One common question you might want answered is which CPUs are running threads and for how long. You can use the on-cpu and off-cpu probes to easily answer this question on a system-wide basis as shown in the following example:
sched:::on-cpu
{
self->ts = timestamp;
}
sched:::off-cpu
/self->ts/
{
@[cpu] = quantize(timestamp - self->ts);
self->ts = 0;
}
Running the above script results in output similar to the following example:
# dtrace -s ./where.ddtrace: script './where.d' matched 5 probes^C0 value ------------- Distribution ------------- count 2048 | 0 4096 |@@ 37 8192 |@@@@@@@@@@@@@ 212 16384 |@ 30 32768 | 10 65536 |@ 17 131072 | 12 262144 | 9 524288 | 6 1048576 | 5 2097152 | 1 4194304 | 3 8388608 |@@@@ 75 16777216 |@@@@@@@@@@@@ 201 33554432 | 6 67108864 | 0 1 value ------------- Distribution ------------- count 2048 | 0 4096 |@ 6 8192 |@@@@ 23 16384 |@@@ 18 32768 |@@@@ 22 65536 |@@@@ 22 131072 |@ 7 262144 | 5 524288 | 2 1048576 | 3 2097152 |@ 9 4194304 | 4 8388608 |@@@ 18 16777216 |@@@ 19 33554432 |@@@ 16 67108864 |@@@@ 21 134217728 |@@ 14 268435456 | 0
The above output shows that on CPU 1 threads tend to run for less than 100 microseconds at a stretch, or for approximately 10 milliseconds. A noticable gap between the two clusters of data shown in the histogram. You also might be interested in knowing which CPUs are running a particular process. You can use the on-cpu and off-cpu probes for answering this question as well. The following script displays which CPUs run a specified application over a period of ten seconds:
#pragma D option quiet
dtrace:::BEGIN
{
start = timestamp;
}
sched:::on-cpu
/execname == $$1/
{
self->ts = timestamp;
}
sched:::off-cpu
/self->ts/
{
@[cpu] = sum(timestamp - self->ts);
self->ts = 0;
}
profile:::tick-1sec
/++x == 10/
{
exit(0);
}
dtrace:::END
{
printf("CPU distribution over %d seconds:\n\n",
(timestamp - start) / 1000000000);
printf("CPU microseconds\n--- ------------\n");
normalize(@, 1000);
printa("%3d %@d\n", @);
}
Running the above script on a large mail server and specifying the IMAP daemon results in output similar to the following example:
# dtrace -s ./whererun.d imapd
CPU distribution of imapd over 10 seconds:
CPU microseconds
--- ------------
15 10102
12 16377
21 25317
19 25504
17 35653
13 41539
14 46669
20 57753
22 70088
16 115860
23 127775
18 160517
Solaris takes into account the amount of time that a thread has been sleeping when selecting a CPU on which to run the thread: a thread that has been sleeping for less time tends not to migrate. You can use the off-cpu and on-cpu probes to observe this behavior:
sched:::off-cpu
/curlwpsinfo->pr_state == SSLEEP/
{
self->cpu = cpu;
self->ts = timestamp;
}
sched:::on-cpu
/self->ts/
{
@[self->cpu == cpu ?
"sleep time, no CPU migration" : "sleep time, CPU migration"] =
lquantize((timestamp - self->ts) / 1000000, 0, 500, 25);
self->ts = 0;
self->cpu = 0;
}
Running the above script for approximately 30 seconds results in output similar to the following example:
# dtrace -s ./howlong.ddtrace: script './howlong.d' matched 5 probes^Csleep time, CPU migration value -------------- Distribution ------------ count < 0 | 0 0 |@@@@@@@ 6838 25 |@@@@@ 4714 50 |@@@ 3108 75 |@ 1304 100 |@ 1557 125 |@ 1425 150 | 894 175 |@ 1526 200 |@@ 2010 225 |@@ 1933 250 |@@ 1982 275 |@@ 2051 300 |@@ 2021 325 |@ 1708 350 |@ 1113 375 | 502 400 | 220 425 | 106 450 | 54 475 | 40 >= 500 |@ 1716 sleep time, no CPU migration value -------------- Distribution ------------ count < 0 | 0 0 |@@@@@@@@@@@@ 58413 25 |@@@ 14793 50 |@@ 10050 75 | 3858 100 |@ 6242 125 |@ 6555 150 | 3980 175 |@ 5987 200 |@ 9024 225 |@ 9070 250 |@@ 10745 275 |@@ 11898 300 |@@ 11704 325 |@@ 10846 350 |@ 6962 375 | 3292 400 | 1713 425 | 585 450 | 201 475 | 96 >= 500 | 3946
The example output shows that there are many more occurences of non-migration than migration. Also, when sleep times are longer, migrations are more likely. The distributions are noticeably different in the sub-100 millisecond range, but look very similar as the sleep times get longer. This result would seem to indicate that sleep time is not factored into the scheduling decision once a certain threshold is exceeded.
The final example using off-cpu and on-cpu shows how to use these probes along with the pr_stype field to determine why threads sleep and for how long:
sched:::off-cpu
/curlwpsinfo->pr_state == SSLEEP/
{
/*
* We're sleeping. Track our sobj type.
*/
self->sobj = curlwpsinfo->pr_stype;
self->bedtime = timestamp;
}
sched:::off-cpu
/curlwpsinfo->pr_state == SRUN/
{
self->bedtime = timestamp;
}
sched:::on-cpu
/self->bedtime && !self->sobj/
{
@["preempted"] = quantize(timestamp - self->bedtime);
self->bedtime = 0;
}
sched:::on-cpu
/self->sobj/
{
@[self->sobj == SOBJ_MUTEX ? "kernel-level lock" :
self->sobj == SOBJ_RWLOCK ? "rwlock" :
self->sobj == SOBJ_CV ? "condition variable" :
self->sobj == SOBJ_SEMA ? "semaphore" :
self->sobj == SOBJ_USER ? "user-level lock" :
self->sobj == SOBJ_USER_PI ? "user-level prio-inheriting lock" :
self->sobj == SOBJ_SHUTTLE ? "shuttle" : "unknown"] =
quantize(timestamp - self->bedtime);
self->sobj = 0;
self->bedtime = 0;
}
Running the above script for several seconds results in output similar to the following example:
# dtrace -s ./whatfor.ddtrace: script './whatfor.d' matched 12 probes^Ckernel-level lock value -------------- Distribution ------------ count 16384 | 0 32768 |@@@@@@@@ 3 65536 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ 11 131072 |@@ 1 262144 | 0 preempted value -------------- Distribution ------------ count 16384 | 0 32768 | 4 65536 |@@@@@@@@ 408 131072 |@@@@@@@@@@@@@@@@@@@@@@ 1031 262144 |@@@ 156 524288 |@@ 116 1048576 |@ 51 2097152 | 42 4194304 | 16 8388608 | 15 16777216 | 4 33554432 | 8 67108864 | 0 semaphore value -------------- Distribution ------------ count 32768 | 0 65536 |@@ 61 131072 |@@@@@@@@@@@@@@@@@@@@@@@@ 553 262144 |@@ 63 524288 |@ 36 1048576 | 7 2097152 | 22 4194304 |@ 44 8388608 |@@@ 84 16777216 |@ 36 33554432 | 3 67108864 | 6 134217728 | 0 268435456 | 0 536870912 | 0 1073741824 | 0 2147483648 | 0 4294967296 | 0 8589934592 | 0 17179869184 | 1 34359738368 | 0 shuttle value -------------- Distribution ------------ count 32768 | 0 65536 |@@@@@ 2 131072 |@@@@@@@@@@@@@@@@ 6 262144 |@@@@@ 2 524288 | 0 1048576 | 0 2097152 | 0 4194304 |@@@@@ 2 8388608 | 0 16777216 | 0 33554432 | 0 67108864 | 0 134217728 | 0 268435456 | 0 536870912 | 0 1073741824 | 0 2147483648 | 0 4294967296 |@@@@@ 2 8589934592 | 0 17179869184 |@@ 1 34359738368 | 0 condition variable value -------------- Distribution ------------ count 32768 | 0 65536 | 122 131072 |@@@@@ 1579 262144 |@ 340 524288 | 268 1048576 |@@@ 1028 2097152 |@@@ 1007 4194304 |@@@ 1176 8388608 |@@@@ 1257 16777216 |@@@@@@@@@@@@@@ 4385 33554432 | 295 67108864 | 157 134217728 | 96 268435456 | 48 536870912 | 144 1073741824 | 10 2147483648 | 22 4294967296 | 18 8589934592 | 5 17179869184 | 6 34359738368 | 4 68719476736 | 0
When a CPU becomes idle, the dispatcher looks for work enqueued on other (non-idle) CPUs. The following example uses the dequeue probe to understand how often applications are transferred and by which CPU:
#pragma D option quiet
sched:::dequeue
/args[2]->cpu_id != -1 && cpu != args[2]->cpu_id &&
(curlwpsinfo->pr_flag & PR_IDLE)/
{
@[stringof(args[1]->pr_fname), args[2]->cpu_id] =
lquantize(cpu, 0, 100);
}
END
{
printa("%s stolen from CPU %d by:\n%@d\n", @);
}
The tail of the output from running the above script on a 4 CPU system results in output similar to the following example:
# dtrace -s ./whosteal.d^C... nscd stolen from CPU 1 by: value -------------- Distribution ------------ count 1 | 0 2 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ 28 3 | 0 snmpd stolen from CPU 1 by: value -------------- Distribution ------------ count < 0 | 0 0 |@ 1 1 | 0 2 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ 31 3 |@@ 2 4 | 0 sched stolen from CPU 1 by: value -------------- Distribution ------------ count < 0 | 0 0 |@@ 3 1 | 0 2 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ 36 3 |@@@@ 5 4 | 0
Instead of knowing which CPUs took which work, you might want to know the CPUs on which processes and threads are waiting to run. You can use the enqueue and dequeue probes together to answer this question:
sched:::enqueue
{
self->ts = timestamp;
}
sched:::dequeue
/self->ts/
{
@[args[2]->cpu_id] = quantize(timestamp - self->ts);
self->ts = 0;
}
Running the above script for several seconds results in output similar to the following example:
# dtrace -s ./qtime.ddtrace: script './qtime.d' matched 5 probes^C-1 value -------------- Distribution ------------ count 4096 | 0 8192 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ 2 16384 | 0 0 value -------------- Distribution ------------ count 1024 | 0 2048 |@@@@@@@@@@@@@@@ 262 4096 |@@@@@@@@@@@@@ 227 8192 |@@@@@ 87 16384 |@@@ 54 32768 | 7 65536 | 9 131072 | 1 262144 | 5 524288 | 4 1048576 | 2 2097152 | 0 4194304 | 0 8388608 | 0 16777216 | 1 33554432 | 2 67108864 | 2 134217728 | 0 268435456 | 0 536870912 | 0 1073741824 | 1 2147483648 | 1 4294967296 | 0 1 value -------------- Distribution ------------ count 1024 | 0 2048 |@@@@ 49 4096 |@@@@@@@@@@@@@@@@@@@@ 241 8192 |@@@@@@@ 91 16384 |@@@@ 55 32768 | 7 65536 | 3 131072 | 2 262144 | 1 524288 | 0 1048576 | 0 2097152 | 0 4194304 | 0 8388608 | 0 16777216 | 0 33554432 | 3 67108864 | 1 134217728 | 4 268435456 | 2 536870912 | 0 1073741824 | 3 2147483648 | 2 4294967296 | 0
Notice the non-zero values at the bottom of the example output. These data points reveal several instances on both CPUs where a thread was enqueued to run for several seconds.
Instead of looking at wait times, you might want to examine the length of the run queue over time. Using the enqueue and dequeue probes, you can set up an associative array to track the queue length:
sched:::enqueue
{
this->len = qlen[args[2]->cpu_id]++;
@[args[2]->cpu_id] = lquantize(this->len, 0, 100);
}
sched:::dequeue
/qlen[args[2]->cpu_id]/
{
qlen[args[2]->cpu_id]—;
}
Running the above script for approximately 30 seconds on a largely idle uniprocessor laptop system results in output similar to the following example:
# dtrace -s ./qlen.ddtrace: script './qlen.d' matched 5 probes^C0 value -------------- Distribution ------------ count < 0 | 0 0 |@@@@@@@@@@@@@@@@@@@@@@@@@ 110626 1 |@@@@@@@@@ 41142 2 |@@ 12655 3 |@ 5074 4 | 1722 5 | 701 6 | 302 7 | 63 8 | 23 9 | 12 10 | 24 11 | 58 12 | 14 13 | 3 14 | 0
The output is roughly what you would expect for an idle system: the majority of the time that a runnable thread is enqueued, the run queue was very short (three or fewer threads in length). However, given that the system was largely idle, the exceptional data points at the bottom of the table might be unexpected. For example, why was the run queue as long as 13 runnable threads? To explore this question, you could write a D script that displays the contents of the run queue when the length of the run queue is long. This problem is complicated because D enablings cannot iterate over data structures, and therefore cannot simply iterate over the entire run queue. Even if D enablings could do so, you should avoid dependencies on the kernel's internal data structures.
For this type of script, you would enable the enqueue and dequeue probes and use both speculations and associative arrays. Whenever a thread is enqueued, the script increments the length of the queue and records the timestamp in an associative array keyed by the thread. You cannot use a thread-local variable in this case because a thread might be enqueued by another thread. The script then checks to see if the queue length exceeds the maximum. If it does, the script starts a new speculation, and records the timestamp and the new maximum. Then, when a thread is dequeued, the script compares the enqueue timestamp to the timestamp of the longest length: if the thread was enqueued before the timestamp of the longest length, the thread was in the queue when the longest length was recorded. In this case, the script speculatively traces the thread's information. Once the kernel dequeues the last thread that was enqueued at the timestamp of the longest length, the script commits the speculation data. This script is shown below:
#pragma D option quiet
#pragma D option nspec=4
#pragma D option specsize=100k
int maxlen;
int spec[int];
sched:::enqueue
{
this->len = ++qlen[this->cpu = args[2]->cpu_id];
in[args[0]->pr_addr] = timestamp;
}
sched:::enqueue
/this->len > maxlen && spec[this->cpu]/
{
/*
* There is already a speculation for this CPU. We just set a new
* record, so we'll discard the old one.
*/
discard(spec[this->cpu]);
}
sched:::enqueue
/this->len > maxlen/
{
/*
* We have a winner. Set the new maximum length and set the timestamp
* of the longest length.
*/
maxlen = this->len;
longtime[this->cpu] = timestamp;
/*
* Now start a new speculation, and speculatively trace the length.
*/
this->spec = spec[this->cpu] = speculation();
speculate(this->spec);
printf("Run queue of length %d:\n", this->len);
}
sched:::dequeue
/(this->in = in[args[0]->pr_addr]) &&
this->in <= longtime[this->cpu = args[2]->cpu_id]/
{
speculate(spec[this->cpu]);
printf(" %d/%d (%s)\n",
args[1]->pr_pid, args[0]->pr_lwpid,
stringof(args[1]->pr_fname));
}
sched:::dequeue
/qlen[args[2]->cpu_id]/
{
in[args[0]->pr_addr] = 0;
this->len = --qlen[args[2]->cpu_id];
}
sched:::dequeue
/this->len == 0 && spec[this->cpu]/
{
/*
* We just processed the last thread that was enqueued at the time
* of longest length; commit the speculation, which by now contains
* each thread that was enqueued when the queue was longest.
*/
commit(spec[this->cpu]);
spec[this->cpu] = 0;
}
Running the above script on the same uniprocessor laptop results in output similar to the following example:
# dtrace -s ./whoqueue.dRun queue of length 3: 0/0 (sched) 0/0 (sched) 101170/1 (dtrace) Run queue of length 4: 0/0 (sched) 100356/1 (Xsun) 100420/1 (xterm) 101170/1 (dtrace) Run queue of length 5: 0/0 (sched) 0/0 (sched) 100356/1 (Xsun) 100420/1 (xterm) 101170/1 (dtrace) Run queue of length 7: 0/0 (sched) 100221/18 (nscd) 100221/17 (nscd) 100221/16 (nscd) 100221/13 (nscd) 100221/14 (nscd) 100221/15 (nscd) Run queue of length 16: 100821/1 (xterm) 100768/1 (xterm) 100365/1 (fvwm2) 101118/1 (xterm) 100577/1 (xterm) 101170/1 (dtrace) 101020/1 (xterm) 101089/1 (xterm) 100795/1 (xterm) 100741/1 (xterm) 100710/1 (xterm) 101048/1 (xterm) 100697/1 (MozillaFirebird-) 100420/1 (xterm) 100394/1 (xterm) 100368/1 (xterm)^C
The output reveals that the long run queues are due to many runnable xterm processes. This experiment coincided with a change in virtual desktop, and therefore the results are probably due to some sort of X event processing.
In
enqueue and dequeue
, the final example demonstrated that a burst in run queue length was due to runnable xterm processes. One hypothesis is that the observations resulted from a change in virtual desktop. You can use the wakeup probe to explore this hypothesis by determining who is waking the xterm processes, and when, as shown in the following example:
#pragma D option quiet
dtrace:::BEGIN
{
start = timestamp;
}
sched:::wakeup
/stringof(args[1]->pr_fname) == "xterm"/
{
@[execname] = lquantize((timestamp - start) / 1000000000, 0, 10);
}
profile:::tick-1sec
/++x == 10/
{
exit(0);
}
To investigate the hypothesis, run the above script, waiting roughly five seconds, and switch your virtual desktop exactly once. If the burst of runnable xterm processes is due to switching the virtual desktop, the output should show a burst of wakeup activity at the five second mark.
# dtrace -s ./xterm.d
Xsun
value -------------- Distribution ------------ count
4 | 0
5 |@ 1
6 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ 32
7 | 0
The output does show that the X server is waking xterm processes, clustered around the time that you switched virtual desktops. If you wanted to understand the interaction between the X server and the xterm processes, you could aggregate on user stack traces when the X server fires the wakeup probe.
Understanding the performance of client/server systems like the X windowing system requires understanding the clients on whose behalf the server is doing work. This kind of question is difficult to answer with conventional performance analysis tools. However, if you have a model where a client sends a message to the server and sleeps pending the server's processing, you can use the wakeup probe to determine the client for whom the request is being performed, as shown in the following example:
self int last;
sched:::wakeup
/self->last && args[0]->pr_stype == SOBJ_CV/
{
@[stringof(args[1]->pr_fname)] = sum(vtimestamp - self->last);
self->last = 0;
}
sched:::wakeup
/execname == "Xsun" && self->last == 0/
{
self->last = vtimestamp;
}
Running the above script results in output similar to the following example:
dtrace -s ./xwork.ddtrace: script './xwork.d' matched 14 probes^Cxterm 9522510 soffice.bin 9912594 fvwm2 100423123 MozillaFirebird 312227077 acroread 345901577
This output reveals that much Xsun work is being done on behalf of the processes acroread, MozillaFirebird and, to a lesser degree, fvwm2. Notice that the script only examined wakeups from condition variable synchronization objects (SOBJ_CV). As described in Table 25–4, condition variables are the type of synchronization object typically used to synchronize for reasons other than access to a shared data region. In the case of the X server, a client will wait for data in a pipe by sleeping on a condition variable.
You can additionally use the sleep probe along with the wakeup probe to understand which applications are blocking on which applications, and for how long, as shown in the following example:
#pragma D option quiet
sched:::sleep
/!(curlwpsinfo->pr_flag & PR_ISSYS) && curlwpsinfo->pr_stype == SOBJ_CV/
{
bedtime[curlwpsinfo->pr_addr] = timestamp;
}
sched:::wakeup
/bedtime[args[0]->pr_addr]/
{
@[stringof(args[1]->pr_fname), execname] =
quantize(timestamp - bedtime[args[0]->pr_addr]);
bedtime[args[0]->pr_addr] = 0;
}
END
{
printa("%s sleeping on %s:\n%@d\n", @);
}
The tail of the output from running the example script for several seconds on a desktop system resembles the following example:
# dtrace -s ./whofor.d^C... xterm sleeping on Xsun: value -------------- Distribution ------------ count 131072 | 0 262144 | 12 524288 | 2 1048576 | 0 2097152 | 5 4194304 |@@@ 45 8388608 | 1 16777216 | 9 33554432 |@@@@@ 83 67108864 |@@@@@@@@@@@ 164 134217728 |@@@@@@@@@@ 147 268435456 |@@@@ 56 536870912 |@ 17 1073741824 | 9 2147483648 | 1 4294967296 | 3 8589934592 | 1 17179869184 | 0 fvwm2 sleeping on Xsun: value -------------- Distribution ------------ count 32768 | 0 65536 |@@@@@@@@@@@@@@@@@@@@@@ 67 131072 |@@@@@ 16 262144 |@@ 6 524288 |@ 3 1048576 |@@@@@ 15 2097152 | 0 4194304 | 0 8388608 | 1 16777216 | 0 33554432 | 0 67108864 | 1 134217728 | 0 268435456 | 0 536870912 | 1 1073741824 | 1 2147483648 | 2 4294967296 | 2 8589934592 | 2 17179869184 | 0 34359738368 | 2 68719476736 | 0 syslogd sleeping on syslogd: value -------------- Distribution ------------ count 17179869184 | 0 34359738368 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ 3 68719476736 | 0 MozillaFirebird sleeping on MozillaFirebird: value -------------- Distribution ------------ count 65536 | 0 131072 | 3 262144 |@@ 14 524288 | 0 1048576 |@@@ 18 2097152 | 0 4194304 | 0 8388608 | 1 16777216 | 0 33554432 | 1 67108864 | 3 134217728 |@ 7 268435456 |@@@@@@@@@@ 53 536870912 |@@@@@@@@@@@@@@ 78 1073741824 |@@@@ 25 2147483648 | 0 4294967296 | 0 8589934592 |@ 7 17179869184 | 0
You might want to understand how and why MozillaFirebird is blocking on itself. You could modify the above script as shown in the following example to answer this question:
#pragma D option quiet
sched:::sleep
/execname == "MozillaFirebird" && curlwpsinfo->pr_stype == SOBJ_CV/
{
bedtime[curlwpsinfo->pr_addr] = timestamp;
}
sched:::wakeup
/execname == "MozillaFirebird" && bedtime[args[0]->pr_addr]/
{
@[args[1]->pr_pid, args[0]->pr_lwpid, pid, curlwpsinfo->pr_lwpid] =
quantize(timestamp - bedtime[args[0]->pr_addr]);
bedtime[args[0]->pr_addr] = 0;
}
sched:::wakeup
/bedtime[args[0]->pr_addr]/
{
bedtime[args[0]->pr_addr] = 0;
}
END
{
printa("%d/%d sleeping on %d/%d:\n%@d\n", @);
}
Running the modified script for several seconds results in output similar to the following example:
# dtrace -s ./firebird.d^C100459/1 sleeping on 100459/13: value -------------- Distribution ------------ count 262144 | 0 524288 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ 1 1048576 | 0 100459/13 sleeping on 100459/1: value -------------- Distribution ------------ count 16777216 | 0 33554432 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ 1 67108864 | 0 100459/1 sleeping on 100459/2: value -------------- Distribution ------------ count 16384 | 0 32768 |@@@@ 5 65536 |@ 2 131072 |@@@@@ 6 262144 | 1 524288 |@ 2 1048576 | 0 2097152 |@@ 3 4194304 |@@@@ 5 8388608 |@@@@@@@@ 9 16777216 |@@@@@ 6 33554432 |@@ 3 67108864 | 0 100459/1 sleeping on 100459/5: value -------------- Distribution ------------ count 16384 | 0 32768 |@@@@@ 12 65536 |@@ 5 131072 |@@@@@@ 15 262144 | 1 524288 | 1 1048576 | 2 2097152 |@ 4 4194304 |@@@@@ 13 8388608 |@@@ 8 16777216 |@@@@@ 13 33554432 |@@ 6 67108864 |@@ 5 134217728 |@ 4 268435456 | 0 536870912 | 1 1073741824 | 0 100459/2 sleeping on 100459/1: value -------------- Distribution ------------ count 16384 | 0 32768 |@@@@@@@@@@@@@@ 11 65536 | 0 131072 |@@ 2 262144 | 0 524288 | 0 1048576 |@@@@ 3 2097152 |@ 1 4194304 |@@ 2 8388608 |@@ 2 16777216 |@ 1 33554432 |@@@@@@ 5 67108864 | 0 134217728 | 0 268435456 | 0 536870912 |@ 1 1073741824 |@ 1 2147483648 |@ 1 4294967296 | 0 100459/5 sleeping on 100459/1: value -------------- Distribution ------------ count 16384 | 0 32768 | 1 65536 | 2 131072 | 4 262144 | 7 524288 | 1 1048576 | 5 2097152 | 10 4194304 |@@@@@@ 77 8388608 |@@@@@@@@@@@@@@@@@@@@@@@ 270 16777216 |@@@ 43 33554432 |@ 20 67108864 |@ 14 134217728 | 5 268435456 | 2 536870912 | 1 1073741824 | 0
You can also use the sleep and wakeup probes to understand the performance of door servers such as the name service cache daemon, as shown in the following example:
sched:::sleep
/curlwpsinfo->pr_stype == SOBJ_SHUTTLE/
{
bedtime[curlwpsinfo->pr_addr] = timestamp;
}
sched:::wakeup
/execname == "nscd" && bedtime[args[0]->pr_addr]/
{
@[stringof(curpsinfo->pr_fname), stringof(args[1]->pr_fname)] =
quantize(timestamp - bedtime[args[0]->pr_addr]);
bedtime[args[0]->pr_addr] = 0;
}
sched:::wakeup
/bedtime[args[0]->pr_addr]/
{
bedtime[args[0]->pr_addr] = 0;
}
The tail of the output from running the above script on a large mail server resembles the following example:
imapd
value -------------- Distribution ------------ count
16384 | 0
32768 | 2
65536 |@@@@@@@@@@@@@@@@@ 57
131072 |@@@@@@@@@@@ 37
262144 | 3
524288 |@@@ 11
1048576 |@@@ 10
2097152 |@@ 9
4194304 | 1
8388608 | 0
mountd
value -------------- Distribution ------------ count
65536 | 0
131072 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ 49
262144 |@@@ 6
524288 | 1
1048576 | 0
2097152 | 0
4194304 |@@@@ 7
8388608 |@ 3
16777216 | 0
sendmail
value -------------- Distribution ------------ count
16384 | 0
32768 |@ 18
65536 |@@@@@@@@@@@@@@@@@ 205
131072 |@@@@@@@@@@@@@ 154
262144 |@ 23
524288 | 5
1048576 |@@@@ 50
2097152 | 7
4194304 | 5
8388608 | 2
16777216 | 0
automountd
value -------------- Distribution ------------ count
32768 | 0
65536 |@@@@@@@@@@ 22
131072 |@@@@@@@@@@@@@@@@@@@@@@@ 51
262144 |@@ 6
524288 | 1
1048576 | 0
2097152 | 2
4194304 | 2
8388608 | 1
16777216 | 1
33554432 | 1
67108864 | 0
134217728 | 0
268435456 | 1
536870912 | 0
You might be interested in the unusual data points for automountd or the persistent data point at over one millisecond for sendmail. You can add additional predicates to the above script to hone in on the causes of any exceptional or anomalous results.
Because Solaris is a preemptive system, higher priority threads preempt lower priority ones. Preemption can induce a significant latency bubble in the lower priority thread, so you might want to know which threads are being preempted by which other threads. The following example shows how to use the preempt and remain-cpu probes to display this information:
#pragma D option quiet
sched:::preempt
{
self->preempt = 1;
}
sched:::remain-cpu
/self->preempt/
{
self->preempt = 0;
}
sched:::off-cpu
/self->preempt/
{
/*
* If we were told to preempt ourselves, see who we ended up giving
* the CPU to.
*/
@[stringof(args[1]->pr_fname), args[0]->pr_pri, execname,
curlwpsinfo->pr_pri] = count();
self->preempt = 0;
}
END
{
printf("%30s %3s %30s %3s %5s\n", "PREEMPTOR", "PRI",
"PREEMPTED", "PRI", "#");
printa("%30s %3d %30s %3d %5@d\n", @);
}
Running the above script for several seconds on a desktop system results in output similar to the following example:
# dtrace -s ./whopreempt.d^CPREEMPTOR PRI PREEMPTED PRI # sched 60 Xsun 53 1 xterm 59 Xsun 53 1 MozillaFirebird 57 Xsun 53 1 mpstat 100 fvwm2 59 1 sched 99 MozillaFirebird 57 1 sched 60 dtrace 30 1 mpstat 100 Xsun 59 2 sched 60 Xsun 54 2 sched 99 sched 60 2 fvwm2 59 Xsun 44 2 sched 99 Xsun 44 2 sched 60 xterm 59 2 sched 99 Xsun 53 2 sched 99 Xsun 54 3 sched 60 fvwm2 59 3 sched 60 Xsun 59 3 sched 99 Xsun 59 4 fvwm2 59 Xsun 54 8 fvwm2 59 Xsun 53 9 Xsun 59 MozillaFirebird 57 10 sched 60 MozillaFirebird 57 14 MozillaFirebird 57 Xsun 44 16 MozillaFirebird 57 Xsun 54 18
Preemption is based on priorities, so you might want to observe changes in priority over time. The following example uses the change-pri probe to display this information:
sched:::change-pri
{
@[stringof(args[0]->pr_clname)] =
lquantize(args[2] - args[0]->pr_pri, -50, 50, 5);
}
The example script captures the degree to which priority is raised or lowered, and aggregates by scheduling class. Running the above script results in output similar to the following example:
# dtrace -s ./pri.ddtrace: script './pri.d' matched 10 probes^CIA value -------------- Distribution ------------ count < -50 | 20 -50 |@ 38 -45 | 4 -40 | 13 -35 | 12 -30 | 18 -25 | 18 -20 | 23 -15 | 6 -10 |@@@@@@@@ 201 -5 |@@@@@@ 160 0 |@@@@@ 138 5 |@ 47 10 |@@ 66 15 |@ 36 20 |@ 26 25 |@ 28 30 | 18 35 | 22 40 | 8 45 | 11 >= 50 |@ 34 TS value -------------- Distribution ------------ count -15 | 0 -10 |@ 1 -5 |@@@@@@@@@@@@ 7 0 |@@@@@@@@@@@@@@@@@@@@ 12 5 | 0 10 |@@@@@ 3 15 | 0
The output shows the priority manipulation of the Interactive (IA) scheduling class. Instead of seeing priority manipulation, you might want to see the priority values of a particular process and thread over time. The following script uses the change-pri probe to display this information:
#pragma D option quiet
BEGIN
{
start = timestamp;
}
sched:::change-pri
/args[1]->pr_pid == $1 && args[0]->pr_lwpid == $2/
{
printf("%d %d\n", timestamp - start, args[2]);
}
tick-1sec
/++n == 5/
{
exit(0);
}
To see the change in priorities over time, type the following command in one window:
$ echo $$139208$ while true ; do let i=0 ; done
In another window, run the script and redirect the output to a file:
# dtrace -s ./pritime.d 139208 1 > /tmp/pritime.out
#
You can use the file /tmp/pritime.out that is generated above as input to plotting software to graphically display priority over time. gnuplot is a freely available plotting package that is included in the Solaris Freeware Companion CD. By default, gnuplot is installed in /opt/sfw/bin.
Solaris uses tick-based CPU accounting, in which a system clock interrupt fires at a fixed interval and attributes CPU utilization to the threads and processes running at the time of the tick. The following example shows how to use the tick probe to observe this attribution:
# dtrace -n sched:::tick'{@[stringof(args[1]->pr_fname)] = count()}'^Carch 1 sh 1 sed 1 echo 1 ls 1 FvwmAuto 1 pwd 1 awk 2 basename 2 expr 2 resize 2 tput 2 uname 2 fsflush 2 dirname 4 vim 9 fvwm2 10 ksh 19 xterm 21 Xsun 93 MozillaFirebird 260
The system clock frequency varies from operating system to operating system, but generally ranges from 25 hertz to 1024 hertz. The Solaris system clock frequency is adjustable, but defaults to 100 hertz.
The tick probe only fires if the system clock detects a runnable thread. To use the tick probe to observe the system clock's frequency, you must have a thread that is always runnable. In one window, create a looping shell as shown in the following example:
$ while true ; do let i=0 ; done
In another window, run the following script:
uint64_t last[int];
sched:::tick
/last[cpu]/
{
@[cpu] = min(timestamp - last[cpu]);
}
sched:::tick
{
last[cpu] = timestamp;
}
# dtrace -s ./ticktime.ddtrace: script './ticktime.d' matched 2 probes^C0 9883789
The minimum interval is 9.8 millisecond, which indicates that the default clock tick frequency is 10 milliseconds (100 hertz). The observed minimum is somewhat less than 10 milliseconds due to jitter.
One deficiency of tick-based accounting is that the system clock that performs accounting is often also responsible for dispatching any time-related scheduling activity. As a result, if a thread is to perform some amount of work every clock tick (that is, every 10 milliseconds), the system will either over-account for the thread or under-account for the thread, depending on whether the accounting is done before or after time-related dispatching scheduling activity. In Solaris, accounting is performed before time-related dispatching. As a result, the system will under-account for threads running at regular interval. If such threads run for less than the clock tick interval, they can effectively “hide” behind the clock tick. The following example shows the degree to which the system has such threads:
sched:::tick,
sched:::enqueue
{
@[probename] = lquantize((timestamp / 1000000) % 10, 0, 10);
}
The output of the example script is two distributions of the millisecond offset within a ten millisecond interval, one for the tick probe and another for enqueue:
# dtrace -s ./tick.ddtrace: script './tick.d' matched 4 probes^Ctick value -------------- Distribution ------------ count 6 | 0 7 |@ 3 8 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ 79 9 | 0 enqueue value -------------- Distribution ------------ count < 0 | 0 0 |@@ 267 1 |@@ 300 2 |@@ 259 3 |@@ 291 4 |@@@ 360 5 |@@ 305 6 |@@ 295 7 |@@@@ 522 8 |@@@@@@@@@@@@ 1315 9 |@@@ 337
The output histogram named tick shows that the clock tick is firing at an 8 millisecond offset. If scheduling were not at all associated with the clock tick, the output for enqueue would be evenly spread across the ten millisecond interval. However, the output shows a spike at the same 8 millisecond offset, indicating that at least some threads in the system are being scheduled on a time basis.