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workqueue: Changes for v6.6 

* Unbound workqueues now support more flexible affinity scopes. The default
   behavior is to soft-affine according to last level cache boundaries. A
   work item queued from a given LLC is executed by a worker running on the
   same LLC but the worker may be moved across cache boundaries as the
   scheduler sees fit. On machines which multiple L3 caches, which are
   becoming more popular along with chiplet designs, this improves cache
   locality while not harming work conservation too much.
 
   Unbound workqueues are now also a lot more flexible in terms of execution
   affinity. Differeing levels of affinity scopes are supported and both the
   default and per-workqueue affinity settings can be modified dynamically.
   This should help working around amny of sub-optimal behaviors observed
   recently with asymmetric ARM CPUs.
 
   This involved signficant restructuring of workqueue code. Nothing was
   reported yet but there's some risk of subtle regressions. Should keep an
   eye out.
 
 * Rescuer workers now has more identifiable comms.
 
 * workqueue.unbound_cpus added so that CPUs which can be used by workqueue
   can be constrained early during boot.
 
 * Now that all the in-tree users have been flushed out, trigger warning if
   system-wide workqueues are flushed.
 
 * One pull commit from for-6.5-fixes to avoid cascading conflicts in the
   affinity scope patchset.
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Merge tag 'wq-for-6.6' of git://git.kernel.org/pub/scm/linux/kernel/git/tj/wq

Pull workqueue updates from Tejun Heo:

 - Unbound workqueues now support more flexible affinity scopes.

   The default behavior is to soft-affine according to last level cache
   boundaries. A work item queued from a given LLC is executed by a
   worker running on the same LLC but the worker may be moved across
   cache boundaries as the scheduler sees fit. On machines which
   multiple L3 caches, which are becoming more popular along with
   chiplet designs, this improves cache locality while not harming work
   conservation too much.

   Unbound workqueues are now also a lot more flexible in terms of
   execution affinity. Differeing levels of affinity scopes are
   supported and both the default and per-workqueue affinity settings
   can be modified dynamically. This should help working around amny of
   sub-optimal behaviors observed recently with asymmetric ARM CPUs.

   This involved signficant restructuring of workqueue code. Nothing was
   reported yet but there's some risk of subtle regressions. Should keep
   an eye out.

 - Rescuer workers now has more identifiable comms.

 - workqueue.unbound_cpus added so that CPUs which can be used by
   workqueue can be constrained early during boot.

 - Now that all the in-tree users have been flushed out, trigger warning
   if system-wide workqueues are flushed.

* tag 'wq-for-6.6' of git://git.kernel.org/pub/scm/linux/kernel/git/tj/wq: (31 commits)
  workqueue: fix data race with the pwq->stats[] increment
  workqueue: Rename rescuer kworker
  workqueue: Make default affinity_scope dynamically updatable
  workqueue: Add "Affinity Scopes and Performance" section to documentation
  workqueue: Implement non-strict affinity scope for unbound workqueues
  workqueue: Add workqueue_attrs->__pod_cpumask
  workqueue: Factor out need_more_worker() check and worker wake-up
  workqueue: Factor out work to worker assignment and collision handling
  workqueue: Add multiple affinity scopes and interface to select them
  workqueue: Modularize wq_pod_type initialization
  workqueue: Add tools/workqueue/wq_dump.py which prints out workqueue configuration
  workqueue: Generalize unbound CPU pods
  workqueue: Factor out clearing of workqueue-only attrs fields
  workqueue: Factor out actual cpumask calculation to reduce subtlety in wq_update_pod()
  workqueue: Initialize unbound CPU pods later in the boot
  workqueue: Move wq_pod_init() below workqueue_init()
  workqueue: Rename NUMA related names to use pod instead
  workqueue: Rename workqueue_attrs->no_numa to ->ordered
  workqueue: Make unbound workqueues to use per-cpu pool_workqueues
  workqueue: Call wq_update_unbound_numa() on all CPUs in NUMA node on CPU hotplug
  ...
This commit is contained in:
Linus Torvalds 2023-09-01 16:06:32 -07:00
parent 7716f383a5 fe48ba7dae
commit bd30fe6a7d
8 changed files with 1512 additions and 804 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

28
Documentation/admin-guide/kernel-parameters.txt
View File

@ -7076,6 +7076,13 @@
disables both lockup detectors. Default is 10
seconds.
workqueue.unbound_cpus=
[KNL,SMP] Specify to constrain one or some CPUs
to use in unbound workqueues.
Format: <cpu-list>
By default, all online CPUs are available for
unbound workqueues.
workqueue.watchdog_thresh=
If CONFIG_WQ_WATCHDOG is configured, workqueue can
warn stall conditions and dump internal state to
@ -7097,15 +7104,6 @@
threshold repeatedly. They are likely good
candidates for using WQ_UNBOUND workqueues instead.
workqueue.disable_numa
By default, all work items queued to unbound
workqueues are affine to the NUMA nodes they're
issued on, which results in better behavior in
general. If NUMA affinity needs to be disabled for
whatever reason, this option can be used. Note
that this also can be controlled per-workqueue for
workqueues visible under /sys/bus/workqueue/.
workqueue.power_efficient
Per-cpu workqueues are generally preferred because
they show better performance thanks to cache
@ -7121,6 +7119,18 @@
The default value of this parameter is determined by
the config option CONFIG_WQ_POWER_EFFICIENT_DEFAULT.
workqueue.default_affinity_scope=
Select the default affinity scope to use for unbound
workqueues. Can be one of "cpu", "smt", "cache",
"numa" and "system". Default is "cache". For more
information, see the Affinity Scopes section in
Documentation/core-api/workqueue.rst.
This can be changed after boot by writing to the
matching /sys/module/workqueue/parameters file. All
workqueues with the "default" affinity scope will be
updated accordignly.
workqueue.debug_force_rr_cpu
Workqueue used to implicitly guarantee that work
items queued without explicit CPU specified are put

356
Documentation/core-api/workqueue.rst
View File

@ -1,6 +1,6 @@
====================================
Concurrency Managed Workqueue (cmwq)
====================================
=========
Workqueue
=========
:Date: September, 2010
:Author: Tejun Heo <tj@kernel.org>
@ -25,8 +25,8 @@ there is no work item left on the workqueue the worker becomes idle.
When a new work item gets queued, the worker begins executing again.
Why cmwq?
=========
Why Concurrency Managed Workqueue?
==================================
In the original wq implementation, a multi threaded (MT) wq had one
worker thread per CPU and a single threaded (ST) wq had one worker
@ -220,17 +220,16 @@ resources, scheduled and executed.
``max_active``
--------------
``@max_active`` determines the maximum number of execution contexts
per CPU which can be assigned to the work items of a wq. For example,
with ``@max_active`` of 16, at most 16 work items of the wq can be
executing at the same time per CPU.
``@max_active`` determines the maximum number of execution contexts per
CPU which can be assigned to the work items of a wq. For example, with
``@max_active`` of 16, at most 16 work items of the wq can be executing
at the same time per CPU. This is always a per-CPU attribute, even for
unbound workqueues.
Currently, for a bound wq, the maximum limit for ``@max_active`` is
512 and the default value used when 0 is specified is 256. For an
unbound wq, the limit is higher of 512 and 4 *
``num_possible_cpus()``. These values are chosen sufficiently high
such that they are not the limiting factor while providing protection
in runaway cases.
The maximum limit for ``@max_active`` is 512 and the default value used
when 0 is specified is 256. These values are chosen sufficiently high
such that they are not the limiting factor while providing protection in
runaway cases.
The number of active work items of a wq is usually regulated by the
users of the wq, more specifically, by how many work items the users
@ -348,27 +347,346 @@ Guidelines
level of locality in wq operations and work item execution.
Affinity Scopes
===============
An unbound workqueue groups CPUs according to its affinity scope to improve
cache locality. For example, if a workqueue is using the default affinity
scope of "cache", it will group CPUs according to last level cache
boundaries. A work item queued on the workqueue will be assigned to a worker
on one of the CPUs which share the last level cache with the issuing CPU.
Once started, the worker may or may not be allowed to move outside the scope
depending on the ``affinity_strict`` setting of the scope.
Workqueue currently supports the following affinity scopes.
``default``
Use the scope in module parameter ``workqueue.default_affinity_scope``
which is always set to one of the scopes below.
``cpu``
CPUs are not grouped. A work item issued on one CPU is processed by a
worker on the same CPU. This makes unbound workqueues behave as per-cpu
workqueues without concurrency management.
``smt``
CPUs are grouped according to SMT boundaries. This usually means that the
logical threads of each physical CPU core are grouped together.
``cache``
CPUs are grouped according to cache boundaries. Which specific cache
boundary is used is determined by the arch code. L3 is used in a lot of
cases. This is the default affinity scope.
``numa``
CPUs are grouped according to NUMA bounaries.
``system``
All CPUs are put in the same group. Workqueue makes no effort to process a
work item on a CPU close to the issuing CPU.
The default affinity scope can be changed with the module parameter
``workqueue.default_affinity_scope`` and a specific workqueue's affinity
scope can be changed using ``apply_workqueue_attrs()``.
If ``WQ_SYSFS`` is set, the workqueue will have the following affinity scope
related interface files under its ``/sys/devices/virtual/WQ_NAME/``
directory.
``affinity_scope``
Read to see the current affinity scope. Write to change.
When default is the current scope, reading this file will also show the
current effective scope in parentheses, for example, ``default (cache)``.
``affinity_strict``
0 by default indicating that affinity scopes are not strict. When a work
item starts execution, workqueue makes a best-effort attempt to ensure
that the worker is inside its affinity scope, which is called
repatriation. Once started, the scheduler is free to move the worker
anywhere in the system as it sees fit. This enables benefiting from scope
locality while still being able to utilize other CPUs if necessary and
available.
If set to 1, all workers of the scope are guaranteed always to be in the
scope. This may be useful when crossing affinity scopes has other
implications, for example, in terms of power consumption or workload
isolation. Strict NUMA scope can also be used to match the workqueue
behavior of older kernels.
Affinity Scopes and Performance
===============================
It'd be ideal if an unbound workqueue's behavior is optimal for vast
majority of use cases without further tuning. Unfortunately, in the current
kernel, there exists a pronounced trade-off between locality and utilization
necessitating explicit configurations when workqueues are heavily used.
Higher locality leads to higher efficiency where more work is performed for
the same number of consumed CPU cycles. However, higher locality may also
cause lower overall system utilization if the work items are not spread
enough across the affinity scopes by the issuers. The following performance
testing with dm-crypt clearly illustrates this trade-off.
The tests are run on a CPU with 12-cores/24-threads split across four L3
caches (AMD Ryzen 9 3900x). CPU clock boost is turned off for consistency.
``/dev/dm-0`` is a dm-crypt device created on NVME SSD (Samsung 990 PRO) and
opened with ``cryptsetup`` with default settings.
Scenario 1: Enough issuers and work spread across the machine
-------------------------------------------------------------
The command used: ::
$ fio --filename=/dev/dm-0 --direct=1 --rw=randrw --bs=32k --ioengine=libaio \
--iodepth=64 --runtime=60 --numjobs=24 --time_based --group_reporting \
--name=iops-test-job --verify=sha512
There are 24 issuers, each issuing 64 IOs concurrently. ``--verify=sha512``
makes ``fio`` generate and read back the content each time which makes
execution locality matter between the issuer and ``kcryptd``. The followings
are the read bandwidths and CPU utilizations depending on different affinity
scope settings on ``kcryptd`` measured over five runs. Bandwidths are in
MiBps, and CPU util in percents.
.. list-table::
:widths: 16 20 20
:header-rows: 1
* - Affinity
- Bandwidth (MiBps)
- CPU util (%)
* - system
- 1159.40 ±1.34
- 99.31 ±0.02
* - cache
- 1166.40 ±0.89
- 99.34 ±0.01
* - cache (strict)
- 1166.00 ±0.71
- 99.35 ±0.01
With enough issuers spread across the system, there is no downside to
"cache", strict or otherwise. All three configurations saturate the whole
machine but the cache-affine ones outperform by 0.6% thanks to improved
locality.
Scenario 2: Fewer issuers, enough work for saturation
-----------------------------------------------------
The command used: ::
$ fio --filename=/dev/dm-0 --direct=1 --rw=randrw --bs=32k \
--ioengine=libaio --iodepth=64 --runtime=60 --numjobs=8 \
--time_based --group_reporting --name=iops-test-job --verify=sha512
The only difference from the previous scenario is ``--numjobs=8``. There are
a third of the issuers but is still enough total work to saturate the
system.
.. list-table::
:widths: 16 20 20
:header-rows: 1
* - Affinity
- Bandwidth (MiBps)
- CPU util (%)
* - system
- 1155.40 ±0.89
- 97.41 ±0.05
* - cache
- 1154.40 ±1.14
- 96.15 ±0.09
* - cache (strict)
- 1112.00 ±4.64
- 93.26 ±0.35
This is more than enough work to saturate the system. Both "system" and
"cache" are nearly saturating the machine but not fully. "cache" is using
less CPU but the better efficiency puts it at the same bandwidth as
"system".
Eight issuers moving around over four L3 cache scope still allow "cache
(strict)" to mostly saturate the machine but the loss of work conservation
is now starting to hurt with 3.7% bandwidth loss.
Scenario 3: Even fewer issuers, not enough work to saturate
-----------------------------------------------------------
The command used: ::
$ fio --filename=/dev/dm-0 --direct=1 --rw=randrw --bs=32k \
--ioengine=libaio --iodepth=64 --runtime=60 --numjobs=4 \
--time_based --group_reporting --name=iops-test-job --verify=sha512
Again, the only difference is ``--numjobs=4``. With the number of issuers
reduced to four, there now isn't enough work to saturate the whole system
and the bandwidth becomes dependent on completion latencies.
.. list-table::
:widths: 16 20 20
:header-rows: 1
* - Affinity
- Bandwidth (MiBps)
- CPU util (%)
* - system
- 993.60 ±1.82
- 75.49 ±0.06
* - cache
- 973.40 ±1.52
- 74.90 ±0.07
* - cache (strict)
- 828.20 ±4.49
- 66.84 ±0.29
Now, the tradeoff between locality and utilization is clearer. "cache" shows
2% bandwidth loss compared to "system" and "cache (struct)" whopping 20%.
Conclusion and Recommendations
------------------------------
In the above experiments, the efficiency advantage of the "cache" affinity
scope over "system" is, while consistent and noticeable, small. However, the
impact is dependent on the distances between the scopes and may be more
pronounced in processors with more complex topologies.
While the loss of work-conservation in certain scenarios hurts, it is a lot
better than "cache (strict)" and maximizing workqueue utilization is
unlikely to be the common case anyway. As such, "cache" is the default
affinity scope for unbound pools.
* As there is no one option which is great for most cases, workqueue usages
that may consume a significant amount of CPU are recommended to configure
the workqueues using ``apply_workqueue_attrs()`` and/or enable
``WQ_SYSFS``.
* An unbound workqueue with strict "cpu" affinity scope behaves the same as
``WQ_CPU_INTENSIVE`` per-cpu workqueue. There is no real advanage to the
latter and an unbound workqueue provides a lot more flexibility.
* Affinity scopes are introduced in Linux v6.5. To emulate the previous
behavior, use strict "numa" affinity scope.
* The loss of work-conservation in non-strict affinity scopes is likely
originating from the scheduler. There is no theoretical reason why the
kernel wouldn't be able to do the right thing and maintain
work-conservation in most cases. As such, it is possible that future
scheduler improvements may make most of these tunables unnecessary.
Examining Configuration
=======================
Use tools/workqueue/wq_dump.py to examine unbound CPU affinity
configuration, worker pools and how workqueues map to the pools: ::
$ tools/workqueue/wq_dump.py
Affinity Scopes
===============
wq_unbound_cpumask=0000000f
CPU
nr_pods 4
pod_cpus [0]=00000001 [1]=00000002 [2]=00000004 [3]=00000008
pod_node [0]=0 [1]=0 [2]=1 [3]=1
cpu_pod [0]=0 [1]=1 [2]=2 [3]=3
SMT
nr_pods 4
pod_cpus [0]=00000001 [1]=00000002 [2]=00000004 [3]=00000008
pod_node [0]=0 [1]=0 [2]=1 [3]=1
cpu_pod [0]=0 [1]=1 [2]=2 [3]=3
CACHE (default)
nr_pods 2
pod_cpus [0]=00000003 [1]=0000000c
pod_node [0]=0 [1]=1
cpu_pod [0]=0 [1]=0 [2]=1 [3]=1
NUMA
nr_pods 2
pod_cpus [0]=00000003 [1]=0000000c
pod_node [0]=0 [1]=1
cpu_pod [0]=0 [1]=0 [2]=1 [3]=1
SYSTEM
nr_pods 1
pod_cpus [0]=0000000f
pod_node [0]=-1
cpu_pod [0]=0 [1]=0 [2]=0 [3]=0
Worker Pools
============
pool[00] ref= 1 nice= 0 idle/workers= 4/ 4 cpu= 0
pool[01] ref= 1 nice=-20 idle/workers= 2/ 2 cpu= 0
pool[02] ref= 1 nice= 0 idle/workers= 4/ 4 cpu= 1
pool[03] ref= 1 nice=-20 idle/workers= 2/ 2 cpu= 1
pool[04] ref= 1 nice= 0 idle/workers= 4/ 4 cpu= 2
pool[05] ref= 1 nice=-20 idle/workers= 2/ 2 cpu= 2
pool[06] ref= 1 nice= 0 idle/workers= 3/ 3 cpu= 3
pool[07] ref= 1 nice=-20 idle/workers= 2/ 2 cpu= 3
pool[08] ref=42 nice= 0 idle/workers= 6/ 6 cpus=0000000f
pool[09] ref=28 nice= 0 idle/workers= 3/ 3 cpus=00000003
pool[10] ref=28 nice= 0 idle/workers= 17/ 17 cpus=0000000c
pool[11] ref= 1 nice=-20 idle/workers= 1/ 1 cpus=0000000f
pool[12] ref= 2 nice=-20 idle/workers= 1/ 1 cpus=00000003
pool[13] ref= 2 nice=-20 idle/workers= 1/ 1 cpus=0000000c
Workqueue CPU -> pool
=====================
[ workqueue \ CPU 0 1 2 3 dfl]
events percpu 0 2 4 6
events_highpri percpu 1 3 5 7
events_long percpu 0 2 4 6
events_unbound unbound 9 9 10 10 8
events_freezable percpu 0 2 4 6
events_power_efficient percpu 0 2 4 6
events_freezable_power_ percpu 0 2 4 6
rcu_gp percpu 0 2 4 6
rcu_par_gp percpu 0 2 4 6
slub_flushwq percpu 0 2 4 6
netns ordered 8 8 8 8 8
...
See the command's help message for more info.
Monitoring
==========
Use tools/workqueue/wq_monitor.py to monitor workqueue operations: ::
$ tools/workqueue/wq_monitor.py events
total infl CPUtime CPUhog CMwake mayday rescued
total infl CPUtime CPUhog CMW/RPR mayday rescued
events 18545 0 6.1 0 5 - -
events_highpri 8 0 0.0 0 0 - -
events_long 3 0 0.0 0 0 - -
events_unbound 38306 0 0.1 - - - -
events_unbound 38306 0 0.1 - 7 - -
events_freezable 0 0 0.0 0 0 - -
events_power_efficient 29598 0 0.2 0 0 - -
events_freezable_power_ 10 0 0.0 0 0 - -
sock_diag_events 0 0 0.0 0 0 - -
total infl CPUtime CPUhog CMwake mayday rescued
total infl CPUtime CPUhog CMW/RPR mayday rescued
events 18548 0 6.1 0 5 - -
events_highpri 8 0 0.0 0 0 - -
events_long 3 0 0.0 0 0 - -
events_unbound 38322 0 0.1 - - - -
events_unbound 38322 0 0.1 - 7 - -
events_freezable 0 0 0.0 0 0 - -
events_power_efficient 29603 0 0.2 0 0 - -
events_freezable_power_ 10 0 0.0 0 0 - -

115
include/linux/workqueue.h
View File

@ -125,6 +125,17 @@ struct rcu_work {
struct workqueue_struct *wq;
};
enum wq_affn_scope {
WQ_AFFN_DFL, /* use system default */
WQ_AFFN_CPU, /* one pod per CPU */
WQ_AFFN_SMT, /* one pod poer SMT */
WQ_AFFN_CACHE, /* one pod per LLC */
WQ_AFFN_NUMA, /* one pod per NUMA node */
WQ_AFFN_SYSTEM, /* one pod across the whole system */
WQ_AFFN_NR_TYPES,
};
/**
* struct workqueue_attrs - A struct for workqueue attributes.
*
@ -138,17 +149,58 @@ struct workqueue_attrs {
/**
* @cpumask: allowed CPUs
*
* Work items in this workqueue are affine to these CPUs and not allowed
* to execute on other CPUs. A pool serving a workqueue must have the
* same @cpumask.
*/
cpumask_var_t cpumask;
/**
* @no_numa: disable NUMA affinity
* @__pod_cpumask: internal attribute used to create per-pod pools
*
* Unlike other fields, ``no_numa`` isn't a property of a worker_pool. It
* only modifies how :c:func:`apply_workqueue_attrs` select pools and thus
* doesn't participate in pool hash calculations or equality comparisons.
* Internal use only.
*
* Per-pod unbound worker pools are used to improve locality. Always a
* subset of ->cpumask. A workqueue can be associated with multiple
* worker pools with disjoint @__pod_cpumask's. Whether the enforcement
* of a pool's @__pod_cpumask is strict depends on @affn_strict.
*/
bool no_numa;
cpumask_var_t __pod_cpumask;
/**
* @affn_strict: affinity scope is strict
*
* If clear, workqueue will make a best-effort attempt at starting the
* worker inside @__pod_cpumask but the scheduler is free to migrate it
* outside.
*
* If set, workers are only allowed to run inside @__pod_cpumask.
*/
bool affn_strict;
/*
* Below fields aren't properties of a worker_pool. They only modify how
* :c:func:`apply_workqueue_attrs` select pools and thus don't
* participate in pool hash calculations or equality comparisons.
*/
/**
* @affn_scope: unbound CPU affinity scope
*
* CPU pods are used to improve execution locality of unbound work
* items. There are multiple pod types, one for each wq_affn_scope, and
* every CPU in the system belongs to one pod in every pod type. CPUs
* that belong to the same pod share the worker pool. For example,
* selecting %WQ_AFFN_NUMA makes the workqueue use a separate worker
* pool for each NUMA node.
*/
enum wq_affn_scope affn_scope;
/**
* @ordered: work items must be executed one by one in queueing order
*/
bool ordered;
};
static inline struct delayed_work *to_delayed_work(struct work_struct *work)
@ -343,14 +395,10 @@ enum {
__WQ_ORDERED_EXPLICIT = 1 << 19, /* internal: alloc_ordered_workqueue() */
WQ_MAX_ACTIVE = 512, /* I like 512, better ideas? */
WQ_MAX_UNBOUND_PER_CPU = 4, /* 4 * #cpus for unbound wq */
WQ_UNBOUND_MAX_ACTIVE = WQ_MAX_ACTIVE,
WQ_DFL_ACTIVE = WQ_MAX_ACTIVE / 2,
};
/* unbound wq's aren't per-cpu, scale max_active according to #cpus */
#define WQ_UNBOUND_MAX_ACTIVE \
max_t(int, WQ_MAX_ACTIVE, num_possible_cpus() * WQ_MAX_UNBOUND_PER_CPU)
/*
* System-wide workqueues which are always present.
*
@ -391,7 +439,7 @@ extern struct workqueue_struct *system_freezable_power_efficient_wq;
* alloc_workqueue - allocate a workqueue
* @fmt: printf format for the name of the workqueue
* @flags: WQ_* flags
* @max_active: max in-flight work items, 0 for default
* @max_active: max in-flight work items per CPU, 0 for default
* remaining args: args for @fmt
*
* Allocate a workqueue with the specified parameters. For detailed
@ -569,6 +617,7 @@ static inline bool schedule_work(struct work_struct *work)
/*
* Detect attempt to flush system-wide workqueues at compile time when possible.
* Warn attempt to flush system-wide workqueues at runtime.
*
* See https://lkml.kernel.org/r/49925af7-78a8-a3dd-bce6-cfc02e1a9236@I-love.SAKURA.ne.jp
* for reasons and steps for converting system-wide workqueues into local workqueues.
@ -576,52 +625,13 @@ static inline bool schedule_work(struct work_struct *work)
extern void __warn_flushing_systemwide_wq(void)
__compiletime_warning("Please avoid flushing system-wide workqueues.");
/**
* flush_scheduled_work - ensure that any scheduled work has run to completion.
*
* Forces execution of the kernel-global workqueue and blocks until its
* completion.
*
* It's very easy to get into trouble if you don't take great care.
* Either of the following situations will lead to deadlock:
*
* One of the work items currently on the workqueue needs to acquire
* a lock held by your code or its caller.
*
* Your code is running in the context of a work routine.
*
* They will be detected by lockdep when they occur, but the first might not
* occur very often. It depends on what work items are on the workqueue and
* what locks they need, which you have no control over.
*
* In most situations flushing the entire workqueue is overkill; you merely
* need to know that a particular work item isn't queued and isn't running.
* In such cases you should use cancel_delayed_work_sync() or
* cancel_work_sync() instead.
*
* Please stop calling this function! A conversion to stop flushing system-wide
* workqueues is in progress. This function will be removed after all in-tree
* users stopped calling this function.
*/
/*
* The background of commit 771c035372a036f8 ("deprecate the
* '__deprecated' attribute warnings entirely and for good") is that,
* since Linus builds all modules between every single pull he does,
* the standard kernel build needs to be _clean_ in order to be able to
* notice when new problems happen. Therefore, don't emit warning while
* there are in-tree users.
*/
/* Please stop using this function, for this function will be removed in near future. */
#define flush_scheduled_work() \
({ \
if (0) \
__warn_flushing_systemwide_wq(); \
__warn_flushing_systemwide_wq(); \
__flush_workqueue(system_wq); \
})
/*
* Although there is no longer in-tree caller, for now just emit warning
* in order to give out-of-tree callers time to update.
*/
#define flush_workqueue(wq) \
({ \
struct workqueue_struct *_wq = (wq); \
@ -714,5 +724,6 @@ int workqueue_offline_cpu(unsigned int cpu);
void __init workqueue_init_early(void);
void __init workqueue_init(void);
void __init workqueue_init_topology(void);
#endif

1
init/main.c
View File

@ -1540,6 +1540,7 @@ static noinline void __init kernel_init_freeable(void)
smp_init();
sched_init_smp();
workqueue_init_topology();
padata_init();
page_alloc_init_late();

1616
kernel/workqueue.c
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File diff suppressed because it is too large Load Diff

2
kernel/workqueue_internal.h
View File

@ -48,7 +48,7 @@ struct worker {
/* A: runs through worker->node */
unsigned long last_active; /* K: last active timestamp */
unsigned int flags; /* X: flags */
unsigned int flags; /* L: flags */
int id; /* I: worker id */
/*

177
tools/workqueue/wq_dump.py Normal file
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@ -0,0 +1,177 @@
#!/usr/bin/env drgn
#
# Copyright (C) 2023 Tejun Heo <tj@kernel.org>
# Copyright (C) 2023 Meta Platforms, Inc. and affiliates.
desc = """
This is a drgn script to show the current workqueue configuration. For more
info on drgn, visit https://github.com/osandov/drgn.
Affinity Scopes
===============
Shows the CPUs that can be used for unbound workqueues and how they will be
grouped by each available affinity type. For each type:
nr_pods number of CPU pods in the affinity type
pod_cpus CPUs in each pod
pod_node NUMA node for memory allocation for each pod
cpu_pod pod that each CPU is associated to
Worker Pools
============
Lists all worker pools indexed by their ID. For each pool:
ref number of pool_workqueue's associated with this pool
nice nice value of the worker threads in the pool
idle number of idle workers
workers number of all workers
cpu CPU the pool is associated with (per-cpu pool)
cpus CPUs the workers in the pool can run on (unbound pool)
Workqueue CPU -> pool
=====================
Lists all workqueues along with their type and worker pool association. For
each workqueue:
NAME TYPE[,FLAGS] POOL_ID...
NAME name of the workqueue
TYPE percpu, unbound or ordered
FLAGS S: strict affinity scope
POOL_ID worker pool ID associated with each possible CPU
"""
import sys
import drgn
from drgn.helpers.linux.list import list_for_each_entry,list_empty
from drgn.helpers.linux.percpu import per_cpu_ptr
from drgn.helpers.linux.cpumask import for_each_cpu,for_each_possible_cpu
from drgn.helpers.linux.idr import idr_for_each
import argparse
parser = argparse.ArgumentParser(description=desc,
formatter_class=argparse.RawTextHelpFormatter)
args = parser.parse_args()
def err(s):
print(s, file=sys.stderr, flush=True)
sys.exit(1)
def cpumask_str(cpumask):
output = ""
base = 0
v = 0
for cpu in for_each_cpu(cpumask[0]):
while cpu - base >= 32:
output += f'{hex(v)} '
base += 32
v = 0
v |= 1 << (cpu - base)
if v > 0:
output += f'{v:08x}'
return output.strip()
worker_pool_idr = prog['worker_pool_idr']
workqueues = prog['workqueues']
wq_unbound_cpumask = prog['wq_unbound_cpumask']
wq_pod_types = prog['wq_pod_types']
wq_affn_dfl = prog['wq_affn_dfl']
wq_affn_names = prog['wq_affn_names']
WQ_UNBOUND = prog['WQ_UNBOUND']
WQ_ORDERED = prog['__WQ_ORDERED']
WQ_MEM_RECLAIM = prog['WQ_MEM_RECLAIM']
WQ_AFFN_CPU = prog['WQ_AFFN_CPU']
WQ_AFFN_SMT = prog['WQ_AFFN_SMT']
WQ_AFFN_CACHE = prog['WQ_AFFN_CACHE']
WQ_AFFN_NUMA = prog['WQ_AFFN_NUMA']
WQ_AFFN_SYSTEM = prog['WQ_AFFN_SYSTEM']
print('Affinity Scopes')
print('===============')
print(f'wq_unbound_cpumask={cpumask_str(wq_unbound_cpumask)}')
def print_pod_type(pt):
print(f' nr_pods {pt.nr_pods.value_()}')
print(' pod_cpus', end='')
for pod in range(pt.nr_pods):
print(f' [{pod}]={cpumask_str(pt.pod_cpus[pod])}', end='')
print('')
print(' pod_node', end='')
for pod in range(pt.nr_pods):
print(f' [{pod}]={pt.pod_node[pod].value_()}', end='')
print('')
print(f' cpu_pod ', end='')
for cpu in for_each_possible_cpu(prog):
print(f' [{cpu}]={pt.cpu_pod[cpu].value_()}', end='')
print('')
for affn in [WQ_AFFN_CPU, WQ_AFFN_SMT, WQ_AFFN_CACHE, WQ_AFFN_NUMA, WQ_AFFN_SYSTEM]:
print('')
print(f'{wq_affn_names[affn].string_().decode().upper()}{" (default)" if affn == wq_affn_dfl else ""}')
print_pod_type(wq_pod_types[affn])
print('')
print('Worker Pools')
print('============')
max_pool_id_len = 0
max_ref_len = 0
for pi, pool in idr_for_each(worker_pool_idr):
pool = drgn.Object(prog, 'struct worker_pool', address=pool)
max_pool_id_len = max(max_pool_id_len, len(f'{pi}'))
max_ref_len = max(max_ref_len, len(f'{pool.refcnt.value_()}'))
for pi, pool in idr_for_each(worker_pool_idr):
pool = drgn.Object(prog, 'struct worker_pool', address=pool)
print(f'pool[{pi:0{max_pool_id_len}}] ref={pool.refcnt.value_():{max_ref_len}} nice={pool.attrs.nice.value_():3} ', end='')
print(f'idle/workers={pool.nr_idle.value_():3}/{pool.nr_workers.value_():3} ', end='')
if pool.cpu >= 0:
print(f'cpu={pool.cpu.value_():3}', end='')
else:
print(f'cpus={cpumask_str(pool.attrs.cpumask)}', end='')
print(f' pod_cpus={cpumask_str(pool.attrs.__pod_cpumask)}', end='')
if pool.attrs.affn_strict:
print(' strict', end='')
print('')
print('')
print('Workqueue CPU -> pool')
print('=====================')
print('[ workqueue \ type CPU', end='')
for cpu in for_each_possible_cpu(prog):
print(f' {cpu:{max_pool_id_len}}', end='')
print(' dfl]')
for wq in list_for_each_entry('struct workqueue_struct', workqueues.address_of_(), 'list'):
print(f'{wq.name.string_().decode()[-24:]:24}', end='')
if wq.flags & WQ_UNBOUND:
if wq.flags & WQ_ORDERED:
print(' ordered ', end='')
else:
print(' unbound', end='')
if wq.unbound_attrs.affn_strict:
print(',S ', end='')
else:
print(' ', end='')
else:
print(' percpu ', end='')
for cpu in for_each_possible_cpu(prog):
pool_id = per_cpu_ptr(wq.cpu_pwq, cpu)[0].pool.id.value_()
field_len = max(len(str(cpu)), max_pool_id_len)
print(f' {pool_id:{field_len}}', end='')
if wq.flags & WQ_UNBOUND:
print(f' {wq.dfl_pwq.pool.id.value_():{max_pool_id_len}}', end='')
print('')

21
tools/workqueue/wq_monitor.py
View File

@ -20,8 +20,11 @@ https://github.com/osandov/drgn.
and got excluded from concurrency management to avoid stalling
other work items.
CMwake The number of concurrency-management wake-ups while executing a
work item of the workqueue.
CMW/RPR For per-cpu workqueues, the number of concurrency-management
wake-ups while executing a work item of the workqueue. For
unbound workqueues, the number of times a worker was repatriated
to its affinity scope after being migrated to an off-scope CPU by
the scheduler.
mayday The number of times the rescuer was requested while waiting for
new worker creation.
@ -65,6 +68,7 @@ PWQ_STAT_COMPLETED = prog['PWQ_STAT_COMPLETED'] # work items completed exec
PWQ_STAT_CPU_TIME = prog['PWQ_STAT_CPU_TIME'] # total CPU time consumed
PWQ_STAT_CPU_INTENSIVE = prog['PWQ_STAT_CPU_INTENSIVE'] # wq_cpu_intensive_thresh_us violations
PWQ_STAT_CM_WAKEUP = prog['PWQ_STAT_CM_WAKEUP'] # concurrency-management worker wakeups
PWQ_STAT_REPATRIATED = prog['PWQ_STAT_REPATRIATED'] # unbound workers brought back into scope
PWQ_STAT_MAYDAY = prog['PWQ_STAT_MAYDAY'] # maydays to rescuer
PWQ_STAT_RESCUED = prog['PWQ_STAT_RESCUED'] # linked work items executed by rescuer
PWQ_NR_STATS = prog['PWQ_NR_STATS']
@ -89,22 +93,25 @@ class WqStats:
'cpu_time' : self.stats[PWQ_STAT_CPU_TIME],
'cpu_intensive' : self.stats[PWQ_STAT_CPU_INTENSIVE],
'cm_wakeup' : self.stats[PWQ_STAT_CM_WAKEUP],
'repatriated' : self.stats[PWQ_STAT_REPATRIATED],
'mayday' : self.stats[PWQ_STAT_MAYDAY],
'rescued' : self.stats[PWQ_STAT_RESCUED], }
def table_header_str():
return f'{"":>24} {"total":>8} {"infl":>5} {"CPUtime":>8} '\
f'{"CPUitsv":>7} {"CMwake":>7} {"mayday":>7} {"rescued":>7}'
f'{"CPUitsv":>7} {"CMW/RPR":>7} {"mayday":>7} {"rescued":>7}'
def table_row_str(self):
cpu_intensive = '-'
cm_wakeup = '-'
cmw_rpr = '-'
mayday = '-'
rescued = '-'
if not self.unbound:
if self.unbound:
cmw_rpr = str(self.stats[PWQ_STAT_REPATRIATED]);
else:
cpu_intensive = str(self.stats[PWQ_STAT_CPU_INTENSIVE])
cm_wakeup = str(self.stats[PWQ_STAT_CM_WAKEUP])
cmw_rpr = str(self.stats[PWQ_STAT_CM_WAKEUP])
if self.mem_reclaim:
mayday = str(self.stats[PWQ_STAT_MAYDAY])
@ -115,7 +122,7 @@ class WqStats:
f'{max(self.stats[PWQ_STAT_STARTED] - self.stats[PWQ_STAT_COMPLETED], 0):5} ' \
f'{self.stats[PWQ_STAT_CPU_TIME] / 1000000:8.1f} ' \
f'{cpu_intensive:>7} ' \
f'{cm_wakeup:>7} ' \
f'{cmw_rpr:>7} ' \
f'{mayday:>7} ' \
f'{rescued:>7} '
return out.rstrip(':')