1 ========= 2 Workqueue 3 ========= 4 5 :Date: September, 2010 6 :Author: Tejun Heo <tj@kernel.org> 7 :Author: Florian Mickler <florian@mickler.org> 8 9 10 Introduction 11 ============ 12 13 There are many cases where an asynchronous process execution context 14 is needed and the workqueue (wq) API is the most commonly used 15 mechanism for such cases. 16 17 When such an asynchronous execution context is needed, a work item 18 describing which function to execute is put on a queue. An 19 independent thread serves as the asynchronous execution context. The 20 queue is called workqueue and the thread is called worker. 21 22 While there are work items on the workqueue the worker executes the 23 functions associated with the work items one after the other. When 24 there is no work item left on the workqueue the worker becomes idle. 25 When a new work item gets queued, the worker begins executing again. 26 27 28 Why Concurrency Managed Workqueue? 29 ================================== 30 31 In the original wq implementation, a multi threaded (MT) wq had one 32 worker thread per CPU and a single threaded (ST) wq had one worker 33 thread system-wide. A single MT wq needed to keep around the same 34 number of workers as the number of CPUs. The kernel grew a lot of MT 35 wq users over the years and with the number of CPU cores continuously 36 rising, some systems saturated the default 32k PID space just booting 37 up. 38 39 Although MT wq wasted a lot of resource, the level of concurrency 40 provided was unsatisfactory. The limitation was common to both ST and 41 MT wq albeit less severe on MT. Each wq maintained its own separate 42 worker pool. An MT wq could provide only one execution context per CPU 43 while an ST wq one for the whole system. Work items had to compete for 44 those very limited execution contexts leading to various problems 45 including proneness to deadlocks around the single execution context. 46 47 The tension between the provided level of concurrency and resource 48 usage also forced its users to make unnecessary tradeoffs like libata 49 choosing to use ST wq for polling PIOs and accepting an unnecessary 50 limitation that no two polling PIOs can progress at the same time. As 51 MT wq don't provide much better concurrency, users which require 52 higher level of concurrency, like async or fscache, had to implement 53 their own thread pool. 54 55 Concurrency Managed Workqueue (cmwq) is a reimplementation of wq with 56 focus on the following goals. 57 58 * Maintain compatibility with the original workqueue API. 59 60 * Use per-CPU unified worker pools shared by all wq to provide 61 flexible level of concurrency on demand without wasting a lot of 62 resource. 63 64 * Automatically regulate worker pool and level of concurrency so that 65 the API users don't need to worry about such details. 66 67 68 The Design 69 ========== 70 71 In order to ease the asynchronous execution of functions a new 72 abstraction, the work item, is introduced. 73 74 A work item is a simple struct that holds a pointer to the function 75 that is to be executed asynchronously. Whenever a driver or subsystem 76 wants a function to be executed asynchronously it has to set up a work 77 item pointing to that function and queue that work item on a 78 workqueue. 79 80 A work item can be executed in either a thread or the BH (softirq) context. 81 82 For threaded workqueues, special purpose threads, called [k]workers, execute 83 the functions off of the queue, one after the other. If no work is queued, 84 the worker threads become idle. These worker threads are managed in 85 worker-pools. 86 87 The cmwq design differentiates between the user-facing workqueues that 88 subsystems and drivers queue work items on and the backend mechanism 89 which manages worker-pools and processes the queued work items. 90 91 There are two worker-pools, one for normal work items and the other 92 for high priority ones, for each possible CPU and some extra 93 worker-pools to serve work items queued on unbound workqueues - the 94 number of these backing pools is dynamic. 95 96 BH workqueues use the same framework. However, as there can only be one 97 concurrent execution context, there's no need to worry about concurrency. 98 Each per-CPU BH worker pool contains only one pseudo worker which represents 99 the BH execution context. A BH workqueue can be considered a convenience 100 interface to softirq. 101 102 Subsystems and drivers can create and queue work items through special 103 workqueue API functions as they see fit. They can influence some 104 aspects of the way the work items are executed by setting flags on the 105 workqueue they are putting the work item on. These flags include 106 things like CPU locality, concurrency limits, priority and more. To 107 get a detailed overview refer to the API description of 108 ``alloc_workqueue()`` below. 109 110 When a work item is queued to a workqueue, the target worker-pool is 111 determined according to the queue parameters and workqueue attributes 112 and appended on the shared worklist of the worker-pool. For example, 113 unless specifically overridden, a work item of a bound workqueue will 114 be queued on the worklist of either normal or highpri worker-pool that 115 is associated to the CPU the issuer is running on. 116 117 For any thread pool implementation, managing the concurrency level 118 (how many execution contexts are active) is an important issue. cmwq 119 tries to keep the concurrency at a minimal but sufficient level. 120 Minimal to save resources and sufficient in that the system is used at 121 its full capacity. 122 123 Each worker-pool bound to an actual CPU implements concurrency 124 management by hooking into the scheduler. The worker-pool is notified 125 whenever an active worker wakes up or sleeps and keeps track of the 126 number of the currently runnable workers. Generally, work items are 127 not expected to hog a CPU and consume many cycles. That means 128 maintaining just enough concurrency to prevent work processing from 129 stalling should be optimal. As long as there are one or more runnable 130 workers on the CPU, the worker-pool doesn't start execution of a new 131 work, but, when the last running worker goes to sleep, it immediately 132 schedules a new worker so that the CPU doesn't sit idle while there 133 are pending work items. This allows using a minimal number of workers 134 without losing execution bandwidth. 135 136 Keeping idle workers around doesn't cost other than the memory space 137 for kthreads, so cmwq holds onto idle ones for a while before killing 138 them. 139 140 For unbound workqueues, the number of backing pools is dynamic. 141 Unbound workqueue can be assigned custom attributes using 142 ``apply_workqueue_attrs()`` and workqueue will automatically create 143 backing worker pools matching the attributes. The responsibility of 144 regulating concurrency level is on the users. There is also a flag to 145 mark a bound wq to ignore the concurrency management. Please refer to 146 the API section for details. 147 148 Forward progress guarantee relies on that workers can be created when 149 more execution contexts are necessary, which in turn is guaranteed 150 through the use of rescue workers. All work items which might be used 151 on code paths that handle memory reclaim are required to be queued on 152 wq's that have a rescue-worker reserved for execution under memory 153 pressure. Else it is possible that the worker-pool deadlocks waiting 154 for execution contexts to free up. 155 156 157 Application Programming Interface (API) 158 ======================================= 159 160 ``alloc_workqueue()`` allocates a wq. The original 161 ``create_*workqueue()`` functions are deprecated and scheduled for 162 removal. ``alloc_workqueue()`` takes three arguments - ``@name``, 163 ``@flags`` and ``@max_active``. ``@name`` is the name of the wq and 164 also used as the name of the rescuer thread if there is one. 165 166 A wq no longer manages execution resources but serves as a domain for 167 forward progress guarantee, flush and work item attributes. ``@flags`` 168 and ``@max_active`` control how work items are assigned execution 169 resources, scheduled and executed. 170 171 172 ``flags`` 173 --------- 174 175 ``WQ_BH`` 176 BH workqueues can be considered a convenience interface to softirq. BH 177 workqueues are always per-CPU and all BH work items are executed in the 178 queueing CPU's softirq context in the queueing order. 179 180 All BH workqueues must have 0 ``max_active`` and ``WQ_HIGHPRI`` is the 181 only allowed additional flag. 182 183 BH work items cannot sleep. All other features such as delayed queueing, 184 flushing and canceling are supported. 185 186 ``WQ_UNBOUND`` 187 Work items queued to an unbound wq are served by the special 188 worker-pools which host workers which are not bound to any 189 specific CPU. This makes the wq behave as a simple execution 190 context provider without concurrency management. The unbound 191 worker-pools try to start execution of work items as soon as 192 possible. Unbound wq sacrifices locality but is useful for 193 the following cases. 194 195 * Wide fluctuation in the concurrency level requirement is 196 expected and using bound wq may end up creating large number 197 of mostly unused workers across different CPUs as the issuer 198 hops through different CPUs. 199 200 * Long running CPU intensive workloads which can be better 201 managed by the system scheduler. 202 203 ``WQ_FREEZABLE`` 204 A freezable wq participates in the freeze phase of the system 205 suspend operations. Work items on the wq are drained and no 206 new work item starts execution until thawed. 207 208 ``WQ_MEM_RECLAIM`` 209 All wq which might be used in the memory reclaim paths **MUST** 210 have this flag set. The wq is guaranteed to have at least one 211 execution context regardless of memory pressure. 212 213 ``WQ_HIGHPRI`` 214 Work items of a highpri wq are queued to the highpri 215 worker-pool of the target cpu. Highpri worker-pools are 216 served by worker threads with elevated nice level. 217 218 Note that normal and highpri worker-pools don't interact with 219 each other. Each maintains its separate pool of workers and 220 implements concurrency management among its workers. 221 222 ``WQ_CPU_INTENSIVE`` 223 Work items of a CPU intensive wq do not contribute to the 224 concurrency level. In other words, runnable CPU intensive 225 work items will not prevent other work items in the same 226 worker-pool from starting execution. This is useful for bound 227 work items which are expected to hog CPU cycles so that their 228 execution is regulated by the system scheduler. 229 230 Although CPU intensive work items don't contribute to the 231 concurrency level, start of their executions is still 232 regulated by the concurrency management and runnable 233 non-CPU-intensive work items can delay execution of CPU 234 intensive work items. 235 236 This flag is meaningless for unbound wq. 237 238 239 ``max_active`` 240 -------------- 241 242 ``@max_active`` determines the maximum number of execution contexts per 243 CPU which can be assigned to the work items of a wq. For example, with 244 ``@max_active`` of 16, at most 16 work items of the wq can be executing 245 at the same time per CPU. This is always a per-CPU attribute, even for 246 unbound workqueues. 247 248 The maximum limit for ``@max_active`` is 512 and the default value used 249 when 0 is specified is 256. These values are chosen sufficiently high 250 such that they are not the limiting factor while providing protection in 251 runaway cases. 252 253 The number of active work items of a wq is usually regulated by the 254 users of the wq, more specifically, by how many work items the users 255 may queue at the same time. Unless there is a specific need for 256 throttling the number of active work items, specifying '0' is 257 recommended. 258 259 Some users depend on strict execution ordering where only one work item 260 is in flight at any given time and the work items are processed in 261 queueing order. While the combination of ``@max_active`` of 1 and 262 ``WQ_UNBOUND`` used to achieve this behavior, this is no longer the 263 case. Use alloc_ordered_workqueue() instead. 264 265 266 Example Execution Scenarios 267 =========================== 268 269 The following example execution scenarios try to illustrate how cmwq 270 behave under different configurations. 271 272 Work items w0, w1, w2 are queued to a bound wq q0 on the same CPU. 273 w0 burns CPU for 5ms then sleeps for 10ms then burns CPU for 5ms 274 again before finishing. w1 and w2 burn CPU for 5ms then sleep for 275 10ms. 276 277 Ignoring all other tasks, works and processing overhead, and assuming 278 simple FIFO scheduling, the following is one highly simplified version 279 of possible sequences of events with the original wq. :: 280 281 TIME IN MSECS EVENT 282 0 w0 starts and burns CPU 283 5 w0 sleeps 284 15 w0 wakes up and burns CPU 285 20 w0 finishes 286 20 w1 starts and burns CPU 287 25 w1 sleeps 288 35 w1 wakes up and finishes 289 35 w2 starts and burns CPU 290 40 w2 sleeps 291 50 w2 wakes up and finishes 292 293 And with cmwq with ``@max_active`` >= 3, :: 294 295 TIME IN MSECS EVENT 296 0 w0 starts and burns CPU 297 5 w0 sleeps 298 5 w1 starts and burns CPU 299 10 w1 sleeps 300 10 w2 starts and burns CPU 301 15 w2 sleeps 302 15 w0 wakes up and burns CPU 303 20 w0 finishes 304 20 w1 wakes up and finishes 305 25 w2 wakes up and finishes 306 307 If ``@max_active`` == 2, :: 308 309 TIME IN MSECS EVENT 310 0 w0 starts and burns CPU 311 5 w0 sleeps 312 5 w1 starts and burns CPU 313 10 w1 sleeps 314 15 w0 wakes up and burns CPU 315 20 w0 finishes 316 20 w1 wakes up and finishes 317 20 w2 starts and burns CPU 318 25 w2 sleeps 319 35 w2 wakes up and finishes 320 321 Now, let's assume w1 and w2 are queued to a different wq q1 which has 322 ``WQ_CPU_INTENSIVE`` set, :: 323 324 TIME IN MSECS EVENT 325 0 w0 starts and burns CPU 326 5 w0 sleeps 327 5 w1 and w2 start and burn CPU 328 10 w1 sleeps 329 15 w2 sleeps 330 15 w0 wakes up and burns CPU 331 20 w0 finishes 332 20 w1 wakes up and finishes 333 25 w2 wakes up and finishes 334 335 336 Guidelines 337 ========== 338 339 * Do not forget to use ``WQ_MEM_RECLAIM`` if a wq may process work 340 items which are used during memory reclaim. Each wq with 341 ``WQ_MEM_RECLAIM`` set has an execution context reserved for it. If 342 there is dependency among multiple work items used during memory 343 reclaim, they should be queued to separate wq each with 344 ``WQ_MEM_RECLAIM``. 345 346 * Unless strict ordering is required, there is no need to use ST wq. 347 348 * Unless there is a specific need, using 0 for @max_active is 349 recommended. In most use cases, concurrency level usually stays 350 well under the default limit. 351 352 * A wq serves as a domain for forward progress guarantee 353 (``WQ_MEM_RECLAIM``, flush and work item attributes. Work items 354 which are not involved in memory reclaim and don't need to be 355 flushed as a part of a group of work items, and don't require any 356 special attribute, can use one of the system wq. There is no 357 difference in execution characteristics between using a dedicated wq 358 and a system wq. 359 360 * Unless work items are expected to consume a huge amount of CPU 361 cycles, using a bound wq is usually beneficial due to the increased 362 level of locality in wq operations and work item execution. 363 364 365 Affinity Scopes 366 =============== 367 368 An unbound workqueue groups CPUs according to its affinity scope to improve 369 cache locality. For example, if a workqueue is using the default affinity 370 scope of "cache", it will group CPUs according to last level cache 371 boundaries. A work item queued on the workqueue will be assigned to a worker 372 on one of the CPUs which share the last level cache with the issuing CPU. 373 Once started, the worker may or may not be allowed to move outside the scope 374 depending on the ``affinity_strict`` setting of the scope. 375 376 Workqueue currently supports the following affinity scopes. 377 378 ``default`` 379 Use the scope in module parameter ``workqueue.default_affinity_scope`` 380 which is always set to one of the scopes below. 381 382 ``cpu`` 383 CPUs are not grouped. A work item issued on one CPU is processed by a 384 worker on the same CPU. This makes unbound workqueues behave as per-cpu 385 workqueues without concurrency management. 386 387 ``smt`` 388 CPUs are grouped according to SMT boundaries. This usually means that the 389 logical threads of each physical CPU core are grouped together. 390 391 ``cache`` 392 CPUs are grouped according to cache boundaries. Which specific cache 393 boundary is used is determined by the arch code. L3 is used in a lot of 394 cases. This is the default affinity scope. 395 396 ``numa`` 397 CPUs are grouped according to NUMA boundaries. 398 399 ``system`` 400 All CPUs are put in the same group. Workqueue makes no effort to process a 401 work item on a CPU close to the issuing CPU. 402 403 The default affinity scope can be changed with the module parameter 404 ``workqueue.default_affinity_scope`` and a specific workqueue's affinity 405 scope can be changed using ``apply_workqueue_attrs()``. 406 407 If ``WQ_SYSFS`` is set, the workqueue will have the following affinity scope 408 related interface files under its ``/sys/devices/virtual/workqueue/WQ_NAME/`` 409 directory. 410 411 ``affinity_scope`` 412 Read to see the current affinity scope. Write to change. 413 414 When default is the current scope, reading this file will also show the 415 current effective scope in parentheses, for example, ``default (cache)``. 416 417 ``affinity_strict`` 418 0 by default indicating that affinity scopes are not strict. When a work 419 item starts execution, workqueue makes a best-effort attempt to ensure 420 that the worker is inside its affinity scope, which is called 421 repatriation. Once started, the scheduler is free to move the worker 422 anywhere in the system as it sees fit. This enables benefiting from scope 423 locality while still being able to utilize other CPUs if necessary and 424 available. 425 426 If set to 1, all workers of the scope are guaranteed always to be in the 427 scope. This may be useful when crossing affinity scopes has other 428 implications, for example, in terms of power consumption or workload 429 isolation. Strict NUMA scope can also be used to match the workqueue 430 behavior of older kernels. 431 432 433 Affinity Scopes and Performance 434 =============================== 435 436 It'd be ideal if an unbound workqueue's behavior is optimal for vast 437 majority of use cases without further tuning. Unfortunately, in the current 438 kernel, there exists a pronounced trade-off between locality and utilization 439 necessitating explicit configurations when workqueues are heavily used. 440 441 Higher locality leads to higher efficiency where more work is performed for 442 the same number of consumed CPU cycles. However, higher locality may also 443 cause lower overall system utilization if the work items are not spread 444 enough across the affinity scopes by the issuers. The following performance 445 testing with dm-crypt clearly illustrates this trade-off. 446 447 The tests are run on a CPU with 12-cores/24-threads split across four L3 448 caches (AMD Ryzen 9 3900x). CPU clock boost is turned off for consistency. 449 ``/dev/dm-0`` is a dm-crypt device created on NVME SSD (Samsung 990 PRO) and 450 opened with ``cryptsetup`` with default settings. 451 452 453 Scenario 1: Enough issuers and work spread across the machine 454 ------------------------------------------------------------- 455 456 The command used: :: 457 458 $ fio --filename=/dev/dm-0 --direct=1 --rw=randrw --bs=32k --ioengine=libaio \ 459 --iodepth=64 --runtime=60 --numjobs=24 --time_based --group_reporting \ 460 --name=iops-test-job --verify=sha512 461 462 There are 24 issuers, each issuing 64 IOs concurrently. ``--verify=sha512`` 463 makes ``fio`` generate and read back the content each time which makes 464 execution locality matter between the issuer and ``kcryptd``. The following 465 are the read bandwidths and CPU utilizations depending on different affinity 466 scope settings on ``kcryptd`` measured over five runs. Bandwidths are in 467 MiBps, and CPU util in percents. 468 469 .. list-table:: 470 :widths: 16 20 20 471 :header-rows: 1 472 473 * - Affinity 474 - Bandwidth (MiBps) 475 - CPU util (%) 476 477 * - system 478 - 1159.40 ±1.34 479 - 99.31 ±0.02 480 481 * - cache 482 - 1166.40 ±0.89 483 - 99.34 ±0.01 484 485 * - cache (strict) 486 - 1166.00 ±0.71 487 - 99.35 ±0.01 488 489 With enough issuers spread across the system, there is no downside to 490 "cache", strict or otherwise. All three configurations saturate the whole 491 machine but the cache-affine ones outperform by 0.6% thanks to improved 492 locality. 493 494 495 Scenario 2: Fewer issuers, enough work for saturation 496 ----------------------------------------------------- 497 498 The command used: :: 499 500 $ fio --filename=/dev/dm-0 --direct=1 --rw=randrw --bs=32k \ 501 --ioengine=libaio --iodepth=64 --runtime=60 --numjobs=8 \ 502 --time_based --group_reporting --name=iops-test-job --verify=sha512 503 504 The only difference from the previous scenario is ``--numjobs=8``. There are 505 a third of the issuers but is still enough total work to saturate the 506 system. 507 508 .. list-table:: 509 :widths: 16 20 20 510 :header-rows: 1 511 512 * - Affinity 513 - Bandwidth (MiBps) 514 - CPU util (%) 515 516 * - system 517 - 1155.40 ±0.89 518 - 97.41 ±0.05 519 520 * - cache 521 - 1154.40 ±1.14 522 - 96.15 ±0.09 523 524 * - cache (strict) 525 - 1112.00 ±4.64 526 - 93.26 ±0.35 527 528 This is more than enough work to saturate the system. Both "system" and 529 "cache" are nearly saturating the machine but not fully. "cache" is using 530 less CPU but the better efficiency puts it at the same bandwidth as 531 "system". 532 533 Eight issuers moving around over four L3 cache scope still allow "cache 534 (strict)" to mostly saturate the machine but the loss of work conservation 535 is now starting to hurt with 3.7% bandwidth loss. 536 537 538 Scenario 3: Even fewer issuers, not enough work to saturate 539 ----------------------------------------------------------- 540 541 The command used: :: 542 543 $ fio --filename=/dev/dm-0 --direct=1 --rw=randrw --bs=32k \ 544 --ioengine=libaio --iodepth=64 --runtime=60 --numjobs=4 \ 545 --time_based --group_reporting --name=iops-test-job --verify=sha512 546 547 Again, the only difference is ``--numjobs=4``. With the number of issuers 548 reduced to four, there now isn't enough work to saturate the whole system 549 and the bandwidth becomes dependent on completion latencies. 550 551 .. list-table:: 552 :widths: 16 20 20 553 :header-rows: 1 554 555 * - Affinity 556 - Bandwidth (MiBps) 557 - CPU util (%) 558 559 * - system 560 - 993.60 ±1.82 561 - 75.49 ±0.06 562 563 * - cache 564 - 973.40 ±1.52 565 - 74.90 ±0.07 566 567 * - cache (strict) 568 - 828.20 ±4.49 569 - 66.84 ±0.29 570 571 Now, the tradeoff between locality and utilization is clearer. "cache" shows 572 2% bandwidth loss compared to "system" and "cache (struct)" whopping 20%. 573 574 575 Conclusion and Recommendations 576 ------------------------------ 577 578 In the above experiments, the efficiency advantage of the "cache" affinity 579 scope over "system" is, while consistent and noticeable, small. However, the 580 impact is dependent on the distances between the scopes and may be more 581 pronounced in processors with more complex topologies. 582 583 While the loss of work-conservation in certain scenarios hurts, it is a lot 584 better than "cache (strict)" and maximizing workqueue utilization is 585 unlikely to be the common case anyway. As such, "cache" is the default 586 affinity scope for unbound pools. 587 588 * As there is no one option which is great for most cases, workqueue usages 589 that may consume a significant amount of CPU are recommended to configure 590 the workqueues using ``apply_workqueue_attrs()`` and/or enable 591 ``WQ_SYSFS``. 592 593 * An unbound workqueue with strict "cpu" affinity scope behaves the same as 594 ``WQ_CPU_INTENSIVE`` per-cpu workqueue. There is no real advanage to the 595 latter and an unbound workqueue provides a lot more flexibility. 596 597 * Affinity scopes are introduced in Linux v6.5. To emulate the previous 598 behavior, use strict "numa" affinity scope. 599 600 * The loss of work-conservation in non-strict affinity scopes is likely 601 originating from the scheduler. There is no theoretical reason why the 602 kernel wouldn't be able to do the right thing and maintain 603 work-conservation in most cases. As such, it is possible that future 604 scheduler improvements may make most of these tunables unnecessary. 605 606 607 Examining Configuration 608 ======================= 609 610 Use tools/workqueue/wq_dump.py to examine unbound CPU affinity 611 configuration, worker pools and how workqueues map to the pools: :: 612 613 $ tools/workqueue/wq_dump.py 614 Affinity Scopes 615 =============== 616 wq_unbound_cpumask=0000000f 617 618 CPU 619 nr_pods 4 620 pod_cpus [0]=00000001 [1]=00000002 [2]=00000004 [3]=00000008 621 pod_node [0]=0 [1]=0 [2]=1 [3]=1 622 cpu_pod [0]=0 [1]=1 [2]=2 [3]=3 623 624 SMT 625 nr_pods 4 626 pod_cpus [0]=00000001 [1]=00000002 [2]=00000004 [3]=00000008 627 pod_node [0]=0 [1]=0 [2]=1 [3]=1 628 cpu_pod [0]=0 [1]=1 [2]=2 [3]=3 629 630 CACHE (default) 631 nr_pods 2 632 pod_cpus [0]=00000003 [1]=0000000c 633 pod_node [0]=0 [1]=1 634 cpu_pod [0]=0 [1]=0 [2]=1 [3]=1 635 636 NUMA 637 nr_pods 2 638 pod_cpus [0]=00000003 [1]=0000000c 639 pod_node [0]=0 [1]=1 640 cpu_pod [0]=0 [1]=0 [2]=1 [3]=1 641 642 SYSTEM 643 nr_pods 1 644 pod_cpus [0]=0000000f 645 pod_node [0]=-1 646 cpu_pod [0]=0 [1]=0 [2]=0 [3]=0 647 648 Worker Pools 649 ============ 650 pool[00] ref= 1 nice= 0 idle/workers= 4/ 4 cpu= 0 651 pool[01] ref= 1 nice=-20 idle/workers= 2/ 2 cpu= 0 652 pool[02] ref= 1 nice= 0 idle/workers= 4/ 4 cpu= 1 653 pool[03] ref= 1 nice=-20 idle/workers= 2/ 2 cpu= 1 654 pool[04] ref= 1 nice= 0 idle/workers= 4/ 4 cpu= 2 655 pool[05] ref= 1 nice=-20 idle/workers= 2/ 2 cpu= 2 656 pool[06] ref= 1 nice= 0 idle/workers= 3/ 3 cpu= 3 657 pool[07] ref= 1 nice=-20 idle/workers= 2/ 2 cpu= 3 658 pool[08] ref=42 nice= 0 idle/workers= 6/ 6 cpus=0000000f 659 pool[09] ref=28 nice= 0 idle/workers= 3/ 3 cpus=00000003 660 pool[10] ref=28 nice= 0 idle/workers= 17/ 17 cpus=0000000c 661 pool[11] ref= 1 nice=-20 idle/workers= 1/ 1 cpus=0000000f 662 pool[12] ref= 2 nice=-20 idle/workers= 1/ 1 cpus=00000003 663 pool[13] ref= 2 nice=-20 idle/workers= 1/ 1 cpus=0000000c 664 665 Workqueue CPU -> pool 666 ===================== 667 [ workqueue \ CPU 0 1 2 3 dfl] 668 events percpu 0 2 4 6 669 events_highpri percpu 1 3 5 7 670 events_long percpu 0 2 4 6 671 events_unbound unbound 9 9 10 10 8 672 events_freezable percpu 0 2 4 6 673 events_power_efficient percpu 0 2 4 6 674 events_freezable_pwr_ef percpu 0 2 4 6 675 rcu_gp percpu 0 2 4 6 676 rcu_par_gp percpu 0 2 4 6 677 slub_flushwq percpu 0 2 4 6 678 netns ordered 8 8 8 8 8 679 ... 680 681 See the command's help message for more info. 682 683 684 Monitoring 685 ========== 686 687 Use tools/workqueue/wq_monitor.py to monitor workqueue operations: :: 688 689 $ tools/workqueue/wq_monitor.py events 690 total infl CPUtime CPUhog CMW/RPR mayday rescued 691 events 18545 0 6.1 0 5 - - 692 events_highpri 8 0 0.0 0 0 - - 693 events_long 3 0 0.0 0 0 - - 694 events_unbound 38306 0 0.1 - 7 - - 695 events_freezable 0 0 0.0 0 0 - - 696 events_power_efficient 29598 0 0.2 0 0 - - 697 events_freezable_pwr_ef 10 0 0.0 0 0 - - 698 sock_diag_events 0 0 0.0 0 0 - - 699 700 total infl CPUtime CPUhog CMW/RPR mayday rescued 701 events 18548 0 6.1 0 5 - - 702 events_highpri 8 0 0.0 0 0 - - 703 events_long 3 0 0.0 0 0 - - 704 events_unbound 38322 0 0.1 - 7 - - 705 events_freezable 0 0 0.0 0 0 - - 706 events_power_efficient 29603 0 0.2 0 0 - - 707 events_freezable_pwr_ef 10 0 0.0 0 0 - - 708 sock_diag_events 0 0 0.0 0 0 - - 709 710 ... 711 712 See the command's help message for more info. 713 714 715 Debugging 716 ========= 717 718 Because the work functions are executed by generic worker threads 719 there are a few tricks needed to shed some light on misbehaving 720 workqueue users. 721 722 Worker threads show up in the process list as: :: 723 724 root 5671 0.0 0.0 0 0 ? S 12:07 0:00 [kworker/0:1] 725 root 5672 0.0 0.0 0 0 ? S 12:07 0:00 [kworker/1:2] 726 root 5673 0.0 0.0 0 0 ? S 12:12 0:00 [kworker/0:0] 727 root 5674 0.0 0.0 0 0 ? S 12:13 0:00 [kworker/1:0] 728 729 If kworkers are going crazy (using too much cpu), there are two types 730 of possible problems: 731 732 1. Something being scheduled in rapid succession 733 2. A single work item that consumes lots of cpu cycles 734 735 The first one can be tracked using tracing: :: 736 737 $ echo workqueue:workqueue_queue_work > /sys/kernel/tracing/set_event 738 $ cat /sys/kernel/tracing/trace_pipe > out.txt 739 (wait a few secs) 740 ^C 741 742 If something is busy looping on work queueing, it would be dominating 743 the output and the offender can be determined with the work item 744 function. 745 746 For the second type of problems it should be possible to just check 747 the stack trace of the offending worker thread. :: 748 749 $ cat /proc/THE_OFFENDING_KWORKER/stack 750 751 The work item's function should be trivially visible in the stack 752 trace. 753 754 755 Non-reentrance Conditions 756 ========================= 757 758 Workqueue guarantees that a work item cannot be re-entrant if the following 759 conditions hold after a work item gets queued: 760 761 1. The work function hasn't been changed. 762 2. No one queues the work item to another workqueue. 763 3. The work item hasn't been reinitiated. 764 765 In other words, if the above conditions hold, the work item is guaranteed to be 766 executed by at most one worker system-wide at any given time. 767 768 Note that requeuing the work item (to the same queue) in the self function 769 doesn't break these conditions, so it's safe to do. Otherwise, caution is 770 required when breaking the conditions inside a work function. 771 772 773 Kernel Inline Documentations Reference 774 ====================================== 775 776 .. kernel-doc:: include/linux/workqueue.h 777 778 .. kernel-doc:: kernel/workqueue.c
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