Concurrency Managed Workqueue (cmwq) September, 2010 Tejun Heo
Florian Mickler 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-pool 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. 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 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 is determined according to the queue parameters and workqueue attributes and appended on the shared worklist of the gcwq. For example, unless specifically overridden, a work item of a bound workqueue will be queued on the worklist of exactly that 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 gcwq bound to an actual CPU implements concurrency management by hooking into the scheduler. The gcwq 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 gcwq 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 gcwq for the pseudo unbound CPU tries 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 at the head of the worklist of the target gcwq and start execution regardless of the current concurrency level. In other words, highpri work items will always start execution as soon as execution resource is available. Ordering among highpri work items is preserved - a highpri work item queued after another highpri work item will start execution after the earlier highpri work item starts. Although highpri work items are not held back by other runnable work items, they still contribute to the concurrency level. Highpri work items in runnable state will prevent non-highpri work items from starting execution. This flag is meaningless for unbound wq. 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 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. WQ_HIGHPRI | WQ_CPU_INTENSIVE This combination makes the wq avoid interaction with concurrency management completely and behave as a simple per-CPU execution context provider. Work items queued on a highpri CPU-intensive wq start execution as soon as resources are available and don't affect execution of other work items. @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_HIGHPRI set, TIME IN MSECS EVENT 0 w1 and w2 start and burn CPU 5 w1 sleeps 10 w2 sleeps 10 w0 starts and burns CPU 15 w0 sleeps 15 w1 wakes up and finishes 20 w2 wakes up and finishes 25 w0 wakes up and burns CPU 30 w0 finishes If q1 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.