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Correct to "following" from "followings" in the sentence "The followings
are the read bandwidths and CPU utilizations depending on different affinity
scope settings on ``kcryptd`` measured over five runs."
Signed-off-by: Attreyee Mukherjee <tintinm2017@gmail.com>
Signed-off-by: Jonathan Corbet <corbet@lwn.net>
Link: https://lore.kernel.org/r/20240110185746.24974-1-tintinm2017@gmail.com
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Correct to “boundaries” from “bounaries”
Signed-off-by: Attreyee Mukherjee <tintinm2017@gmail.com>
Acked-by: Tejun Heo <tj@kernel.org>
Signed-off-by: Jonathan Corbet <corbet@lwn.net>
Link: https://lore.kernel.org/r/20231223175316.24951-1-tintinm2017@gmail.com
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alloc_ordered_queue -> alloc_ordered_workqueue
/sys/devices/virtual/WQ_NAME/
-> /sys/devices/virtual/workqueue/WQ_NAME/
Signed-off-by: WangJinchao <wangjinchao@xfusion.com>
Signed-off-by: Tejun Heo <tj@kernel.org>
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While workqueue.default_affinity_scope is writable, it only affects
workqueues which are created afterwards and isn't very useful. Instead,
let's introduce explicit "default" scope and update the effective scope
dynamically when workqueue.default_affinity_scope is changed.
Signed-off-by: Tejun Heo <tj@kernel.org>
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With affinity scopes and their strictness setting added, unbound workqueues
should now be able to cover wide variety of configurations and use cases.
Unfortunately, the performance picture is not entirely straight-forward due
to a trade-off between efficiency and work-conservation in some situations
necessitating manual configuration.
This patch adds "Affinity Scopes and Performance" section to
Documentation/core-api/workqueue.rst which illustrates the trade-off with a
set of experiments and provides some guidelines.
Signed-off-by: Tejun Heo <tj@kernel.org>
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An unbound workqueue can be served by multiple worker_pools to improve
locality. The segmentation is achieved by grouping CPUs into pods. By
default, the cache boundaries according to cpus_share_cache() define the
CPUs are grouped. Let's a workqueue is allowed to run on all CPUs and the
system has two L3 caches. The workqueue would be mapped to two worker_pools
each serving one L3 cache domains.
While this improves locality, because the pod boundaries are strict, it
limits the total bandwidth a given issuer can consume. For example, let's
say there is a thread pinned to a CPU issuing enough work items to saturate
the whole machine. With the machine segmented into two pods, no matter how
many work items it issues, it can only use half of the CPUs on the system.
While this limitation has existed for a very long time, it wasn't very
pronounced because the affinity grouping used to be always by NUMA nodes.
With cache boundaries as the default and support for even finer grained
scopes (smt and cpu), it is now an a lot more pressing problem.
This patch implements non-strict affinity scope where the pod boundaries
aren't enforced strictly. Going back to the previous example, the workqueue
would still be mapped to two worker_pools; however, the affinity enforcement
would be soft. The workers in both pools would have their cpus_allowed set
to the whole machine thus allowing the scheduler to migrate them anywhere on
the machine. However, whenever an idle worker is woken up, the workqueue
code asks the scheduler to bring back the task within the pod if the worker
is outside. ie. work items start executing within its affinity scope but can
be migrated outside as the scheduler sees fit. This removes the hard cap on
utilization while maintaining the benefits of affinity scopes.
After the earlier ->__pod_cpumask changes, the implementation is pretty
simple. When non-strict which is the new default:
* pool_allowed_cpus() returns @pool->attrs->cpumask instead of
->__pod_cpumask so that the workers are allowed to run on any CPU that
the associated workqueues allow.
* If the idle worker task's ->wake_cpu is outside the pod, kick_pool() sets
the field to a CPU within the pod.
This would be the first use of task_struct->wake_cpu outside scheduler
proper, so it isn't clear whether this would be acceptable. However, other
methods of migrating tasks are significantly more expensive and are likely
prohibitively so if we want to do this on every work item. This needs
discussion with scheduler folks.
There is also a race window where setting ->wake_cpu wouldn't be effective
as the target task is still on CPU. However, the window is pretty small and
this being a best-effort optimization, it doesn't seem to warrant more
complexity at the moment.
While the non-strict cache affinity scopes seem to be the best option, the
performance picture interacts with the affinity scope and is a bit
complicated to fully discuss in this patch, so the behavior is made easily
selectable through wqattrs and sysfs and the next patch will add
documentation to discuss performance implications.
v2: pool->attrs->affn_strict is set to true for per-cpu worker_pools.
Signed-off-by: Tejun Heo <tj@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Linus Torvalds <torvalds@linux-foundation.org>
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Add three more affinity scopes - WQ_AFFN_CPU, SMT and CACHE - and make CACHE
the default. The code changes to actually add the additional scopes are
trivial.
Also add module parameter "workqueue.default_affinity_scope" to override the
default scope and "affinity_scope" sysfs file to configure it per workqueue.
wq_dump.py and documentations are updated accordingly.
This enables significant flexibility in configuring how unbound workqueues
behave. If affinity scope is set to "cpu", it'll behave close to a per-cpu
workqueue. On the other hand, "system" removes all locality boundaries.
Many modern machines have multiple L3 caches often while being mostly
uniform in terms of memory access. Thus, workqueue's previous behavior of
spreading work items in each NUMA node had negative performance implications
from unncessarily crossing L3 boundaries between issue and execution.
However, picking a finer grained affinity scope also has a downside in that
an issuer in one group can't utilize CPUs in other groups.
While dependent on the specifics of workload, there's usually a noticeable
penalty in crossing L3 boundaries, so let's default to CACHE. This issue
will be further addressed and documented with examples in future patches.
Signed-off-by: Tejun Heo <tj@kernel.org>
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configuration
Lack of visibility has always been a pain point for workqueues. While the
recently added wq_monitor.py improved the situation, it's still difficult to
understand what worker pools are active in the system, how workqueues map to
them and why. The lack of visibility into how workqueues are configured is
going to become more noticeable as workqueue improves locality awareness and
provides more mechanisms to customize locality related behaviors.
Now that the basic framework for more flexible locality support is in place,
this is a good time to improve the situation. This patch adds
tools/workqueues/wq_dump.py which prints out the topology configuration,
worker pools and how workqueues are mapped to pools. Read the command's help
message for more details.
Signed-off-by: Tejun Heo <tj@kernel.org>
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A pwq (pool_workqueue) represents an association between a workqueue and a
worker_pool. When a work item is queued, the workqueue selects the pwq to
use, which in turn determines the pool, and queues the work item to the pool
through the pwq. pwq is also what implements the maximum concurrency limit -
@max_active.
As a per-cpu workqueue should be assocaited with a different worker_pool on
each CPU, it always had per-cpu pwq's that are accessed through wq->cpu_pwq.
However, unbound workqueues were sharing a pwq within each NUMA node by
default. The sharing has several downsides:
* Because @max_active is per-pwq, the meaning of @max_active changes
depending on the machine configuration and whether workqueue NUMA locality
support is enabled.
* Makes per-cpu and unbound code deviate.
* Gets in the way of making workqueue CPU locality awareness more flexible.
This patch makes unbound workqueues use per-cpu pwq's the same way per-cpu
workqueues do by making the following changes:
* wq->numa_pwq_tbl[] is removed and unbound workqueues now use wq->cpu_pwq
just like per-cpu workqueues. wq->cpu_pwq is now RCU protected for unbound
workqueues.
* numa_pwq_tbl_install() is renamed to install_unbound_pwq() and installs
the specified pwq to the target CPU's wq->cpu_pwq.
* apply_wqattrs_prepare() now always allocates a separate pwq for each CPU
unless the workqueue is ordered. If ordered, all CPUs use wq->dfl_pwq.
This makes the return value of wq_calc_node_cpumask() unnecessary. It now
returns void.
* @max_active now means the same thing for both per-cpu and unbound
workqueues. WQ_UNBOUND_MAX_ACTIVE now equals WQ_MAX_ACTIVE and
documentation is updated accordingly. WQ_UNBOUND_MAX_ACTIVE is no longer
used in workqueue implementation and will be removed later.
* All unbound pwq operations which used to be per-numa-node are now per-cpu.
For most unbound workqueue users, this shouldn't cause noticeable changes.
Work item issue and completion will be a small bit faster, flush_workqueue()
would become a bit more expensive, and the total concurrency limit would
likely become higher. All @max_active==1 use cases are currently being
audited for conversion into alloc_ordered_workqueue() and they shouldn't be
affected once the audit and conversion is complete.
One area where the behavior change may be more noticeable is
workqueue_congested() as the reported congestion state is now per CPU
instead of NUMA node. There are only two users of this interface -
drivers/infiniband/hw/hfi1 and net/smc. Maintainers of both subsystems are
cc'd. Inputs on the behavior change would be very much appreciated.
Signed-off-by: Tejun Heo <tj@kernel.org>
Acked-by: Dennis Dalessandro <dennis.dalessandro@cornelisnetworks.com>
Cc: Jason Gunthorpe <jgg@ziepe.ca>
Cc: Leon Romanovsky <leon@kernel.org>
Cc: Karsten Graul <kgraul@linux.ibm.com>
Cc: Wenjia Zhang <wenjia@linux.ibm.com>
Cc: Jan Karcher <jaka@linux.ibm.com>
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Now that wq_worker_tick() is there, we can easily track the rough CPU time
consumption of each workqueue by charging the whole tick whenever a tick
hits an active workqueue. While not super accurate, it provides reasonable
visibility into the workqueues that consume a lot of CPU cycles.
wq_monitor.py is updated to report the per-workqueue CPU times.
v2: wq_monitor.py was using "cputime" as the key when outputting in json
format. Use "cpu_time" instead for consistency with other fields.
Signed-off-by: Tejun Heo <tj@kernel.org>
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If a per-cpu work item hogs the CPU, it can prevent other work items from
starting through concurrency management. A per-cpu workqueue which intends
to host such CPU-hogging work items can choose to not participate in
concurrency management by setting %WQ_CPU_INTENSIVE; however, this can be
error-prone and difficult to debug when missed.
This patch adds an automatic CPU usage based detection. If a
concurrency-managed work item consumes more CPU time than the threshold
(10ms by default) continuously without intervening sleeps, wq_worker_tick()
which is called from scheduler_tick() will detect the condition and
automatically mark it CPU_INTENSIVE.
The mechanism isn't foolproof:
* Detection depends on tick hitting the work item. Getting preempted at the
right timings may allow a violating work item to evade detection at least
temporarily.
* nohz_full CPUs may not be running ticks and thus can fail detection.
* Even when detection is working, the 10ms detection delays can add up if
many CPU-hogging work items are queued at the same time.
However, in vast majority of cases, this should be able to detect violations
reliably and provide reasonable protection with a small increase in code
complexity.
If some work items trigger this condition repeatedly, the bigger problem
likely is the CPU being saturated with such per-cpu work items and the
solution would be making them UNBOUND. The next patch will add a debug
mechanism to help spot such cases.
v4: Documentation for workqueue.cpu_intensive_thresh_us added to
kernel-parameters.txt.
v3: Switch to use wq_worker_tick() instead of hooking into preemptions as
suggested by Peter.
v2: Lai pointed out that wq_worker_stopping() also needs to be called from
preemption and rtlock paths and an earlier patch was updated
accordingly. This patch adds a comment describing the risk of infinte
recursions and how they're avoided.
Signed-off-by: Tejun Heo <tj@kernel.org>
Acked-by: Peter Zijlstra <peterz@infradead.org>
Cc: Linus Torvalds <torvalds@linux-foundation.org>
Cc: Lai Jiangshan <jiangshanlai@gmail.com>
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Currently, the only way to peer into workqueue operations is through
tracing. While possible, it isn't easy or convenient to monitor
per-workqueue behaviors over time this way. Let's add pwq->stats[] that
track relevant events and a drgn monitoring script -
tools/workqueue/wq_monitor.py.
It's arguable whether this needs to be configurable. However, it currently
only has several counters and the runtime overhead shouldn't be noticeable
given that they're on pwq's which are per-cpu on per-cpu workqueues and
per-numa-node on unbound ones. Let's keep it simple for the time being.
v2: Patch reordered to earlier with fewer fields. Field will be added back
gradually. Help message improved.
Signed-off-by: Tejun Heo <tj@kernel.org>
Cc: Lai Jiangshan <jiangshanlai@gmail.com>
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The canonical location for the tracefs filesystem is at /sys/kernel/tracing.
But, from Documentation/trace/ftrace.rst:
Before 4.1, all ftrace tracing control files were within the debugfs
file system, which is typically located at /sys/kernel/debug/tracing.
For backward compatibility, when mounting the debugfs file system,
the tracefs file system will be automatically mounted at:
/sys/kernel/debug/tracing
Many parts of Documentation still reference this older debugfs path, so
let's update them to avoid confusion.
Signed-off-by: Ross Zwisler <zwisler@google.com>
Reviewed-by: Steven Rostedt (Google) <rostedt@goodmis.org>
Link: https://lore.kernel.org/r/20230125213251.2013791-1-zwisler@google.com
Signed-off-by: Jonathan Corbet <corbet@lwn.net>
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The current doc of workqueue API suggests that work items are
non-reentrant: any work item is guaranteed to be executed by at most one
worker system-wide at any given time. However this is not true, the
following case can cause a work item W executed by two workers at
the same time:
queue_work_on(0, WQ1, W);
// after a worker picks up W and clear the pending bit
queue_work_on(1, WQ2, W);
// workers on CPU0 and CPU1 will execute W in the same time.
, which means the non-reentrance of a work item is conditional, and
Lai Jiangshan provided a nice summary[1] of the conditions, therefore
use it to describe a work item instance and improve the doc.
[1]: https://lore.kernel.org/lkml/CAJhGHyDudet_xyNk=8xnuO2==o-u06s0E0GZVP4Q67nmQ84Ceg@mail.gmail.com/
Suggested-by: Matthew Wilcox <willy@infradead.org>
Suggested-by: Tejun Heo <tj@kernel.org>
Signed-off-by: Boqun Feng <boqun.feng@gmail.com>
Signed-off-by: Tejun Heo <tj@kernel.org>
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As there's already a rst file with workqueue markups, containing
part of them, move the other definitions, in order to avoid
warnings with Sphinx.
Signed-off-by: Mauro Carvalho Chehab <mchehab+huawei@kernel.org>
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Clean up workqueue.rst:
- fix minor typos
- put '@' after `` instead of preceding them (one place)
- use "CPU" instead of "cpu" in text consistently
- quote one function name
Signed-off-by: Randy Dunlap <rdunlap@infradead.org>
Cc: Tejun Heo <tj@kernel.org>
Cc: Florian Mickler <florian@mickler.org>
Signed-off-by: Tejun Heo <tj@kernel.org>
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NUMA rework of workqueue made the combination of max_active of 1 and
WQ_UNBOUND insufficient to guarantee ST behavior system wide.
alloc_ordered_queue should now be used instead.
Signed-off-by: Alexei Potashnik <alexei@purestorage.com>
Signed-off-by: Tejun Heo <tj@kernel.org>
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... and move to Documentation/core-api folder.
Signed-off-by: Silvio Fricke <silvio.fricke@gmail.com>
Signed-off-by: Jonathan Corbet <corbet@lwn.net>
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