Lack of visibility has always been a pain point for workqueues. While the
recently added wq_monitor.py improved the situation, it's still difficult to
understand what worker pools are active in the system, how workqueues map to
them and why. The lack of visibility into how workqueues are configured is
going to become more noticeable as workqueue improves locality awareness and
provides more mechanisms to customize locality related behaviors.
Now that the basic framework for more flexible locality support is in place,
this is a good time to improve the situation. This patch adds
tools/workqueues/wq_dump.py which prints out the topology configuration,
worker pools and how workqueues are mapped to pools. Read the command's help
message for more details.
Signed-off-by: Tejun Heo <tj@kernel.org>
Now that wq_worker_tick() is there, we can easily track the rough CPU time
consumption of each workqueue by charging the whole tick whenever a tick
hits an active workqueue. While not super accurate, it provides reasonable
visibility into the workqueues that consume a lot of CPU cycles.
wq_monitor.py is updated to report the per-workqueue CPU times.
v2: wq_monitor.py was using "cputime" as the key when outputting in json
format. Use "cpu_time" instead for consistency with other fields.
Signed-off-by: Tejun Heo <tj@kernel.org>
If a per-cpu work item hogs the CPU, it can prevent other work items from
starting through concurrency management. A per-cpu workqueue which intends
to host such CPU-hogging work items can choose to not participate in
concurrency management by setting %WQ_CPU_INTENSIVE; however, this can be
error-prone and difficult to debug when missed.
This patch adds an automatic CPU usage based detection. If a
concurrency-managed work item consumes more CPU time than the threshold
(10ms by default) continuously without intervening sleeps, wq_worker_tick()
which is called from scheduler_tick() will detect the condition and
automatically mark it CPU_INTENSIVE.
The mechanism isn't foolproof:
* Detection depends on tick hitting the work item. Getting preempted at the
right timings may allow a violating work item to evade detection at least
temporarily.
* nohz_full CPUs may not be running ticks and thus can fail detection.
* Even when detection is working, the 10ms detection delays can add up if
many CPU-hogging work items are queued at the same time.
However, in vast majority of cases, this should be able to detect violations
reliably and provide reasonable protection with a small increase in code
complexity.
If some work items trigger this condition repeatedly, the bigger problem
likely is the CPU being saturated with such per-cpu work items and the
solution would be making them UNBOUND. The next patch will add a debug
mechanism to help spot such cases.
v4: Documentation for workqueue.cpu_intensive_thresh_us added to
kernel-parameters.txt.
v3: Switch to use wq_worker_tick() instead of hooking into preemptions as
suggested by Peter.
v2: Lai pointed out that wq_worker_stopping() also needs to be called from
preemption and rtlock paths and an earlier patch was updated
accordingly. This patch adds a comment describing the risk of infinte
recursions and how they're avoided.
Signed-off-by: Tejun Heo <tj@kernel.org>
Acked-by: Peter Zijlstra <peterz@infradead.org>
Cc: Linus Torvalds <torvalds@linux-foundation.org>
Cc: Lai Jiangshan <jiangshanlai@gmail.com>
Currently, the only way to peer into workqueue operations is through
tracing. While possible, it isn't easy or convenient to monitor
per-workqueue behaviors over time this way. Let's add pwq->stats[] that
track relevant events and a drgn monitoring script -
tools/workqueue/wq_monitor.py.
It's arguable whether this needs to be configurable. However, it currently
only has several counters and the runtime overhead shouldn't be noticeable
given that they're on pwq's which are per-cpu on per-cpu workqueues and
per-numa-node on unbound ones. Let's keep it simple for the time being.
v2: Patch reordered to earlier with fewer fields. Field will be added back
gradually. Help message improved.
Signed-off-by: Tejun Heo <tj@kernel.org>
Cc: Lai Jiangshan <jiangshanlai@gmail.com>
