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Based on kernel version 4.9. Page generated on 2016-12-21 14:37 EST.

2	Concurrency Managed Workqueue (cmwq)
4	September, 2010		Tejun Heo <tj@kernel.org>
5				Florian Mickler <florian@mickler.org>
9	1. Introduction
10	2. Why cmwq?
11	3. The Design
12	4. Application Programming Interface (API)
13	5. Example Execution Scenarios
14	6. Guidelines
15	7. Debugging
18	1. Introduction
20	There are many cases where an asynchronous process execution context
21	is needed and the workqueue (wq) API is the most commonly used
22	mechanism for such cases.
24	When such an asynchronous execution context is needed, a work item
25	describing which function to execute is put on a queue.  An
26	independent thread serves as the asynchronous execution context.  The
27	queue is called workqueue and the thread is called worker.
29	While there are work items on the workqueue the worker executes the
30	functions associated with the work items one after the other.  When
31	there is no work item left on the workqueue the worker becomes idle.
32	When a new work item gets queued, the worker begins executing again.
35	2. Why cmwq?
37	In the original wq implementation, a multi threaded (MT) wq had one
38	worker thread per CPU and a single threaded (ST) wq had one worker
39	thread system-wide.  A single MT wq needed to keep around the same
40	number of workers as the number of CPUs.  The kernel grew a lot of MT
41	wq users over the years and with the number of CPU cores continuously
42	rising, some systems saturated the default 32k PID space just booting
43	up.
45	Although MT wq wasted a lot of resource, the level of concurrency
46	provided was unsatisfactory.  The limitation was common to both ST and
47	MT wq albeit less severe on MT.  Each wq maintained its own separate
48	worker pool.  A MT wq could provide only one execution context per CPU
49	while a ST wq one for the whole system.  Work items had to compete for
50	those very limited execution contexts leading to various problems
51	including proneness to deadlocks around the single execution context.
53	The tension between the provided level of concurrency and resource
54	usage also forced its users to make unnecessary tradeoffs like libata
55	choosing to use ST wq for polling PIOs and accepting an unnecessary
56	limitation that no two polling PIOs can progress at the same time.  As
57	MT wq don't provide much better concurrency, users which require
58	higher level of concurrency, like async or fscache, had to implement
59	their own thread pool.
61	Concurrency Managed Workqueue (cmwq) is a reimplementation of wq with
62	focus on the following goals.
64	* Maintain compatibility with the original workqueue API.
66	* Use per-CPU unified worker pools shared by all wq to provide
67	  flexible level of concurrency on demand without wasting a lot of
68	  resource.
70	* Automatically regulate worker pool and level of concurrency so that
71	  the API users don't need to worry about such details.
74	3. The Design
76	In order to ease the asynchronous execution of functions a new
77	abstraction, the work item, is introduced.
79	A work item is a simple struct that holds a pointer to the function
80	that is to be executed asynchronously.  Whenever a driver or subsystem
81	wants a function to be executed asynchronously it has to set up a work
82	item pointing to that function and queue that work item on a
83	workqueue.
85	Special purpose threads, called worker threads, execute the functions
86	off of the queue, one after the other.  If no work is queued, the
87	worker threads become idle.  These worker threads are managed in so
88	called worker-pools.
90	The cmwq design differentiates between the user-facing workqueues that
91	subsystems and drivers queue work items on and the backend mechanism
92	which manages worker-pools and processes the queued work items.
94	There are two worker-pools, one for normal work items and the other
95	for high priority ones, for each possible CPU and some extra
96	worker-pools to serve work items queued on unbound workqueues - the
97	number of these backing pools is dynamic.
99	Subsystems and drivers can create and queue work items through special
100	workqueue API functions as they see fit. They can influence some
101	aspects of the way the work items are executed by setting flags on the
102	workqueue they are putting the work item on. These flags include
103	things like CPU locality, concurrency limits, priority and more.  To
104	get a detailed overview refer to the API description of
105	alloc_workqueue() below.
107	When a work item is queued to a workqueue, the target worker-pool is
108	determined according to the queue parameters and workqueue attributes
109	and appended on the shared worklist of the worker-pool.  For example,
110	unless specifically overridden, a work item of a bound workqueue will
111	be queued on the worklist of either normal or highpri worker-pool that
112	is associated to the CPU the issuer is running on.
114	For any worker pool implementation, managing the concurrency level
115	(how many execution contexts are active) is an important issue.  cmwq
116	tries to keep the concurrency at a minimal but sufficient level.
117	Minimal to save resources and sufficient in that the system is used at
118	its full capacity.
120	Each worker-pool bound to an actual CPU implements concurrency
121	management by hooking into the scheduler.  The worker-pool is notified
122	whenever an active worker wakes up or sleeps and keeps track of the
123	number of the currently runnable workers.  Generally, work items are
124	not expected to hog a CPU and consume many cycles.  That means
125	maintaining just enough concurrency to prevent work processing from
126	stalling should be optimal.  As long as there are one or more runnable
127	workers on the CPU, the worker-pool doesn't start execution of a new
128	work, but, when the last running worker goes to sleep, it immediately
129	schedules a new worker so that the CPU doesn't sit idle while there
130	are pending work items.  This allows using a minimal number of workers
131	without losing execution bandwidth.
133	Keeping idle workers around doesn't cost other than the memory space
134	for kthreads, so cmwq holds onto idle ones for a while before killing
135	them.
137	For unbound workqueues, the number of backing pools is dynamic.
138	Unbound workqueue can be assigned custom attributes using
139	apply_workqueue_attrs() and workqueue will automatically create
140	backing worker pools matching the attributes.  The responsibility of
141	regulating concurrency level is on the users.  There is also a flag to
142	mark a bound wq to ignore the concurrency management.  Please refer to
143	the API section for details.
145	Forward progress guarantee relies on that workers can be created when
146	more execution contexts are necessary, which in turn is guaranteed
147	through the use of rescue workers.  All work items which might be used
148	on code paths that handle memory reclaim are required to be queued on
149	wq's that have a rescue-worker reserved for execution under memory
150	pressure.  Else it is possible that the worker-pool deadlocks waiting
151	for execution contexts to free up.
154	4. Application Programming Interface (API)
156	alloc_workqueue() allocates a wq.  The original create_*workqueue()
157	functions are deprecated and scheduled for removal.  alloc_workqueue()
158	takes three arguments - @name, @flags and @max_active.  @name is the
159	name of the wq and also used as the name of the rescuer thread if
160	there is one.
162	A wq no longer manages execution resources but serves as a domain for
163	forward progress guarantee, flush and work item attributes.  @flags
164	and @max_active control how work items are assigned execution
165	resources, scheduled and executed.
167	@flags:
171		Work items queued to an unbound wq are served by the special
172		worker-pools which host workers which are not bound to any
173		specific CPU.  This makes the wq behave as a simple execution
174		context provider without concurrency management.  The unbound
175		worker-pools try to start execution of work items as soon as
176		possible.  Unbound wq sacrifices locality but is useful for
177		the following cases.
179		* Wide fluctuation in the concurrency level requirement is
180		  expected and using bound wq may end up creating large number
181		  of mostly unused workers across different CPUs as the issuer
182		  hops through different CPUs.
184		* Long running CPU intensive workloads which can be better
185		  managed by the system scheduler.
189		A freezable wq participates in the freeze phase of the system
190		suspend operations.  Work items on the wq are drained and no
191		new work item starts execution until thawed.
195		All wq which might be used in the memory reclaim paths _MUST_
196		have this flag set.  The wq is guaranteed to have at least one
197		execution context regardless of memory pressure.
201		Work items of a highpri wq are queued to the highpri
202		worker-pool of the target cpu.  Highpri worker-pools are
203		served by worker threads with elevated nice level.
205		Note that normal and highpri worker-pools don't interact with
206		each other.  Each maintain its separate pool of workers and
207		implements concurrency management among its workers.
211		Work items of a CPU intensive wq do not contribute to the
212		concurrency level.  In other words, runnable CPU intensive
213		work items will not prevent other work items in the same
214		worker-pool from starting execution.  This is useful for bound
215		work items which are expected to hog CPU cycles so that their
216		execution is regulated by the system scheduler.
218		Although CPU intensive work items don't contribute to the
219		concurrency level, start of their executions is still
220		regulated by the concurrency management and runnable
221		non-CPU-intensive work items can delay execution of CPU
222		intensive work items.
224		This flag is meaningless for unbound wq.
226	Note that the flag WQ_NON_REENTRANT no longer exists as all workqueues
227	are now non-reentrant - any work item is guaranteed to be executed by
228	at most one worker system-wide at any given time.
230	@max_active:
232	@max_active determines the maximum number of execution contexts per
233	CPU which can be assigned to the work items of a wq.  For example,
234	with @max_active of 16, at most 16 work items of the wq can be
235	executing at the same time per CPU.
237	Currently, for a bound wq, the maximum limit for @max_active is 512
238	and the default value used when 0 is specified is 256.  For an unbound
239	wq, the limit is higher of 512 and 4 * num_possible_cpus().  These
240	values are chosen sufficiently high such that they are not the
241	limiting factor while providing protection in runaway cases.
243	The number of active work items of a wq is usually regulated by the
244	users of the wq, more specifically, by how many work items the users
245	may queue at the same time.  Unless there is a specific need for
246	throttling the number of active work items, specifying '0' is
247	recommended.
249	Some users depend on the strict execution ordering of ST wq.  The
250	combination of @max_active of 1 and WQ_UNBOUND is used to achieve this
251	behavior.  Work items on such wq are always queued to the unbound
252	worker-pools and only one work item can be active at any given time thus
253	achieving the same ordering property as ST wq.
256	5. Example Execution Scenarios
258	The following example execution scenarios try to illustrate how cmwq
259	behave under different configurations.
261	 Work items w0, w1, w2 are queued to a bound wq q0 on the same CPU.
262	 w0 burns CPU for 5ms then sleeps for 10ms then burns CPU for 5ms
263	 again before finishing.  w1 and w2 burn CPU for 5ms then sleep for
264	 10ms.
266	Ignoring all other tasks, works and processing overhead, and assuming
267	simple FIFO scheduling, the following is one highly simplified version
268	of possible sequences of events with the original wq.
271	 0		w0 starts and burns CPU
272	 5		w0 sleeps
273	 15		w0 wakes up and burns CPU
274	 20		w0 finishes
275	 20		w1 starts and burns CPU
276	 25		w1 sleeps
277	 35		w1 wakes up and finishes
278	 35		w2 starts and burns CPU
279	 40		w2 sleeps
280	 50		w2 wakes up and finishes
282	And with cmwq with @max_active >= 3,
285	 0		w0 starts and burns CPU
286	 5		w0 sleeps
287	 5		w1 starts and burns CPU
288	 10		w1 sleeps
289	 10		w2 starts and burns CPU
290	 15		w2 sleeps
291	 15		w0 wakes up and burns CPU
292	 20		w0 finishes
293	 20		w1 wakes up and finishes
294	 25		w2 wakes up and finishes
296	If @max_active == 2,
299	 0		w0 starts and burns CPU
300	 5		w0 sleeps
301	 5		w1 starts and burns CPU
302	 10		w1 sleeps
303	 15		w0 wakes up and burns CPU
304	 20		w0 finishes
305	 20		w1 wakes up and finishes
306	 20		w2 starts and burns CPU
307	 25		w2 sleeps
308	 35		w2 wakes up and finishes
310	Now, let's assume w1 and w2 are queued to a different wq q1 which has
314	 0		w0 starts and burns CPU
315	 5		w0 sleeps
316	 5		w1 and w2 start and burn CPU
317	 10		w1 sleeps
318	 15		w2 sleeps
319	 15		w0 wakes up and burns CPU
320	 20		w0 finishes
321	 20		w1 wakes up and finishes
322	 25		w2 wakes up and finishes
325	6. Guidelines
327	* Do not forget to use WQ_MEM_RECLAIM if a wq may process work items
328	  which are used during memory reclaim.  Each wq with WQ_MEM_RECLAIM
329	  set has an execution context reserved for it.  If there is
330	  dependency among multiple work items used during memory reclaim,
331	  they should be queued to separate wq each with WQ_MEM_RECLAIM.
333	* Unless strict ordering is required, there is no need to use ST wq.
335	* Unless there is a specific need, using 0 for @max_active is
336	  recommended.  In most use cases, concurrency level usually stays
337	  well under the default limit.
339	* A wq serves as a domain for forward progress guarantee
340	  (WQ_MEM_RECLAIM, flush and work item attributes.  Work items which
341	  are not involved in memory reclaim and don't need to be flushed as a
342	  part of a group of work items, and don't require any special
343	  attribute, can use one of the system wq.  There is no difference in
344	  execution characteristics between using a dedicated wq and a system
345	  wq.
347	* Unless work items are expected to consume a huge amount of CPU
348	  cycles, using a bound wq is usually beneficial due to the increased
349	  level of locality in wq operations and work item execution.
352	7. Debugging
354	Because the work functions are executed by generic worker threads
355	there are a few tricks needed to shed some light on misbehaving
356	workqueue users.
358	Worker threads show up in the process list as:
360	root      5671  0.0  0.0      0     0 ?        S    12:07   0:00 [kworker/0:1]
361	root      5672  0.0  0.0      0     0 ?        S    12:07   0:00 [kworker/1:2]
362	root      5673  0.0  0.0      0     0 ?        S    12:12   0:00 [kworker/0:0]
363	root      5674  0.0  0.0      0     0 ?        S    12:13   0:00 [kworker/1:0]
365	If kworkers are going crazy (using too much cpu), there are two types
366	of possible problems:
368		1. Something being scheduled in rapid succession
369		2. A single work item that consumes lots of cpu cycles
371	The first one can be tracked using tracing:
373		$ echo workqueue:workqueue_queue_work > /sys/kernel/debug/tracing/set_event
374		$ cat /sys/kernel/debug/tracing/trace_pipe > out.txt
375		(wait a few secs)
376		^C
378	If something is busy looping on work queueing, it would be dominating
379	the output and the offender can be determined with the work item
380	function.
382	For the second type of problems it should be possible to just check
383	the stack trace of the offending worker thread.
385		$ cat /proc/THE_OFFENDING_KWORKER/stack
387	The work item's function should be trivially visible in the stack
388	trace.
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