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Based on kernel version 3.9. Page generated on 2013-05-02 23:17 EST.

	
	Concurrency Managed Workqueue (cmwq)
	
	September, 2010		Tejun Heo <tj@kernel.org>
				Florian Mickler <florian@mickler.org>
	
	CONTENTS
	
	1. Introduction
	2. Why cmwq?
	3. The Design
	4. Application Programming Interface (API)
	5. Example Execution Scenarios
	6. Guidelines
	7. Debugging
	
	
	1. Introduction
	
	There are many cases where an asynchronous process execution context
	is needed and the workqueue (wq) API is the most commonly used
	mechanism for such cases.
	
	When such an asynchronous execution context is needed, a work item
	describing which function to execute is put on a queue.  An
	independent thread serves as the asynchronous execution context.  The
	queue is called workqueue and the thread is called worker.
	
	While there are work items on the workqueue the worker executes the
	functions associated with the work items one after the other.  When
	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.
	
	
	2. Why cmwq?
	
	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
	thread system-wide.  A single MT wq needed to keep around the same
	number of workers as the number of CPUs.  The kernel grew a lot of MT
	wq users over the years and with the number of CPU cores continuously
	rising, some systems saturated the default 32k PID space just booting
	up.
	
	Although MT wq wasted a lot of resource, the level of concurrency
	provided was unsatisfactory.  The limitation was common to both ST and
	MT wq albeit less severe on MT.  Each wq maintained its own separate
	worker pool.  A MT wq could provide only one execution context per CPU
	while a ST wq one for the whole system.  Work items had to compete for
	those very limited execution contexts leading to various problems
	including proneness to deadlocks around the single execution context.
	
	The tension between the provided level of concurrency and resource
	usage also forced its users to make unnecessary tradeoffs like libata
	choosing to use ST wq for polling PIOs and accepting an unnecessary
	limitation that no two polling PIOs can progress at the same time.  As
	MT wq don't provide much better concurrency, users which require
	higher level of concurrency, like async or fscache, had to implement
	their own thread pool.
	
	Concurrency Managed Workqueue (cmwq) is a reimplementation of wq with
	focus on the following goals.
	
	* Maintain compatibility with the original workqueue API.
	
	* Use per-CPU unified worker pools shared by all wq to provide
	  flexible level of concurrency on demand without wasting a lot of
	  resource.
	
	* Automatically regulate worker pool and level of concurrency so that
	  the API users don't need to worry about such details.
	
	
	3. The Design
	
	In order to ease the asynchronous execution of functions a new
	abstraction, the work item, is introduced.
	
	A work item is a simple struct that holds a pointer to the function
	that is to be executed asynchronously.  Whenever a driver or subsystem
	wants a function to be executed asynchronously it has to set up a work
	item pointing to that function and queue that work item on a
	workqueue.
	
	Special purpose threads, called worker threads, execute the functions
	off of the queue, one after the other.  If no work is queued, the
	worker threads become idle.  These worker threads are managed in so
	called thread-pools.
	
	The cmwq design differentiates between the user-facing workqueues that
	subsystems and drivers queue work items on and the backend mechanism
	which manages thread-pools and processes the queued work items.
	
	The backend is called gcwq.  There is one gcwq for each possible CPU
	and one gcwq to serve work items queued on unbound workqueues.  Each
	gcwq has two thread-pools - one for normal work items and the other
	for high priority ones.
	
	Subsystems and drivers can create and queue work items through special
	workqueue API functions as they see fit. They can influence some
	aspects of the way the work items are executed by setting flags on the
	workqueue they are putting the work item on. These flags include
	things like CPU locality, reentrancy, concurrency limits, priority and
	more.  To get a detailed overview refer to the API description of
	alloc_workqueue() below.
	
	When a work item is queued to a workqueue, the target gcwq and
	thread-pool is determined according to the queue parameters and
	workqueue attributes and appended on the shared worklist of the
	thread-pool.  For example, unless specifically overridden, a work item
	of a bound workqueue will be queued on the worklist of either normal
	or highpri thread-pool of the gcwq that is associated to the CPU the
	issuer is running on.
	
	For any worker pool implementation, managing the concurrency level
	(how many execution contexts are active) is an important issue.  cmwq
	tries to keep the concurrency at a minimal but sufficient level.
	Minimal to save resources and sufficient in that the system is used at
	its full capacity.
	
	Each thread-pool bound to an actual CPU implements concurrency
	management by hooking into the scheduler.  The thread-pool is notified
	whenever an active worker wakes up or sleeps and keeps track of the
	number of the currently runnable workers.  Generally, work items are
	not expected to hog a CPU and consume many cycles.  That means
	maintaining just enough concurrency to prevent work processing from
	stalling should be optimal.  As long as there are one or more runnable
	workers on the CPU, the thread-pool doesn't start execution of a new
	work, but, when the last running worker goes to sleep, it immediately
	schedules a new worker so that the CPU doesn't sit idle while there
	are pending work items.  This allows using a minimal number of workers
	without losing execution bandwidth.
	
	Keeping idle workers around doesn't cost other than the memory space
	for kthreads, so cmwq holds onto idle ones for a while before killing
	them.
	
	For an unbound wq, the above concurrency management doesn't apply and
	the thread-pools for the pseudo unbound CPU try to start executing all
	work items as soon as possible.  The responsibility of regulating
	concurrency level is on the users.  There is also a flag to mark a
	bound wq to ignore the concurrency management.  Please refer to the
	API section for details.
	
	Forward progress guarantee relies on that workers can be created when
	more execution contexts are necessary, which in turn is guaranteed
	through the use of rescue workers.  All work items which might be used
	on code paths that handle memory reclaim are required to be queued on
	wq's that have a rescue-worker reserved for execution under memory
	pressure.  Else it is possible that the thread-pool deadlocks waiting
	for execution contexts to free up.
	
	
	4. Application Programming Interface (API)
	
	alloc_workqueue() allocates a wq.  The original create_*workqueue()
	functions are deprecated and scheduled for removal.  alloc_workqueue()
	takes three arguments - @name, @flags and @max_active.  @name is the
	name of the wq and also used as the name of the rescuer thread if
	there is one.
	
	A wq no longer manages execution resources but serves as a domain for
	forward progress guarantee, flush and work item attributes.  @flags
	and @max_active control how work items are assigned execution
	resources, scheduled and executed.
	
	@flags:
	
	  WQ_NON_REENTRANT
	
		By default, a wq guarantees non-reentrance only on the same
		CPU.  A work item may not be executed concurrently on the same
		CPU by multiple workers but is allowed to be executed
		concurrently on multiple CPUs.  This flag makes sure
		non-reentrance is enforced across all CPUs.  Work items queued
		to a non-reentrant wq are guaranteed to be executed by at most
		one worker system-wide at any given time.
	
	  WQ_UNBOUND
	
		Work items queued to an unbound wq are served by a special
		gcwq which hosts workers which are not bound to any specific
		CPU.  This makes the wq behave as a simple execution context
		provider without concurrency management.  The unbound gcwq
		tries to start execution of work items as soon as possible.
		Unbound wq sacrifices locality but is useful for the following
		cases.
	
		* Wide fluctuation in the concurrency level requirement is
		  expected and using bound wq may end up creating large number
		  of mostly unused workers across different CPUs as the issuer
		  hops through different CPUs.
	
		* Long running CPU intensive workloads which can be better
		  managed by the system scheduler.
	
	  WQ_FREEZABLE
	
		A freezable wq participates in the freeze phase of the system
		suspend operations.  Work items on the wq are drained and no
		new work item starts execution until thawed.
	
	  WQ_MEM_RECLAIM
	
		All wq which might be used in the memory reclaim paths _MUST_
		have this flag set.  The wq is guaranteed to have at least one
		execution context regardless of memory pressure.
	
	  WQ_HIGHPRI
	
		Work items of a highpri wq are queued to the highpri
		thread-pool of the target gcwq.  Highpri thread-pools are
		served by worker threads with elevated nice level.
	
		Note that normal and highpri thread-pools don't interact with
		each other.  Each maintain its separate pool of workers and
		implements concurrency management among its workers.
	
	  WQ_CPU_INTENSIVE
	
		Work items of a CPU intensive wq do not contribute to the
		concurrency level.  In other words, runnable CPU intensive
		work items will not prevent other work items in the same
		thread-pool from starting execution.  This is useful for bound
		work items which are expected to hog CPU cycles so that their
		execution is regulated by the system scheduler.
	
		Although CPU intensive work items don't contribute to the
		concurrency level, start of their executions is still
		regulated by the concurrency management and runnable
		non-CPU-intensive work items can delay execution of CPU
		intensive work items.
	
		This flag is meaningless for unbound wq.
	
	@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.
	
	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 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
	may queue at the same time.  Unless there is a specific need for
	throttling the number of active work items, specifying '0' is
	recommended.
	
	Some users depend on the strict execution ordering of ST wq.  The
	combination of @max_active of 1 and WQ_UNBOUND is used to achieve this
	behavior.  Work items on such wq are always queued to the unbound gcwq
	and only one work item can be active at any given time thus achieving
	the same ordering property as ST wq.
	
	
	5. Example Execution Scenarios
	
	The following example execution scenarios try to illustrate how cmwq
	behave under different configurations.
	
	 Work items w0, w1, w2 are queued to a bound wq q0 on the same CPU.
	 w0 burns CPU for 5ms then sleeps for 10ms then burns CPU for 5ms
	 again before finishing.  w1 and w2 burn CPU for 5ms then sleep for
	 10ms.
	
	Ignoring all other tasks, works and processing overhead, and assuming
	simple FIFO scheduling, the following is one highly simplified version
	of possible sequences of events with the original wq.
	
	 TIME IN MSECS	EVENT
	 0		w0 starts and burns CPU
	 5		w0 sleeps
	 15		w0 wakes up and burns CPU
	 20		w0 finishes
	 20		w1 starts and burns CPU
	 25		w1 sleeps
	 35		w1 wakes up and finishes
	 35		w2 starts and burns CPU
	 40		w2 sleeps
	 50		w2 wakes up and finishes
	
	And with cmwq with @max_active >= 3,
	
	 TIME IN MSECS	EVENT
	 0		w0 starts and burns CPU
	 5		w0 sleeps
	 5		w1 starts and burns CPU
	 10		w1 sleeps
	 10		w2 starts and burns CPU
	 15		w2 sleeps
	 15		w0 wakes up and burns CPU
	 20		w0 finishes
	 20		w1 wakes up and finishes
	 25		w2 wakes up and finishes
	
	If @max_active == 2,
	
	 TIME IN MSECS	EVENT
	 0		w0 starts and burns CPU
	 5		w0 sleeps
	 5		w1 starts and burns CPU
	 10		w1 sleeps
	 15		w0 wakes up and burns CPU
	 20		w0 finishes
	 20		w1 wakes up and finishes
	 20		w2 starts and burns CPU
	 25		w2 sleeps
	 35		w2 wakes up and finishes
	
	Now, let's assume w1 and w2 are queued to a different wq q1 which has
	WQ_CPU_INTENSIVE set,
	
	 TIME IN MSECS	EVENT
	 0		w0 starts and burns CPU
	 5		w0 sleeps
	 5		w1 and w2 start and burn CPU
	 10		w1 sleeps
	 15		w2 sleeps
	 15		w0 wakes up and burns CPU
	 20		w0 finishes
	 20		w1 wakes up and finishes
	 25		w2 wakes up and finishes
	
	
	6. Guidelines
	
	* Do not forget to use WQ_MEM_RECLAIM if a wq may process work items
	  which are used during memory reclaim.  Each wq with WQ_MEM_RECLAIM
	  set has an execution context reserved for it.  If there is
	  dependency among multiple work items used during memory reclaim,
	  they should be queued to separate wq each with WQ_MEM_RECLAIM.
	
	* Unless strict ordering is required, there is no need to use ST wq.
	
	* Unless there is a specific need, using 0 for @max_active is
	  recommended.  In most use cases, concurrency level usually stays
	  well under the default limit.
	
	* A wq serves as a domain for forward progress guarantee
	  (WQ_MEM_RECLAIM, flush and work item attributes.  Work items which
	  are not involved in memory reclaim and don't need to be flushed as a
	  part of a group of work items, and don't require any special
	  attribute, can use one of the system wq.  There is no difference in
	  execution characteristics between using a dedicated wq and a system
	  wq.
	
	* Unless work items are expected to consume a huge amount of CPU
	  cycles, using a bound wq is usually beneficial due to the increased
	  level of locality in wq operations and work item execution.
	
	
	7. 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 beeing 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/debug/tracing/set_event
		$ cat /sys/kernel/debug/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.

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