Make DAMON sysfs interface to read the user inputs for DAMOS quota goals
and pass those to DAMOS, so that the users can use the quota auto-tuning
feature. It uses the DAMON sysfs interface's user input commit mechanism,
which applies all user inputs for initial starting of DAMON and online
input updates, which can be done by writing 'on' and 'commit' to the
kdamond's 'state' file, respectively. In other words, the user should
periodically write appropriate value to 'current_value' files and 'commit'
command to the 'state' file. 'target_value' files could also be similarly
updated at any time.
Note that the interface is supporting multiple goals while the core logic
supports only one goal. DAMON sysfs interface passes only best feedback
among the given inputs, to avoid making DAMOS too aggressive.
Link: https://lkml.kernel.org/r/20231130023652.50284-4-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Gow <davidgow@google.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Implement DAMON sysfs directories and files for the goals of DAMOS quota.
Those allow users set multiple goals for their aim, with target values.
Users can further enter the current score value for each goal as feedback
for DAMOS.
Note that this commit is implementing only the basic file operations, and
not connecting the files with the DAMOS core logic. Hence writing
something to the files makes no real effect. The following commit will
connect the file operations and the core logic.
Link: https://lkml.kernel.org/r/20231130023652.50284-3-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Gow <davidgow@google.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Patch series "mm/damon: let users feed and tame/auto-tune DAMOS".
Introduce Aim-oriented Feedback-driven DAMOS Aggressiveness Auto-tuning.
It makes DAMOS self-tuned with periodic simple user feedback.
Background: DAMOS Control Difficulty
====================================
DAMOS helps users easily implement access pattern aware system operations.
However, controlling DAMOS in the wild is not that easy.
The basic way for DAMOS control is specifying the target access pattern.
In this approach, the user is assumed to well understand the access
pattern and the characteristics of the system and the workloads. Though
there are useful tools for that, it takes time and effort depending on the
complexity and the dynamicity of the system and the workloads. After all,
the access pattern consists of three ranges, namely the size, the access
rate, and the age of the regions. It means users need to tune six
parameters, which is anyway not a simple task.
One of the worst cases would be DAMOS being too aggressive like a
berserker, and therefore consuming too much system resource and making
unwanted radical system operations. To let users avoid such cases, DAMOS
allows users to set the upper-limit of the schemes' aggressiveness, namely
DAMOS quota. DAMOS further provides its best-effort under the limit by
prioritizing regions based on the access pattern of the regions. For
example, users can ask DAMOS to page out up to 100 MiB of memory regions
per second. Then DAMOS pages out regions that are not accessed for a
longer time (colder) first under the limit. This allows users to set the
target access pattern a bit naive with wider ranges, and focus on tuning
only one parameter, the quota. In other words, the number of parameters
to tune can be reduced from six to one.
Still, however, the optimum value for the quota depends on the system and
the workloads' characteristics, so not that simple. The number of
parameters to tune can also increase again if the user needs to run
multiple schemes.
Aim-oriented Feedback-driven DAMOS Aggressiveness Auto Tuning
=============================================================
Users would use DAMOS since they want to achieve something with it. They
will likely have measurable metrics representing the achievement and the
target number of the metric like SLO, and continuously measure that
anyway. While the additional cost of getting the information is nearly
zero, it could be useful for DAMOS to understand how appropriate its
current aggressiveness is set, and adjust it on its own to make the metric
value more close to the target.
Based on this idea, we introduce a new way of tuning DAMOS with nearly
zero additional effort, namely Aim-oriented Feedback-driven DAMOS
Aggressiveness Auto Tuning. It asks users to provide feedback
representing how well DAMOS is doing relative to the users' aim. Then
DAMOS adjusts its aggressiveness, specifically the quota that provides
the best effort result under the limit, based on the current level of
the aggressiveness and the users' feedback.
Implementation
==============
The implementation asks users to represent the feedback with score
numbers. The scores could be anything including user-space specific
metrics including latency and throughput of special user-space workloads,
and system metrics including free memory ratio, memory pressure stall time
(PSI), and active to inactive LRU lists size ratio. The feedback scores
and the aggressiveness of the given DAMOS scheme are assumed to be
positively proportional, though. Selecting metrics of the assumption is
the users' responsibility.
The core logic uses the below simple feedback loop algorithm to calculate
the next aggressiveness level of the scheme from the current
aggressiveness level and the current feedback (target_score and
current_score). It calculates the compensation for next aggressiveness as
a proportion of current aggressiveness and distance to the target score.
As a result, it arrives at the near-goal state in a short time using big
steps when it's far from the goal, but avoids making unnecessarily radical
changes that could turn out to be a bad decision using small steps when
its near to the goal.
f(n) = max(1, f(n - 1) * ((target_score - current_score) / target_score + 1))
Note that the compensation value becomes negative when it's over
achieving the goal. That's why the feedback metric and the
aggressiveness of the scheme should be positively proportional. The
distance-adaptive speed manipulation is simply applied.
Example Use Cases
=================
If users want to reduce the memory footprint of the system as much as
possible as long as the time spent for handling the resulting memory
pressure is within a threshold, they could use DAMOS scheme that reclaims
cold memory regions aiming for a little level of memory pressure stall
time.
If users want the active/inactive LRU lists well balanced to reduce the
performance impact due to possible future memory pressure, they could use
two schemes. The first one would be set to locate hot pages in the active
LRU list, aiming for a specific active-to-inactive LRU list size ratio,
say, 70%. The second one would be to locate cold pages in the inactive
LRU list, aiming for a specific inactive-to-active LRU list size ratio,
say, 30%. Then, DAMOS will balance the two schemes based on the goal and
feedback.
This aim-oriented auto tuning could also be useful for general
balancing-required access aware system operations such as system memory
auto scaling[3] and tiered memory management[4]. These two example usages
are not what current DAMOS implementation is already supporting, but
require additional DAMOS action developments, though.
Evaluation: subtle memory pressure aiming proactive reclamation
===============================================================
To show if the implementation works as expected, we prepare four different
system configurations on AWS i3.metal instances. The first setup
(original) runs the workload without any DAMOS scheme. The second setup
(not-tuned) runs the workload with a virtual address space-based proactive
reclamation scheme that pages out memory regions that are not accessed for
five seconds or more. The third setup (offline-tuned) runs the same
proactive reclamation DAMOS scheme, but after making it tuned for each
workload offline, using our previous user-space driven automatic tuning
approach, namely DAMOOS[1]. The fourth and final setup (AFDAA) runs the
scheme that is the same as that of 'not-tuned' setup, but aims to keep
0.5% of 'some' memory pressure stall time (PSI) for the last 10 seconds
using the aiming-oriented auto tuning.
For each setup, we run realistic workloads from PARSEC3 and SPLASH-2X
benchmark suites. For each run, we measure RSS and runtime of the
workload, and 'some' memory pressure stall time (PSI) of the system. We
repeat the runs five times and use averaged measurements.
For simple comparison of the results, we normalize the measurements to
those of 'original'. In the case of the PSI, though, the measurement for
'original' was zero, so we normalize the value to that of 'not-tuned'
scheme's result. The normalized results are shown below.
Not-tuned Offline-tuned AFDAA
RSS 0.622688178226118 0.787950678944904 0.740093483278979
runtime 1.11767826657912 1.0564674983585 1.0910833880499
PSI 1 0.727521443794069 0.308498846350299
The 'not-tuned' scheme achieves about 38.7% memory saving but incur about
11.7% runtime slowdown. The 'offline-tuned' scheme achieves about 22.2%
memory saving with about 5.5% runtime slowdown. It also achieves about
28.2% memory pressure stall time saving. AFDAA achieves about 26% memory
saving with about 9.1% runtime slowdown. It also achieves about 69.1%
memory pressure stall time saving. We repeat this test multiple times,
and get consistent results. AFDAA is now integrated in our daily DAMON
performance test setup.
Apparently the aggressiveness of 'AFDAA' setup is somewhere between those
of 'not-tuned' and 'offline-tuned' setup, since its memory saving and
runtime overhead are between those of the other two setups. Actually we
set the memory pressure stall time goal aiming for this middle
aggressiveness. The difference in the two metrics are not significant,
though. However, it shows significant saving of the memory pressure stall
time, which was the goal of the auto-tuning, over the two variants.
Hence, we conclude the automatic tuning is working as expected.
Please note that the AFDAA setup is only for the evaluation, and
therefore intentionally set a bit aggressive. It might not be
appropriate for production environments.
The test code is also available[2], so you could reproduce it on your
system and workloads.
Patches Sequence
================
The first four patches implement the core logic and user interfaces for
the auto tuning. The first patch implements the core logic for the auto
tuning, and the API for DAMOS users in the kernel space. The second
patch implements basic file operations of DAMON sysfs directories and
files that will be used for setting the goals and providing the
feedback. The third patch connects the quota goals files inputs to the
DAMOS core logic. Finally the fourth patch implements a dedicated DAMOS
sysfs command for efficiently committing the quota goals feedback.
Two patches for simple tests of the logic and interfaces follow. The
fifth patch implements the core logic unit test. The sixth patch
implements a selftest for the DAMON Sysfs interface for the goals.
Finally, three patches for documentation follows. The seventh patch
documents the design of the feature. The eighth patch updates the API
doc for the new sysfs files. The final eighth patch updates the usage
document for the features.
References
==========
[1] DAOS paper:
https://www.amazon.science/publications/daos-data-access-aware-operating-system
[2] Evaluation code:
3f884e6119
[3] Memory auto scaling RFC idea:
https://lore.kernel.org/damon/20231112195114.61474-1-sj@kernel.org/
[4] DAMON-based tiered memory management RFC idea:
https://lore.kernel.org/damon/20231112195602.61525-1-sj@kernel.org/
This patch (of 9)
Users can effectively control the upper-limit aggressiveness of DAMOS
schemes using the quota feature. The quota provides best result under the
limit by prioritizing regions based on the access pattern. That said,
finding the best value, which could depend on dynamic characteristics of
the system and the workloads, is still challenging.
Implement a simple feedback-driven tuning mechanism and use it for
automatic tuning of DAMOS quota. The implementation allows users to
provide the feedback by setting a feedback score returning callback
function. Then DAMOS periodically calls the function back and adjusts the
quota based on the return value of the callback and current quota value.
Note that the absolute-value based time/size quotas still work as the
maximum hard limits of the scheme's aggressiveness. The feedback-driven
auto-tuned quota is applied only if it is not exceeding the manually set
maximum limits. Same for the scheme-target access pattern and filters
like other features.
[sj@kernel.org: document get_score_arg field of struct damos_quota]
Link: https://lkml.kernel.org/r/20231204170106.60992-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20231130023652.50284-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20231130023652.50284-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Gow <davidgow@google.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Currently, we only shrink the zswap pool when the user-defined limit is
hit. This means that if we set the limit too high, cold data that are
unlikely to be used again will reside in the pool, wasting precious
memory. It is hard to predict how much zswap space will be needed ahead
of time, as this depends on the workload (specifically, on factors such as
memory access patterns and compressibility of the memory pages).
This patch implements a memcg- and NUMA-aware shrinker for zswap, that is
initiated when there is memory pressure. The shrinker does not have any
parameter that must be tuned by the user, and can be opted in or out on a
per-memcg basis.
Furthermore, to make it more robust for many workloads and prevent
overshrinking (i.e evicting warm pages that might be refaulted into
memory), we build in the following heuristics:
* Estimate the number of warm pages residing in zswap, and attempt to
protect this region of the zswap LRU.
* Scale the number of freeable objects by an estimate of the memory
saving factor. The better zswap compresses the data, the fewer pages
we will evict to swap (as we will otherwise incur IO for relatively
small memory saving).
* During reclaim, if the shrinker encounters a page that is also being
brought into memory, the shrinker will cautiously terminate its
shrinking action, as this is a sign that it is touching the warmer
region of the zswap LRU.
As a proof of concept, we ran the following synthetic benchmark: build the
linux kernel in a memory-limited cgroup, and allocate some cold data in
tmpfs to see if the shrinker could write them out and improved the overall
performance. Depending on the amount of cold data generated, we observe
from 14% to 35% reduction in kernel CPU time used in the kernel builds.
[nphamcs@gmail.com: check shrinker enablement early, use less costly stat flushing]
Link: https://lkml.kernel.org/r/20231206194456.3234203-1-nphamcs@gmail.com
Link: https://lkml.kernel.org/r/20231130194023.4102148-7-nphamcs@gmail.com
Signed-off-by: Nhat Pham <nphamcs@gmail.com>
Acked-by: Johannes Weiner <hannes@cmpxchg.org>
Tested-by: Bagas Sanjaya <bagasdotme@gmail.com>
Cc: Chris Li <chrisl@kernel.org>
Cc: Dan Streetman <ddstreet@ieee.org>
Cc: Domenico Cerasuolo <cerasuolodomenico@gmail.com>
Cc: Michal Hocko <mhocko@kernel.org>
Cc: Muchun Song <muchun.song@linux.dev>
Cc: Roman Gushchin <roman.gushchin@linux.dev>
Cc: Seth Jennings <sjenning@redhat.com>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vitaly Wool <vitaly.wool@konsulko.com>
Cc: Yosry Ahmed <yosryahmed@google.com>
Cc: Chengming Zhou <chengming.zhou@linux.dev>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Patch series "workload-specific and memory pressure-driven zswap
writeback", v8.
There are currently several issues with zswap writeback:
1. There is only a single global LRU for zswap, making it impossible to
perform worload-specific shrinking - an memcg under memory pressure
cannot determine which pages in the pool it owns, and often ends up
writing pages from other memcgs. This issue has been previously
observed in practice and mitigated by simply disabling
memcg-initiated shrinking:
https://lore.kernel.org/all/20230530232435.3097106-1-nphamcs@gmail.com/T/#u
But this solution leaves a lot to be desired, as we still do not
have an avenue for an memcg to free up its own memory locked up in
the zswap pool.
2. We only shrink the zswap pool when the user-defined limit is hit.
This means that if we set the limit too high, cold data that are
unlikely to be used again will reside in the pool, wasting precious
memory. It is hard to predict how much zswap space will be needed
ahead of time, as this depends on the workload (specifically, on
factors such as memory access patterns and compressibility of the
memory pages).
This patch series solves these issues by separating the global zswap LRU
into per-memcg and per-NUMA LRUs, and performs workload-specific (i.e
memcg- and NUMA-aware) zswap writeback under memory pressure. The new
shrinker does not have any parameter that must be tuned by the user, and
can be opted in or out on a per-memcg basis.
As a proof of concept, we ran the following synthetic benchmark: build the
linux kernel in a memory-limited cgroup, and allocate some cold data in
tmpfs to see if the shrinker could write them out and improved the overall
performance. Depending on the amount of cold data generated, we observe
from 14% to 35% reduction in kernel CPU time used in the kernel builds.
This patch (of 6):
The interface of list_lru is based on the assumption that the list node
and the data it represents belong to the same allocated on the correct
node/memcg. While this assumption is valid for existing slab objects LRU
such as dentries and inodes, it is undocumented, and rather inflexible for
certain potential list_lru users (such as the upcoming zswap shrinker and
the THP shrinker). It has caused us a lot of issues during our
development.
This patch changes list_lru interface so that the caller must explicitly
specify numa node and memcg when adding and removing objects. The old
list_lru_add() and list_lru_del() are renamed to list_lru_add_obj() and
list_lru_del_obj(), respectively.
It also extends the list_lru API with a new function, list_lru_putback,
which undoes a previous list_lru_isolate call. Unlike list_lru_add, it
does not increment the LRU node count (as list_lru_isolate does not
decrement the node count). list_lru_putback also allows for explicit
memcg and NUMA node selection.
Link: https://lkml.kernel.org/r/20231130194023.4102148-1-nphamcs@gmail.com
Link: https://lkml.kernel.org/r/20231130194023.4102148-2-nphamcs@gmail.com
Signed-off-by: Nhat Pham <nphamcs@gmail.com>
Suggested-by: Johannes Weiner <hannes@cmpxchg.org>
Acked-by: Johannes Weiner <hannes@cmpxchg.org>
Tested-by: Bagas Sanjaya <bagasdotme@gmail.com>
Cc: Chris Li <chrisl@kernel.org>
Cc: Dan Streetman <ddstreet@ieee.org>
Cc: Domenico Cerasuolo <cerasuolodomenico@gmail.com>
Cc: Michal Hocko <mhocko@kernel.org>
Cc: Muchun Song <muchun.song@linux.dev>
Cc: Roman Gushchin <roman.gushchin@linux.dev>
Cc: Seth Jennings <sjenning@redhat.com>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vitaly Wool <vitaly.wool@konsulko.com>
Cc: Yosry Ahmed <yosryahmed@google.com>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
mtree_range_walk() needed to be updated to avoid checking if there was a
pivot value. On closer examination, the code could avoid setting min or
max in certain scenarios. The commit removes the extra check for
pivot[offset] before setting max and only sets max when necessary. It
also only sets min if it is necessary by checking offset 0 prior to the
loop (as it has always done).
The commit also drops a dead node check since the end of the node will
return the array size when the last slot is occupied (by a potential reuse
in a dead node). The data will be discarded later if the node is marked
dead.
Benchmarking these changes results in an increase in performance of 5.45%
using the BENCH_WALK in the maple tree test code.
Link: https://lkml.kernel.org/r/20231101171629.3612299-13-Liam.Howlett@oracle.com
Signed-off-by: Liam R. Howlett <Liam.Howlett@oracle.com>
Cc: Peng Zhang <zhangpeng.00@bytedance.com>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Since the pivot being set is now reliable, the optimized loop no longer
needs to find the node end. The redundant check for a dead node can also
be avoided as there is no danger of using the wrong pivot since the
results will be thrown out in the case of a dead node by the later check.
This patch also adds a benchmark test for the function to the maple tree
test framework. The benchmark shows an average increase performance of
5.98% over 3 runs with this commit.
Link: https://lkml.kernel.org/r/20231101171629.3612299-12-Liam.Howlett@oracle.com
Signed-off-by: Liam R. Howlett <Liam.Howlett@oracle.com>
Cc: Peng Zhang <zhangpeng.00@bytedance.com>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
__mas_set_range() was created to shortcut resetting the maple state and a
debug check was added to the caller (the vma iterator) to ensure the
internal maple state remains safe to use. Move the debug check from the
vma iterator into the maple tree itself so other users do not incorrectly
use the advanced maple state modification.
Fallout from this change include a large amount of debug setup needed to
be moved to earlier in the header, and the maple_tree.h radix-tree test
code needed to move the inclusion of the header to after the atomic
define. None of those changes have functional changes.
Link: https://lkml.kernel.org/r/20231101171629.3612299-4-Liam.Howlett@oracle.com
Signed-off-by: Liam R. Howlett <Liam.Howlett@oracle.com>
Cc: Peng Zhang <zhangpeng.00@bytedance.com>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Patch series "maple_tree: iterator state changes".
These patches have some general cleanup and a change to separate the maple
state status tracking from the maple state node.
The maple state status change allows for walks to continue from previous
places when the status needs to be recorded to make logical sense for the
next call to the maple state. For instance, it allows for prev/next to
function in a way that better resembles the linked list. It also allows
switch statements to be used to detect missed states during compile, and
the addition of fast-path "active" state is cleaner as an enum.
While making the status change, perf showed some very small (one line)
functions that were not inlined even with the inline key word. Making
these small functions __always_inline is less expensive according to perf.
As part of that change, some inlines have been dropped from larger
functions.
Perf also showed that the commonly used mas_for_each() iterator was
spending a lot of time finding the end of the node. This series
introduces caching of the end of the node in the maple state (and updating
it during writes). This caching along with the inline changes yielded at
23.25% improvement on the BENCH_MAS_FOR_EACH maple tree test framework
benchmark.
I've also included a change to mtree_range_walk and mtree_lookup_walk to
take advantage of Peng's change [1] to the initial pivot setup.
mmtests did not produce any significant gains.
[1] https://lore.kernel.org/all/20230711035444.526-1-zhangpeng.00@bytedance.com/T/#u
This patch (of 12):
Removing the default types from the switch statements will cause compile
warnings on missing cases.
Link: https://lkml.kernel.org/r/20231101171629.3612299-2-Liam.Howlett@oracle.com
Signed-off-by: Liam R. Howlett <Liam.Howlett@oracle.com>
Suggested-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Record and report more information to help us find the cause of the bug
and to help us correlate the error with other system events.
This patch adds recording and showing CPU number and timestamp at
allocation and free (controlled by CONFIG_KASAN_EXTRA_INFO). The
timestamps in the report use the same format and source as printk.
Error occurrence timestamp is already implicit in the printk log, and CPU
number is already shown by dump_stack_lvl, so there is no need to add it.
In order to record CPU number and timestamp at allocation and free,
corresponding members need to be added to the relevant data structures,
which will lead to increased memory consumption.
In Generic KASAN, members are added to struct kasan_track. Since in most
cases, alloc meta is stored in the redzone and free meta is stored in the
object or the redzone, memory consumption will not increase much.
In SW_TAGS KASAN and HW_TAGS KASAN, members are added to struct
kasan_stack_ring_entry. Memory consumption increases as the size of
struct kasan_stack_ring_entry increases (this part of the memory is
allocated by memblock), but since this is configurable, it is up to the
user to choose.
Link: https://lkml.kernel.org/r/VI1P193MB0752BD991325D10E4AB1913599BDA@VI1P193MB0752.EURP193.PROD.OUTLOOK.COM
Signed-off-by: Juntong Deng <juntong.deng@outlook.com>
Cc: Alexander Potapenko <glider@google.com>
Cc: Andrey Konovalov <andreyknvl@gmail.com>
Cc: Andrey Ryabinin <ryabinin.a.a@gmail.com>
Cc: Dmitry Vyukov <dvyukov@google.com>
Cc: Vincenzo Frascino <vincenzo.frascino@arm.com>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
This is a simple listener for memory events that handles counter changes
in runtime. It can be set up for a specific memory cgroup v2.
The output example:
=====
$ /tmp/memcg_event_listener test
Initialized MEMCG events with counters:
MEMCG events:
low: 0
high: 0
max: 0
oom: 0
oom_kill: 0
oom_group_kill: 0
Started monitoring memory events from '/sys/fs/cgroup/test/memory.events'...
Received event in /sys/fs/cgroup/test/memory.events:
*** 1 MEMCG oom_kill event, change counter 0 => 1
Received event in /sys/fs/cgroup/test/memory.events:
*** 1 MEMCG oom_kill event, change counter 1 => 2
Received event in /sys/fs/cgroup/test/memory.events:
*** 1 MEMCG oom_kill event, change counter 2 => 3
Received event in /sys/fs/cgroup/test/memory.events:
*** 1 MEMCG oom_kill event, change counter 3 => 4
Received event in /sys/fs/cgroup/test/memory.events:
*** 2 MEMCG max events, change counter 0 => 2
Received event in /sys/fs/cgroup/test/memory.events:
*** 8 MEMCG max events, change counter 2 => 10
*** 1 MEMCG oom event, change counter 0 => 1
Received event in /sys/fs/cgroup/test/memory.events:
*** 1 MEMCG oom_kill event, change counter 4 => 5
^CExiting memcg event listener...
=====
Link: https://lkml.kernel.org/r/20231123071945.25811-3-ddrokosov@salutedevices.com
Signed-off-by: Dmitry Rokosov <ddrokosov@salutedevices.com>
Cc: Johannes Weiner <hannes@cmpxchg.org>
Cc: Michal Hocko <mhocko@kernel.org>
Cc: Muchun Song <muchun.song@linux.dev>
Cc: Roman Gushchin <roman.gushchin@linux.dev>
Cc: Shakeel Butt <shakeelb@google.com>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
The 8782fb61cc ("mm: pagewalk: Fix race between unmap and page walker")
introduces an assertion to walk_page_range_novma() to make all the users
of page table walker is safe. However, the race only exists for walking
the user page tables. And it is ridiculous to hold a particular user mmap
write lock against the changes of the kernel page tables. So only assert
at least mmap read lock when walking the kernel page tables. And some
users matching this case could downgrade to a mmap read lock to relief the
contention of mmap lock of init_mm, it will be nicer in hugetlb (only
holding mmap read lock) in the next patch.
Link: https://lkml.kernel.org/r/20231127084645.27017-2-songmuchun@bytedance.com
Signed-off-by: Muchun Song <songmuchun@bytedance.com>
Acked-by: Mike Kravetz <mike.kravetz@oracle.com>
Cc: Kefeng Wang <wangkefeng.wang@huawei.com>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
kmap_atomic() has been deprecated in favor of kmap_local_page().
Therefore, replace kmap_atomic() with kmap_local_page() in swapfile.c.
kmap_atomic() is implemented like a kmap_local_page() which also disables
page-faults and preemption (the latter only in !PREEMPT_RT kernels). The
kernel virtual addresses returned by these two API are only valid in the
context of the callers (i.e., they cannot be handed to other threads).
With kmap_local_page() the mappings are per thread and CPU local like in
kmap_atomic(); however, they can handle page-faults and can be called from
any context (including interrupts). The tasks that call kmap_local_page()
can be preempted and, when they are scheduled to run again, the kernel
virtual addresses are restored and are still valid.
In mm/swapfile.c, the blocks of code between the mappings and un-mappings
do not depend on the above-mentioned side effects of kmap_atomic(), so
that the mere replacements of the old API with the new one is all that is
required (i.e., there is no need to explicitly call pagefault_disable()
and/or preempt_disable()).
Link: https://lkml.kernel.org/r/20231127155452.586387-1-fabio.maria.de.francesco@linux.intel.com
Signed-off-by: Fabio M. De Francesco <fabio.maria.de.francesco@linux.intel.com>
Cc: Ira Weiny <ira.weiny@intel.com>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
kmap_atomic() has been deprecated in favor of kmap_local_page().
Therefore, replace kmap_atomic() with kmap_local_page() in
zswap.c.
kmap_atomic() is implemented like a kmap_local_page() which also disables
page-faults and preemption (the latter only in !PREEMPT_RT kernels). The
kernel virtual addresses returned by these two API are only valid in the
context of the callers (i.e., they cannot be handed to other threads).
With kmap_local_page() the mappings are per thread and CPU local like in
kmap_atomic(); however, they can handle page-faults and can be called from
any context (including interrupts). The tasks that call kmap_local_page()
can be preempted and, when they are scheduled to run again, the kernel
virtual addresses are restored and are still valid.
In mm/zswap.c, the blocks of code between the mappings and un-mappings do
not depend on the above-mentioned side effects of kmap_atomic(), so that
the mere replacements of the old API with the new one is all that is
required (i.e., there is no need to explicitly call pagefault_disable()
and/or preempt_disable()).
Link: https://lkml.kernel.org/r/20231127160058.586446-1-fabio.maria.de.francesco@linux.intel.com
Signed-off-by: Fabio M. De Francesco <fabio.maria.de.francesco@linux.intel.com>
Reviewed-by: Nhat Pham <nphamcs@gmail.com>
Acked-by: Chris Li <chrisl@kernel.org> (Google)
Cc: Ira Weiny <ira.weiny@intel.com>
Cc: Seth Jennings <sjenning@redhat.com>
Cc: Dan Streetman <ddstreet@ieee.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
