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| author | Tejun Heo <tj@kernel.org> | 2023-05-17 17:02:09 -1000 |
|---|---|---|
| committer | Tejun Heo <tj@kernel.org> | 2023-05-17 17:02:09 -1000 |
| commit | 8a1dd1e547c1a037692e7a6da6a76108108c72b1 (patch) | |
| tree | 8441f7c4e566a76ecc0bb4d94091d0997f05f8f4 /kernel/workqueue.c | |
| parent | 6363845005202148b8409ec3082e80845c19d309 (diff) | |
| download | linux-8a1dd1e547c1a037692e7a6da6a76108108c72b1.tar.gz | |
workqueue: Track and monitor per-workqueue CPU time usage
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>
Diffstat (limited to 'kernel/workqueue.c')
| -rw-r--r-- | kernel/workqueue.c | 3 |
1 files changed, 3 insertions, 0 deletions
diff --git a/kernel/workqueue.c b/kernel/workqueue.c index 4ca66384d288c..ee16ddb0647c6 100644 --- a/kernel/workqueue.c +++ b/kernel/workqueue.c @@ -212,6 +212,7 @@ struct worker_pool { enum pool_workqueue_stats { PWQ_STAT_STARTED, /* work items started execution */ PWQ_STAT_COMPLETED, /* work items completed execution */ + PWQ_STAT_CPU_TIME, /* total CPU time consumed */ PWQ_STAT_CPU_INTENSIVE, /* wq_cpu_intensive_thresh_us violations */ PWQ_STAT_CM_WAKEUP, /* concurrency-management worker wakeups */ PWQ_STAT_MAYDAY, /* maydays to rescuer */ @@ -1136,6 +1137,8 @@ void wq_worker_tick(struct task_struct *task) if (!pwq) return; + pwq->stats[PWQ_STAT_CPU_TIME] += TICK_USEC; + /* * If the current worker is concurrency managed and hogged the CPU for * longer than wq_cpu_intensive_thresh_us, it's automatically marked |