63c5484e74
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>
567 lines
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ReStructuredText
567 lines
21 KiB
ReStructuredText
====================================
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Concurrency Managed Workqueue (cmwq)
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====================================
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:Date: September, 2010
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:Author: Tejun Heo <tj@kernel.org>
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:Author: Florian Mickler <florian@mickler.org>
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Introduction
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============
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There are many cases where an asynchronous process execution context
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is needed and the workqueue (wq) API is the most commonly used
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mechanism for such cases.
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When such an asynchronous execution context is needed, a work item
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describing which function to execute is put on a queue. An
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independent thread serves as the asynchronous execution context. The
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queue is called workqueue and the thread is called worker.
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While there are work items on the workqueue the worker executes the
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functions associated with the work items one after the other. When
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there is no work item left on the workqueue the worker becomes idle.
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When a new work item gets queued, the worker begins executing again.
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Why cmwq?
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=========
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In the original wq implementation, a multi threaded (MT) wq had one
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worker thread per CPU and a single threaded (ST) wq had one worker
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thread system-wide. A single MT wq needed to keep around the same
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number of workers as the number of CPUs. The kernel grew a lot of MT
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wq users over the years and with the number of CPU cores continuously
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rising, some systems saturated the default 32k PID space just booting
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up.
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Although MT wq wasted a lot of resource, the level of concurrency
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provided was unsatisfactory. The limitation was common to both ST and
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MT wq albeit less severe on MT. Each wq maintained its own separate
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worker pool. An MT wq could provide only one execution context per CPU
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while an ST wq one for the whole system. Work items had to compete for
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those very limited execution contexts leading to various problems
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including proneness to deadlocks around the single execution context.
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The tension between the provided level of concurrency and resource
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usage also forced its users to make unnecessary tradeoffs like libata
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choosing to use ST wq for polling PIOs and accepting an unnecessary
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limitation that no two polling PIOs can progress at the same time. As
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MT wq don't provide much better concurrency, users which require
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higher level of concurrency, like async or fscache, had to implement
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their own thread pool.
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Concurrency Managed Workqueue (cmwq) is a reimplementation of wq with
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focus on the following goals.
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* Maintain compatibility with the original workqueue API.
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* Use per-CPU unified worker pools shared by all wq to provide
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flexible level of concurrency on demand without wasting a lot of
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resource.
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* Automatically regulate worker pool and level of concurrency so that
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the API users don't need to worry about such details.
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The Design
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==========
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In order to ease the asynchronous execution of functions a new
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abstraction, the work item, is introduced.
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A work item is a simple struct that holds a pointer to the function
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that is to be executed asynchronously. Whenever a driver or subsystem
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wants a function to be executed asynchronously it has to set up a work
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item pointing to that function and queue that work item on a
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workqueue.
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Special purpose threads, called worker threads, execute the functions
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off of the queue, one after the other. If no work is queued, the
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worker threads become idle. These worker threads are managed in so
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called worker-pools.
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The cmwq design differentiates between the user-facing workqueues that
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subsystems and drivers queue work items on and the backend mechanism
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which manages worker-pools and processes the queued work items.
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There are two worker-pools, one for normal work items and the other
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for high priority ones, for each possible CPU and some extra
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worker-pools to serve work items queued on unbound workqueues - the
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number of these backing pools is dynamic.
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Subsystems and drivers can create and queue work items through special
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workqueue API functions as they see fit. They can influence some
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aspects of the way the work items are executed by setting flags on the
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workqueue they are putting the work item on. These flags include
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things like CPU locality, concurrency limits, priority and more. To
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get a detailed overview refer to the API description of
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``alloc_workqueue()`` below.
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When a work item is queued to a workqueue, the target worker-pool is
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determined according to the queue parameters and workqueue attributes
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and appended on the shared worklist of the worker-pool. For example,
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unless specifically overridden, a work item of a bound workqueue will
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be queued on the worklist of either normal or highpri worker-pool that
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is associated to the CPU the issuer is running on.
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For any worker pool implementation, managing the concurrency level
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(how many execution contexts are active) is an important issue. cmwq
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tries to keep the concurrency at a minimal but sufficient level.
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Minimal to save resources and sufficient in that the system is used at
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its full capacity.
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Each worker-pool bound to an actual CPU implements concurrency
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management by hooking into the scheduler. The worker-pool is notified
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whenever an active worker wakes up or sleeps and keeps track of the
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number of the currently runnable workers. Generally, work items are
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not expected to hog a CPU and consume many cycles. That means
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maintaining just enough concurrency to prevent work processing from
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stalling should be optimal. As long as there are one or more runnable
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workers on the CPU, the worker-pool doesn't start execution of a new
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work, but, when the last running worker goes to sleep, it immediately
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schedules a new worker so that the CPU doesn't sit idle while there
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are pending work items. This allows using a minimal number of workers
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without losing execution bandwidth.
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Keeping idle workers around doesn't cost other than the memory space
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for kthreads, so cmwq holds onto idle ones for a while before killing
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them.
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For unbound workqueues, the number of backing pools is dynamic.
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Unbound workqueue can be assigned custom attributes using
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``apply_workqueue_attrs()`` and workqueue will automatically create
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backing worker pools matching the attributes. The responsibility of
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regulating concurrency level is on the users. There is also a flag to
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mark a bound wq to ignore the concurrency management. Please refer to
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the API section for details.
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Forward progress guarantee relies on that workers can be created when
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more execution contexts are necessary, which in turn is guaranteed
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through the use of rescue workers. All work items which might be used
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on code paths that handle memory reclaim are required to be queued on
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wq's that have a rescue-worker reserved for execution under memory
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pressure. Else it is possible that the worker-pool deadlocks waiting
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for execution contexts to free up.
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Application Programming Interface (API)
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=======================================
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``alloc_workqueue()`` allocates a wq. The original
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``create_*workqueue()`` functions are deprecated and scheduled for
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removal. ``alloc_workqueue()`` takes three arguments - ``@name``,
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``@flags`` and ``@max_active``. ``@name`` is the name of the wq and
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also used as the name of the rescuer thread if there is one.
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A wq no longer manages execution resources but serves as a domain for
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forward progress guarantee, flush and work item attributes. ``@flags``
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and ``@max_active`` control how work items are assigned execution
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resources, scheduled and executed.
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``flags``
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---------
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``WQ_UNBOUND``
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Work items queued to an unbound wq are served by the special
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worker-pools which host workers which are not bound to any
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specific CPU. This makes the wq behave as a simple execution
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context provider without concurrency management. The unbound
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worker-pools try to start execution of work items as soon as
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possible. Unbound wq sacrifices locality but is useful for
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the following cases.
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* Wide fluctuation in the concurrency level requirement is
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expected and using bound wq may end up creating large number
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of mostly unused workers across different CPUs as the issuer
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hops through different CPUs.
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* Long running CPU intensive workloads which can be better
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managed by the system scheduler.
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``WQ_FREEZABLE``
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A freezable wq participates in the freeze phase of the system
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suspend operations. Work items on the wq are drained and no
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new work item starts execution until thawed.
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``WQ_MEM_RECLAIM``
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All wq which might be used in the memory reclaim paths **MUST**
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have this flag set. The wq is guaranteed to have at least one
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execution context regardless of memory pressure.
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``WQ_HIGHPRI``
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Work items of a highpri wq are queued to the highpri
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worker-pool of the target cpu. Highpri worker-pools are
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served by worker threads with elevated nice level.
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Note that normal and highpri worker-pools don't interact with
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each other. Each maintains its separate pool of workers and
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implements concurrency management among its workers.
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``WQ_CPU_INTENSIVE``
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Work items of a CPU intensive wq do not contribute to the
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concurrency level. In other words, runnable CPU intensive
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work items will not prevent other work items in the same
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worker-pool from starting execution. This is useful for bound
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work items which are expected to hog CPU cycles so that their
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execution is regulated by the system scheduler.
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Although CPU intensive work items don't contribute to the
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concurrency level, start of their executions is still
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regulated by the concurrency management and runnable
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non-CPU-intensive work items can delay execution of CPU
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intensive work items.
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This flag is meaningless for unbound wq.
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``max_active``
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--------------
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``@max_active`` determines the maximum number of execution contexts per
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CPU which can be assigned to the work items of a wq. For example, with
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``@max_active`` of 16, at most 16 work items of the wq can be executing
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at the same time per CPU. This is always a per-CPU attribute, even for
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unbound workqueues.
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The maximum limit for ``@max_active`` is 512 and the default value used
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when 0 is specified is 256. These values are chosen sufficiently high
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such that they are not the limiting factor while providing protection in
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runaway cases.
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The number of active work items of a wq is usually regulated by the
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users of the wq, more specifically, by how many work items the users
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may queue at the same time. Unless there is a specific need for
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throttling the number of active work items, specifying '0' is
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recommended.
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Some users depend on the strict execution ordering of ST wq. The
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combination of ``@max_active`` of 1 and ``WQ_UNBOUND`` used to
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achieve this behavior. Work items on such wq were always queued to the
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unbound worker-pools and only one work item could be active at any given
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time thus achieving the same ordering property as ST wq.
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In the current implementation the above configuration only guarantees
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ST behavior within a given NUMA node. Instead ``alloc_ordered_queue()`` should
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be used to achieve system-wide ST behavior.
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Example Execution Scenarios
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===========================
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The following example execution scenarios try to illustrate how cmwq
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behave under different configurations.
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Work items w0, w1, w2 are queued to a bound wq q0 on the same CPU.
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w0 burns CPU for 5ms then sleeps for 10ms then burns CPU for 5ms
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again before finishing. w1 and w2 burn CPU for 5ms then sleep for
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10ms.
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Ignoring all other tasks, works and processing overhead, and assuming
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simple FIFO scheduling, the following is one highly simplified version
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of possible sequences of events with the original wq. ::
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TIME IN MSECS EVENT
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0 w0 starts and burns CPU
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5 w0 sleeps
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15 w0 wakes up and burns CPU
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20 w0 finishes
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20 w1 starts and burns CPU
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25 w1 sleeps
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35 w1 wakes up and finishes
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35 w2 starts and burns CPU
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40 w2 sleeps
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50 w2 wakes up and finishes
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And with cmwq with ``@max_active`` >= 3, ::
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TIME IN MSECS EVENT
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0 w0 starts and burns CPU
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5 w0 sleeps
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5 w1 starts and burns CPU
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10 w1 sleeps
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10 w2 starts and burns CPU
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15 w2 sleeps
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15 w0 wakes up and burns CPU
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20 w0 finishes
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20 w1 wakes up and finishes
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25 w2 wakes up and finishes
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If ``@max_active`` == 2, ::
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TIME IN MSECS EVENT
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0 w0 starts and burns CPU
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5 w0 sleeps
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5 w1 starts and burns CPU
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10 w1 sleeps
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15 w0 wakes up and burns CPU
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20 w0 finishes
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20 w1 wakes up and finishes
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20 w2 starts and burns CPU
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25 w2 sleeps
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35 w2 wakes up and finishes
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Now, let's assume w1 and w2 are queued to a different wq q1 which has
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``WQ_CPU_INTENSIVE`` set, ::
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TIME IN MSECS EVENT
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0 w0 starts and burns CPU
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5 w0 sleeps
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5 w1 and w2 start and burn CPU
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10 w1 sleeps
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15 w2 sleeps
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15 w0 wakes up and burns CPU
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20 w0 finishes
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20 w1 wakes up and finishes
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25 w2 wakes up and finishes
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Guidelines
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==========
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* Do not forget to use ``WQ_MEM_RECLAIM`` if a wq may process work
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items which are used during memory reclaim. Each wq with
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``WQ_MEM_RECLAIM`` set has an execution context reserved for it. If
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there is dependency among multiple work items used during memory
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reclaim, they should be queued to separate wq each with
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``WQ_MEM_RECLAIM``.
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* Unless strict ordering is required, there is no need to use ST wq.
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* Unless there is a specific need, using 0 for @max_active is
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recommended. In most use cases, concurrency level usually stays
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well under the default limit.
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* A wq serves as a domain for forward progress guarantee
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(``WQ_MEM_RECLAIM``, flush and work item attributes. Work items
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which are not involved in memory reclaim and don't need to be
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flushed as a part of a group of work items, and don't require any
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special attribute, can use one of the system wq. There is no
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difference in execution characteristics between using a dedicated wq
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and a system wq.
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* Unless work items are expected to consume a huge amount of CPU
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cycles, using a bound wq is usually beneficial due to the increased
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level of locality in wq operations and work item execution.
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Affinity Scopes
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===============
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An unbound workqueue groups CPUs according to its affinity scope to improve
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cache locality. For example, if a workqueue is using the default affinity
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scope of "cache", it will group CPUs according to last level cache
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boundaries. A work item queued on the workqueue will be processed by a
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worker running on one of the CPUs which share the last level cache with the
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issuing CPU.
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Workqueue currently supports the following five affinity scopes.
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``cpu``
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CPUs are not grouped. A work item issued on one CPU is processed by a
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worker on the same CPU. This makes unbound workqueues behave as per-cpu
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workqueues without concurrency management.
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``smt``
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CPUs are grouped according to SMT boundaries. This usually means that the
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logical threads of each physical CPU core are grouped together.
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``cache``
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CPUs are grouped according to cache boundaries. Which specific cache
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boundary is used is determined by the arch code. L3 is used in a lot of
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cases. This is the default affinity scope.
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``numa``
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CPUs are grouped according to NUMA bounaries.
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``system``
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All CPUs are put in the same group. Workqueue makes no effort to process a
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work item on a CPU close to the issuing CPU.
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The default affinity scope can be changed with the module parameter
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``workqueue.default_affinity_scope`` and a specific workqueue's affinity
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scope can be changed using ``apply_workqueue_attrs()``.
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If ``WQ_SYSFS`` is set, the workqueue will have the following affinity scope
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related interface files under its ``/sys/devices/virtual/WQ_NAME/``
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directory.
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``affinity_scope``
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Read to see the current affinity scope. Write to change.
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Examining Configuration
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=======================
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Use tools/workqueue/wq_dump.py to examine unbound CPU affinity
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configuration, worker pools and how workqueues map to the pools: ::
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$ tools/workqueue/wq_dump.py
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Affinity Scopes
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===============
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wq_unbound_cpumask=0000000f
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CPU
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nr_pods 4
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pod_cpus [0]=00000001 [1]=00000002 [2]=00000004 [3]=00000008
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pod_node [0]=0 [1]=0 [2]=1 [3]=1
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cpu_pod [0]=0 [1]=1 [2]=2 [3]=3
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SMT
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nr_pods 4
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pod_cpus [0]=00000001 [1]=00000002 [2]=00000004 [3]=00000008
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pod_node [0]=0 [1]=0 [2]=1 [3]=1
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cpu_pod [0]=0 [1]=1 [2]=2 [3]=3
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CACHE (default)
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nr_pods 2
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pod_cpus [0]=00000003 [1]=0000000c
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pod_node [0]=0 [1]=1
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cpu_pod [0]=0 [1]=0 [2]=1 [3]=1
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NUMA
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nr_pods 2
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pod_cpus [0]=00000003 [1]=0000000c
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pod_node [0]=0 [1]=1
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cpu_pod [0]=0 [1]=0 [2]=1 [3]=1
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SYSTEM
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nr_pods 1
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pod_cpus [0]=0000000f
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pod_node [0]=-1
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cpu_pod [0]=0 [1]=0 [2]=0 [3]=0
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Worker Pools
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============
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pool[00] ref= 1 nice= 0 idle/workers= 4/ 4 cpu= 0
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pool[01] ref= 1 nice=-20 idle/workers= 2/ 2 cpu= 0
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pool[02] ref= 1 nice= 0 idle/workers= 4/ 4 cpu= 1
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pool[03] ref= 1 nice=-20 idle/workers= 2/ 2 cpu= 1
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pool[04] ref= 1 nice= 0 idle/workers= 4/ 4 cpu= 2
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pool[05] ref= 1 nice=-20 idle/workers= 2/ 2 cpu= 2
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pool[06] ref= 1 nice= 0 idle/workers= 3/ 3 cpu= 3
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pool[07] ref= 1 nice=-20 idle/workers= 2/ 2 cpu= 3
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pool[08] ref=42 nice= 0 idle/workers= 6/ 6 cpus=0000000f
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pool[09] ref=28 nice= 0 idle/workers= 3/ 3 cpus=00000003
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pool[10] ref=28 nice= 0 idle/workers= 17/ 17 cpus=0000000c
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pool[11] ref= 1 nice=-20 idle/workers= 1/ 1 cpus=0000000f
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pool[12] ref= 2 nice=-20 idle/workers= 1/ 1 cpus=00000003
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pool[13] ref= 2 nice=-20 idle/workers= 1/ 1 cpus=0000000c
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Workqueue CPU -> pool
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=====================
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[ workqueue \ CPU 0 1 2 3 dfl]
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events percpu 0 2 4 6
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events_highpri percpu 1 3 5 7
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events_long percpu 0 2 4 6
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events_unbound unbound 9 9 10 10 8
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events_freezable percpu 0 2 4 6
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events_power_efficient percpu 0 2 4 6
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events_freezable_power_ percpu 0 2 4 6
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rcu_gp percpu 0 2 4 6
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rcu_par_gp percpu 0 2 4 6
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slub_flushwq percpu 0 2 4 6
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netns ordered 8 8 8 8 8
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...
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See the command's help message for more info.
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Monitoring
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==========
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Use tools/workqueue/wq_monitor.py to monitor workqueue operations: ::
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$ tools/workqueue/wq_monitor.py events
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total infl CPUtime CPUhog CMwake 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_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
|
|
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_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 - -
|
|
sock_diag_events 0 0 0.0 0 0 - -
|
|
|
|
...
|
|
|
|
See the command's help message for more info.
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|
|
|
|
|
Debugging
|
|
=========
|
|
|
|
Because the work functions are executed by generic worker threads
|
|
there are a few tricks needed to shed some light on misbehaving
|
|
workqueue users.
|
|
|
|
Worker threads show up in the process list as: ::
|
|
|
|
root 5671 0.0 0.0 0 0 ? S 12:07 0:00 [kworker/0:1]
|
|
root 5672 0.0 0.0 0 0 ? S 12:07 0:00 [kworker/1:2]
|
|
root 5673 0.0 0.0 0 0 ? S 12:12 0:00 [kworker/0:0]
|
|
root 5674 0.0 0.0 0 0 ? S 12:13 0:00 [kworker/1:0]
|
|
|
|
If kworkers are going crazy (using too much cpu), there are two types
|
|
of possible problems:
|
|
|
|
1. Something being scheduled in rapid succession
|
|
2. A single work item that consumes lots of cpu cycles
|
|
|
|
The first one can be tracked using tracing: ::
|
|
|
|
$ echo workqueue:workqueue_queue_work > /sys/kernel/tracing/set_event
|
|
$ cat /sys/kernel/tracing/trace_pipe > out.txt
|
|
(wait a few secs)
|
|
^C
|
|
|
|
If something is busy looping on work queueing, it would be dominating
|
|
the output and the offender can be determined with the work item
|
|
function.
|
|
|
|
For the second type of problems it should be possible to just check
|
|
the stack trace of the offending worker thread. ::
|
|
|
|
$ cat /proc/THE_OFFENDING_KWORKER/stack
|
|
|
|
The work item's function should be trivially visible in the stack
|
|
trace.
|
|
|
|
|
|
Non-reentrance Conditions
|
|
=========================
|
|
|
|
Workqueue guarantees that a work item cannot be re-entrant if the following
|
|
conditions hold after a work item gets queued:
|
|
|
|
1. The work function hasn't been changed.
|
|
2. No one queues the work item to another workqueue.
|
|
3. The work item hasn't been reinitiated.
|
|
|
|
In other words, if the above conditions hold, the work item is guaranteed to be
|
|
executed by at most one worker system-wide at any given time.
|
|
|
|
Note that requeuing the work item (to the same queue) in the self function
|
|
doesn't break these conditions, so it's safe to do. Otherwise, caution is
|
|
required when breaking the conditions inside a work function.
|
|
|
|
|
|
Kernel Inline Documentations Reference
|
|
======================================
|
|
|
|
.. kernel-doc:: include/linux/workqueue.h
|
|
|
|
.. kernel-doc:: kernel/workqueue.c
|