you are a programmer, you can optimize your program by managing the memory
based on the actual data access pattern.  For example, you can identify the
dynamic hotness of your data using DAMON and call ``mlock()`` to keep your hot
data in DRAM, or call ``madvise()`` with ``MADV_PAGEOUT`` to proactively
reclaim cold data.  Even though your program is guaranteed to not encounter
memory pressure, you can still improve the performance by applying the DAMON
outputs for call of ``MADV_HUGEPAGE`` and ``MADV_NOHUGEPAGE``.  More creative
optimizations would be possible.  Our evaluations of DAMON includes a
straightforward optimization using the ``mlock()``.  Please refer to the below
Evaluation section for more detail.

As DAMON incurs very low overhead, such optimizations can be applied not only
offline, but also online.  Also, there is no reason to limit such optimizations
to the user space.  Several parts of the kernel's memory management mechanisms
could be also optimized using DAMON. The reclamation, the THP (de)promotion
decisions, and the compaction would be such a candidates.  Nevertheless,
current version of DAMON is not highly optimized for the online/in-kernel uses.


Mechanisms of DAMON
===================


Basic Access Check
------------------

DAMON basically reports what pages are how frequently accessed.  The report is
passed to users in binary format via a ``result file`` which users can set it's
path.  Note that the frequency is not an absolute number of accesses, but a
relative frequency among the pages of the target workloads.

Users can also control the resolution of the reports by setting two time
intervals, ``sampling interval`` and ``aggregation interval``.  In detail,
DAMON checks access to each page per ``sampling interval``, aggregates the
results (counts the number of the accesses to each page), and reports the
aggregated results per ``aggregation interval``.  For the access check of each
page, DAMON uses the Accessed bits of PTEs.

This is thus similar to the previously mentioned periodic access checks based
mechanisms, which overhead is increasing as the size of the target process
grows.


Region Based Sampling
---------------------

To avoid the unbounded increase of the overhead, DAMON groups a number of
adjacent pages that assumed to have same access frequencies into a region.  As
long as the assumption (pages in a region have 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 and clears
its Accessed bit.  After one more ``sampling interval``, DAMON reads the
Accessed bit of the page and increases the access frequency of the region if
the bit has set meanwhile.  Therefore, the monitoring overhead is controllable
by setting the number of regions.  DAMON allows users to set the minimal and
maximum number of regions for the trade-off.

Except the assumption, this is almost same with the above-mentioned
miniature-like static region based sampling.  In other words, this scheme
cannot preserve the quality of the output if the assumption is not guaranteed.


Adaptive Regions Adjustment
---------------------------

At the beginning of the monitoring, DAMON constructs the initial regions by
evenly splitting the memory mapped address space of the process into the
user-specified minimal number of regions.  In this initial state, the
assumption is normally not kept and thus the quality could be low.  To keep the
assumption as much as possible, DAMON adaptively merges and splits each region.
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 regions if the total number of regions is smaller
than the half of the user-specified maximum number of regions.

In this way, DAMON provides its best-effort quality and minimal overhead while
keeping the bounds users set for their trade-off.


Applying Dynamic Memory Mappings
--------------------------------

Only a number of small parts in the super-huge virtual address space of the
processes is mapped to physical memory and accessed.  Thus, tracking the
unmapped address regions is just wasteful.  However, tracking every memory
mapping change might incur an overhead.  For the reason, DAMON applies the
dynamic memory mapping changes to the tracking regions only for each of an
user-specified time interval (``regions update interval``).


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].


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.
The optimized versions consistently show speedup (2.55x in best case, 1.65x in
average) under memory pressure situation.


Overhead
--------

We also measured the overhead of DAMON.  It was not only under the upperbound
we set, but 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 the adaptive regions adjustment.  We also compared the overhead
with that of the straightforward periodic Accessed bit check-based monitoring,
which checks the access of every page frame.  DAMON's overhead was much smaller
than the straightforward mechanism by 94,242.42x in best case, 3,159.61x in
average.


References
==========

Prototypes of DAMON have introduced by an LPC kernel summit track talk[1] and
two academic papers[2,3].  Please refer to those for more detailed information,
especially the evaluations.

[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
[3] SeongJae Park, Yunjae Lee, Yunhee Kim, Heon Y. Yeom, Profiling Dynamic Data
    Access Patterns with Bounded Overhead and Accuracy. In IEEE International
    Workshop on Foundations and Applications of Self- Systems (FAS 2019), June
    2019.


SeongJae Park (5):
  mm: Introduce Data Access MONitor (DAMON)
  mm/damon: Add debugfs interface
  mm/damon: Add minimal user-space tools
  Documentation/admin-guide/mm: Add a document for DAMON
  mm/damon: Add kunit tests

 .../admin-guide/mm/data_access_monitor.rst    |  235 +++
 Documentation/admin-guide/mm/index.rst        |    1 +
 mm/Kconfig                                    |   23 +
 mm/Makefile                                   |    1 +
 mm/damon-test.h                               |  571 ++++++++
 mm/damon.c                                    | 1266 +++++++++++++++++
 tools/damon/bin2txt.py                        |   64 +
 tools/damon/damn                              |   36 +
 tools/damon/heats.py                          |  358 +++++
 tools/damon/nr_regions.py                     |  116 ++
 tools/damon/record.py                         |  182 +++
 tools/damon/report.py                         |   45 +
 tools/damon/wss.py                            |  121 ++
 13 files changed, 3019 insertions(+)
 create mode 100644 Documentation/admin-guide/mm/data_access_monitor.rst
 create mode 100644 mm/damon-test.h
 create mode 100644 mm/damon.c
 create mode 100644 tools/damon/bin2txt.py
 create mode 100644 tools/damon/damn
 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

-- 
2.17.1

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