399 lines
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ReStructuredText
399 lines
15 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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Note that the flag ``WQ_NON_REENTRANT`` no longer exists as all
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workqueues are now non-reentrant - any work item is guaranteed to be
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executed by at most one worker system-wide at any given time.
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``max_active``
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--------------
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``@max_active`` determines the maximum number of execution contexts
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per CPU which can be assigned to the work items of a wq. For example,
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with ``@max_active`` of 16, at most 16 work items of the wq can be
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executing at the same time per CPU.
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Currently, for a bound wq, the maximum limit for ``@max_active`` is
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512 and the default value used when 0 is specified is 256. For an
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unbound wq, the limit is higher of 512 and 4 *
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``num_possible_cpus()``. These values are chosen sufficiently high
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such that they are not the limiting factor while providing protection
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in 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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Debugging
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=========
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Because the work functions are executed by generic worker threads
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there are a few tricks needed to shed some light on misbehaving
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workqueue users.
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Worker threads show up in the process list as: ::
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root 5671 0.0 0.0 0 0 ? S 12:07 0:00 [kworker/0:1]
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root 5672 0.0 0.0 0 0 ? S 12:07 0:00 [kworker/1:2]
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root 5673 0.0 0.0 0 0 ? S 12:12 0:00 [kworker/0:0]
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root 5674 0.0 0.0 0 0 ? S 12:13 0:00 [kworker/1:0]
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If kworkers are going crazy (using too much cpu), there are two types
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of possible problems:
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1. Something being scheduled in rapid succession
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2. A single work item that consumes lots of cpu cycles
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The first one can be tracked using tracing: ::
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$ echo workqueue:workqueue_queue_work > /sys/kernel/debug/tracing/set_event
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$ cat /sys/kernel/debug/tracing/trace_pipe > out.txt
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(wait a few secs)
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^C
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If something is busy looping on work queueing, it would be dominating
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the output and the offender can be determined with the work item
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function.
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For the second type of problems it should be possible to just check
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the stack trace of the offending worker thread. ::
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$ cat /proc/THE_OFFENDING_KWORKER/stack
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The work item's function should be trivially visible in the stack
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trace.
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Kernel Inline Documentations Reference
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======================================
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.. kernel-doc:: include/linux/workqueue.h
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