android_kernel_xiaomi_sm8350/Documentation/workqueue.txt

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 worker-pools.

The cmwq design differentiates between the user-facing workqueues that
subsystems and drivers queue work items on and the backend mechanism
which manages worker-pools and processes the queued work items.

There are two worker-pools, one for normal work items and the other
for high priority ones, for each possible CPU and some extra
worker-pools to serve work items queued on unbound workqueues - the
number of these backing pools is dynamic.

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, 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 worker-pool is
determined according to the queue parameters and workqueue attributes
and appended on the shared worklist of the worker-pool.  For example,
unless specifically overridden, a work item of a bound workqueue will
be queued on the worklist of either normal or highpri worker-pool 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 worker-pool bound to an actual CPU implements concurrency
management by hooking into the scheduler.  The worker-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 worker-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 unbound workqueues, the number of backing pools is dynamic.
Unbound workqueue can be assigned custom attributes using
apply_workqueue_attrs() and workqueue will automatically create
backing worker pools matching the attributes.  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 worker-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_UNBOUND

	Work items queued to an unbound wq are served by the special
	woker-pools which host 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
	worker-pools try 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
	worker-pool of the target cpu.  Highpri worker-pools are
	served by worker threads with elevated nice level.

	Note that normal and highpri worker-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
	worker-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.

Note that the flag WQ_NON_REENTRANT no longer exists as all workqueues
are now non-reentrant - any work item is guaranteed to be executed by
at most one worker system-wide at any given time.

@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
worker-pools 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 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/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.