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

  	worker-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.